CN116299576A - Deception jamming detection method and device for integrated navigation system - Google Patents

Deception jamming detection method and device for integrated navigation system Download PDF

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CN116299576A
CN116299576A CN202310531348.2A CN202310531348A CN116299576A CN 116299576 A CN116299576 A CN 116299576A CN 202310531348 A CN202310531348 A CN 202310531348A CN 116299576 A CN116299576 A CN 116299576A
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detection
state
variance
fraud
deception
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CN116299576B (en
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王怡晨
刘小汇
文超
刘瀛翔
李宗楠
徐子晨
嵇志敏
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National University of Defense Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
    • G01S19/215—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service issues related to spoofing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention provides a deception jamming detection method and device for an integrated navigation system, and belongs to the technical field of satellite navigation. The method comprises the following steps: constructing zero-speed detection statistics; constructing a filtering model; constructing static state spoofing detection statistics; the motion state spoof detection statistic is constructed. According to the invention, the deception rate of jitter is modeled as a random variable obeying non-zero mean Gaussian distribution, and detection statistics of the variance of the innovation sequence is constructed by comparing the statistical reference noise variance in a static deception-free state and the variance of the innovation sequence in a motion state, so that deception interference detection is carried out, the deception rate is insensitive to the mean value of deception rate, and the deception system has good detection performance for slow-change deception interference.

Description

Deception jamming detection method and device for integrated navigation system
Technical Field
The invention belongs to the technical field of satellite navigation, and particularly relates to a deception jamming detection method and device of a combined navigation system.
Background
GNSS spoofing interference is becoming a considerable threat to GNSS civilian users, where the generated spoofing interference is difficult to identify and eliminate by the receiver due to the time delay being dynamically adjustable as required, and is becoming a focus of research on current spoofing interference detection techniques. In the deception jamming detection technology, the technology of performing deception detection by using an external device to assist the GNSS is introduced, so that navigation information is redundant and has good detection performance, and the commonly used auxiliary device is an INS which is not interfered by radio signals, and the INS and the GNSS are fused in a Kalman filtering mode and the like and combined with a detection theory to realize detection of deception jamming. However, the conventional theory of performing fraud detection based on GNSS/INS combination considers that the rate of the fraud signal is an unknown constant, and in practical situations, due to the influence of environmental noise, various errors, etc., the rate of applying the fraud signal to the target receiver may have jitter, which is unreasonable as a constant.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a scheme for detecting GNSS receiver deception jamming by using measurement signals under the mode of a GNSS (Global Navigation Satellite System, global satellite navigation system) and INS (Inertial Navigation System ) combined navigation system, which is particularly suitable for discontinuous slow variation deception jamming detection.
According to the invention, the deception rate with jitter is modeled as a random variable obeying non-zero mean Gaussian distribution, zero-speed detection is firstly carried out according to acceleration and angular velocity measurement values output by an INS, and when the carrier is detected to be stationary, whether the GNSS signal is deception is easy to judge at the moment, and whether the user speed calculated by a GNSS receiver is changed can be judged. If the deception direct alarm is detected in the static state, collecting the innovation at the moment and counting the variance when the deception is not detected in the static state. Since the variance of the innovation when not deceptively included only INS propagation errors and observed noise, the innovation at this time can be regarded as reference noise, and the variance of the statistical reference noise is used as prior information in the deceptive-free state. When the carrier moves, counting the variance of the innovation in a period of time and comparing the variance with the prior reference noise variance, treating the deception rate as a random variable can lead to the increase of the innovation variance when deception exists, and deception can be detected by comparing the counted innovation sequence variance in a moving state with the reference noise variance in a static state. If fraud is detected, eliminating the fraud signal from the integrated navigation system; if no fraud is detected, correcting inertial navigation by using the GNSS signals, and then continuing to execute the flow of the next cycle.
The first aspect of the invention discloses a spoofing interference detection method of an integrated navigation system. The integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, wherein the inertial navigation system INS comprises a measurement inertial unit IMU; the method comprises the following steps: s1, acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not; s2, constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model; s3, responding to the carrier in the static state, constructing fraud detection statistics in the static state according to the current system state, wherein the fraud detection statistics in the static state are used for determining whether the carrier is interfered by fraud in the static state, and responding to the carrier not being interfered by the fraud in the static state, further determining reference noise; s4, calculating the variance of the reference noise, and constructing fraud interference statistics in a motion state by using the variance of the reference noise, wherein the fraud interference statistics in the motion state are used for judging whether the carrier is subjected to fraud interference in the motion state; wherein the carrier is a vehicle.
According to the method of the first aspect of the invention, in said step S1:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure SMS_1
,
wherein,,
Figure SMS_9
、/>
Figure SMS_7
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure SMS_11
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure SMS_8
And angular velocity->
Figure SMS_15
N is the number in the detection window +.>
Figure SMS_10
Stress constant->
Figure SMS_16
For the average acceleration value within the length of the detection window, < >>
Figure SMS_4
For the average acceleration +.>
Figure SMS_13
Amplitude of>
Figure SMS_2
Representing matrix transpose->
Figure SMS_12
In order to detect the length of the window,
Figure SMS_6
，/>
Figure SMS_14
representation->
Figure SMS_5
Is a binary norm operation of->
Figure SMS_17
Representation->
Figure SMS_3
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure SMS_18
Less than a set threshold->
Figure SMS_19
When the carrier is determined to be in the stationary state.
According to the method of the first aspect of the invention, in said step S2:
the integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure SMS_20
,
Wherein,,
Figure SMS_22
representing a position error vector, consisting of longitude, latitude and altitude;
Figure SMS_26
representing an earth reference velocity error vector; />
Figure SMS_28
Is an attitude error vector; />
Figure SMS_23
The zero offset error vector is a gyroscope; />
Figure SMS_25
The zero offset error vector of the accelerometer; />
Figure SMS_27
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure SMS_29
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure SMS_21
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure SMS_24
The expression is as follows:
Figure SMS_30
,
wherein,,
Figure SMS_31
、/>
Figure SMS_32
the observed pseudo-range and the observed pseudo-range rate of the ith satellite at the k moment respectively,
Figure SMS_33
m is the number of observation satellites; the system state equation and measurement equation are:
Figure SMS_34
,
wherein,,
Figure SMS_37
and->
Figure SMS_39
State vectors representing the times k and k-1, respectively,/->
Figure SMS_42
State noise vector representing time k-1, < ->
Figure SMS_35
The state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure SMS_40
Representation ofkTime measurement matrix->
Figure SMS_43
Is thatkObserving noise vector at moment, < >>
Figure SMS_45
Is thatkTime state noise vector, ">
Figure SMS_36
And->
Figure SMS_38
All assuming zero mean Gaussian white noise, covariance was calculated by +. >
Figure SMS_41
And->
Figure SMS_44
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure SMS_46
And state prediction vector +.>
Figure SMS_47
Construction of an innovation vector->
Figure SMS_48
The method comprises the following steps: />
Figure SMS_49
。
According to the method of the first aspect of the invention, in said step S3:
when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure SMS_50
the speed measurement of the carrier, which is calculated by the global navigation satellite system GNSS receiver at this moment k, is +.>
Figure SMS_51
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized likelihood ratio formula by using the velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistic in the static state to obtain a deception jamming detection formula in the static state:
Figure SMS_52
,
wherein,,
Figure SMS_53
for the N-th detection statistics set, N is the number of data in the detection window, and +.>
Figure SMS_54
For detecting window length, +.>
Figure SMS_55
For GNSS observation variance>
Figure SMS_56
Representing->
Figure SMS_57
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure SMS_58
Less than a set threshold->
Figure SMS_59
When the GNSS receiver is not deceptively interfered, obtaining the innovation sequence of the deceptively interfered period >
Figure SMS_60
As the reference noise, for detecting the deception jamming in the motion state, the average value of the innovation sequences in the period of non-deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure SMS_61
,
wherein,,
Figure SMS_62
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure SMS_63
For a window length that is not disturbed by fraud, +.>
Figure SMS_64
Is the innovation of the j satellite at the moment k.
According to the method of the first aspect of the invention, in said step S4:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure SMS_65
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; order the
Figure SMS_66
，/>
Figure SMS_67
Is in a deceptive state, is compromised>
Figure SMS_68
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model: / >
Figure SMS_69
,
Wherein the length of the detection sequence is M,
Figure SMS_70
is the known reference noise variance->
Figure SMS_71
Zero mean gaussian noise>
Figure SMS_72
For variance introduced by fraud signals>
Figure SMS_73
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is:
Figure SMS_74
,
Figure SMS_75
,
estimation using MLE method
Figure SMS_76
The method comprises the following steps of:
Figure SMS_77
,
for a pair of
Figure SMS_78
And (3) deriving to obtain:
Figure SMS_79
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure SMS_80
,
if it is
Figure SMS_81
The corresponding MLE is +.>
Figure SMS_82
Keeping with the parameter constraint, then +.>
Figure SMS_83
MLE of (2) is
Figure SMS_84
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure SMS_85
Wherein the superscript + means +_>
Figure SMS_86
Positive, it is MLE;
when (when)
Figure SMS_87
When it is judged->
Figure SMS_88
Estimate +.>
Figure SMS_89
Substituting log-likelihood ratio can obtain:
Figure SMS_90
,
order the
Figure SMS_92
Figure SMS_95
Is a monotonically increasing function for->
Figure SMS_97
Reverse->
Figure SMS_93
If there is
Figure SMS_94
Or->
Figure SMS_96
Judging as->
Figure SMS_98
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure SMS_91
；
At the position of
Figure SMS_99
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure SMS_100
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution: />
Figure SMS_101
,
Wherein,,
Figure SMS_102
,/>
Figure SMS_103
，/>
Figure SMS_104
then->
Figure SMS_105
The probability density function of (2) is: />
Figure SMS_106
According to
Figure SMS_107
Is used for calculating a threshold and a threshold +.>
Figure SMS_108
The method comprises the following steps: />
Figure SMS_109
,
Wherein,,
Figure SMS_110
is an inverse Gaussian right tail probability function, +.>
Figure SMS_111
For false alarm probability, when statistics +>
Figure SMS_112
Greater than a set threshold->
Figure SMS_113
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
The second aspect of the invention discloses a spoofing interference detection device of an integrated navigation system. The integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, wherein the inertial navigation system INS comprises a measurement inertial unit IMU; the device comprises: a first processing unit configured to: acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not; a second processing unit configured to: constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model; a third processing unit configured to: constructing fraud detection statistics in the quiescent state from the current system state in response to the carrier being in the quiescent state, the fraud detection statistics in the quiescent state being used to determine whether the carrier is under fraud interference in the quiescent state, and further determining reference noise in response to the carrier not being under the fraud interference in the quiescent state; a fourth processing unit configured to: calculating the variance of the reference noise, and constructing fraud interference statistics under a motion state by using the variance of the reference noise, wherein the fraud interference statistics under the motion state are used for judging whether the carrier is subjected to fraud interference under the motion state; wherein the carrier is a vehicle.
According to the apparatus of the second aspect of the present invention, the first processing unit is specifically configured to:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure SMS_114
,
wherein,,
Figure SMS_121
、/>
Figure SMS_117
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure SMS_130
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure SMS_120
And angular velocity->
Figure SMS_129
N is the number of data in the detection window, +.>
Figure SMS_122
Stress constant->
Figure SMS_126
For the average acceleration value within the length of the detection window, < >>
Figure SMS_116
For the average acceleration +.>
Figure SMS_124
Amplitude of>
Figure SMS_123
Representing matrix transpose->
Figure SMS_127
For detecting window length, +.>
Figure SMS_118
，/>
Figure SMS_128
Representation->
Figure SMS_119
Is used for the two-norm operation of (c),
Figure SMS_125
representation->
Figure SMS_115
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure SMS_131
Less than a set threshold->
Figure SMS_132
When the carrier is determined to be in the stationary state.
According to an apparatus of the second aspect of the invention, the second processing unit is specifically configured to:
The integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure SMS_133
,
wherein,,
Figure SMS_135
representing a position error vector, consisting of longitude, latitude and altitude;
Figure SMS_137
representing an earth reference velocity error vector; />
Figure SMS_140
Is an attitude error vector; />
Figure SMS_136
The zero offset error vector is a gyroscope; />
Figure SMS_138
The zero offset error vector of the accelerometer; />
Figure SMS_141
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure SMS_142
Respectively represent the east, north and heaven components in the local geographic coordinate system, the followingMark->
Figure SMS_134
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure SMS_139
The expression is as follows:
Figure SMS_143
,
wherein,,
Figure SMS_144
、/>
Figure SMS_145
the observed pseudo-range and the observed pseudo-range rate of the ith satellite at the k moment respectively,
Figure SMS_146
m is the number of observation satellites; the system state equation and measurement equation are:
Figure SMS_147
,
wherein,,
Figure SMS_149
and->
Figure SMS_153
State vectors representing the times k and k-1, respectively,/->
Figure SMS_156
State noise vector representing time k-1, < ->
Figure SMS_150
The state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure SMS_152
Representation ofkTime measurement matrix->
Figure SMS_155
Is that kObserving noise vector at moment, < >>
Figure SMS_158
Is thatkTime state noise vector, ">
Figure SMS_148
And->
Figure SMS_151
All assuming zero mean Gaussian white noise, covariance was calculated by +.>
Figure SMS_154
And->
Figure SMS_157
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure SMS_159
And state prediction vector +.>
Figure SMS_160
Construction of an innovation vector->
Figure SMS_161
The method comprises the following steps: />
Figure SMS_162
。
According to the apparatus of the second aspect of the present invention, the third processing unit is specifically configured to:
when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure SMS_163
guided by the global satellite at the time kThe speed measurement of the carrier calculated by the navigation system GNSS receiver is +.>
Figure SMS_164
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized likelihood ratio formula by using the velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistic in the static state to obtain a deception jamming detection formula in the static state:
Figure SMS_165
,
wherein,,
Figure SMS_166
for the N-th detection statistics set, N is the number of data in the detection window, and +.>
Figure SMS_167
For detecting window length, +.>
Figure SMS_168
For GNSS observation variance >
Figure SMS_169
Representing->
Figure SMS_170
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure SMS_171
Less than a set threshold->
Figure SMS_172
When the GNSS receiver is determined not to be deceptively interfered, the information sequence of the deceptively interfered period is acquired>
Figure SMS_173
As a houseThe reference noise is used for deception jamming detection in the motion state, the mean value of the innovation sequences in the period of no deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure SMS_174
,
wherein,,
Figure SMS_175
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure SMS_176
For a window length that is not disturbed by fraud, +.>
Figure SMS_177
Is the innovation of the j satellite at the moment k.
According to the apparatus of the second aspect of the present invention, the fourth processing unit is specifically configured to:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure SMS_178
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; order the
Figure SMS_179
，/>
Figure SMS_180
Is in a deceptive state, is compromised>
Figure SMS_181
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model:
Figure SMS_182
,
wherein the length of the detection sequence is M,
Figure SMS_183
is the known reference noise variance->
Figure SMS_184
Zero mean gaussian noise>
Figure SMS_185
For variance introduced by fraud signals>
Figure SMS_186
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is:
Figure SMS_187
,
Figure SMS_188
,
estimation using MLE method
Figure SMS_189
The method comprises the following steps of:
Figure SMS_190
,
for a pair of
Figure SMS_191
And (3) deriving to obtain:
Figure SMS_192
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure SMS_193
if it is
Figure SMS_194
The corresponding MLE is +.>
Figure SMS_195
Keeping with the parameter constraint, then +.>
Figure SMS_196
MLE of (2) is
Figure SMS_197
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure SMS_198
Wherein the superscript + means +_>
Figure SMS_199
Positive, it is MLE;
when (when)
Figure SMS_200
When it is judged->
Figure SMS_201
Estimate +.>
Figure SMS_202
Substituting log-likelihood ratio can obtain:
Figure SMS_203
,
order the
Figure SMS_205
，/>
Figure SMS_207
Is a monotonically increasing function for->
Figure SMS_209
Reverse->
Figure SMS_206
If there is
Figure SMS_208
Or->
Figure SMS_210
Judging as->
Figure SMS_211
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure SMS_204
；
At the position of
Figure SMS_212
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure SMS_213
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution:
Figure SMS_214
,
wherein,,
Figure SMS_215
,/>
Figure SMS_216
，/>
Figure SMS_217
then->
Figure SMS_218
The probability density function of (2) is:
Figure SMS_219
,
according to
Figure SMS_220
Is used for calculating a threshold and a threshold +.>
Figure SMS_221
The method comprises the following steps:
Figure SMS_222
,
Wherein,,
Figure SMS_223
is an inverse Gaussian right tail probability function, +.>
Figure SMS_224
For false alarm probability, when statistics +>
Figure SMS_225
Greater than a set threshold->
Figure SMS_226
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps in the fraud interference detection method of the integrated navigation system according to the first aspect of the disclosure.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a fraud detection method of an integrated navigation system according to the first aspect of the present disclosure.
In summary, the technical scheme provided by the invention models the deception rate of jitter as a random variable obeying non-zero mean Gaussian distribution, and constructs detection statistics of the variance of the innovation sequence by comparing the statistical reference noise variance in a static deception-free state with the variance of the innovation sequence in a motion state, so as to detect deception. The designed detection scheme mainly detects variance variation, so that the detection scheme is insensitive to deception rate average value and has good detection performance on deception interference with slow detection rate. The obvious technical effects brought by the invention are mainly as follows: (1) Compared with the traditional SPRT (Sequential Probability Ratio Test ) method for deception detection by utilizing the variation of the mean value of the information, the method for deception detection by using the variance of the information sequence has higher sensitivity to the deception detection by using the slowly varying type, can timely detect the generation and disappearance of deception, is particularly suitable for discontinuous slowly varying type deception detection, and can improve the utilization rate of GNSS signals. (2) When multiple satellites are deceptively detected, the method can better identify the deceptively detected satellites, and detection statistics corresponding to the satellite signals which are not deceptively detected are not affected by the deceptively detected satellite signals. And the deception detection rate is higher when the deception rate variance is smaller, is less influenced by deception rate mean change, and has better detection rate performance on deception interference with slow change of the detection rate.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for detecting fraud in an integrated navigation system according to an embodiment of the present invention;
FIGS. 2a-2b are schematic diagrams of the present invention in comparison to conventional SPRT algorithms for detection sensitivity of 0.2m/s mean, 0.1 (m/s) 2 fraud rate variance;
FIGS. 3a-3b are schematic diagrams of the present invention in comparison to conventional SPRT algorithms for detection sensitivity of 0.4m/s mean, 0.1 (m/s) 2 fraud rate variance;
FIGS. 4a-4b are graphs showing the comparison of the detection sensitivity of the present invention to a conventional SPRT algorithm for a 0.4m/s mean, 0.05 (m/s) 2 fraud rate variance;
FIG. 5 is a graph of the results of spoofing detection on multiple stars in accordance with an embodiment of the present invention;
FIG. 6 is a graph of fraud detection rate versus fraud rate variance and mean in accordance with an embodiment of the present invention;
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The first aspect of the invention discloses a spoofing interference detection method of an integrated navigation system. The integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, wherein the inertial navigation system INS comprises a measurement inertial unit IMU; the method comprises the following steps: s1, acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not; s2, constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model; s3, responding to the carrier in the static state, constructing fraud detection statistics in the static state according to the current system state, wherein the fraud detection statistics in the static state are used for determining whether the carrier is interfered by fraud in the static state, and responding to the carrier not being interfered by the fraud in the static state, further determining reference noise; s4, calculating the variance of the reference noise, and constructing fraud interference statistics in a motion state by using the variance of the reference noise, wherein the fraud interference statistics in the motion state are used for judging whether the carrier is subjected to fraud interference in the motion state; wherein the carrier is a vehicle.
In some embodiments, in said step S1:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure SMS_227
,
wherein,,
Figure SMS_235
、/>
Figure SMS_229
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure SMS_237
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure SMS_230
And angular velocity->
Figure SMS_241
N is the number of data in the detection window, +.>
Figure SMS_232
Stress constant->
Figure SMS_238
For the average acceleration value within the length of the detection window, < >>
Figure SMS_236
For the average acceleration +.>
Figure SMS_240
Amplitude of>
Figure SMS_233
Representing matrix transpose->
Figure SMS_243
For detecting window length, +.>
Figure SMS_231
，/>
Figure SMS_239
Representation->
Figure SMS_234
Is used for the two-norm operation of (c),
Figure SMS_242
representation->
Figure SMS_228
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure SMS_244
Less than a set threshold->
Figure SMS_245
When the carrier is determined to be in the stationary state.
In some embodiments, in said step S2:
the integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure SMS_246
,
Wherein,,
Figure SMS_248
representing a position error vector, consisting of longitude, latitude and altitude;
Figure SMS_251
representing an earth reference velocity error vector; />
Figure SMS_253
Is an attitude error vector; />
Figure SMS_249
The zero offset error vector is a gyroscope; />
Figure SMS_252
The zero offset error vector of the accelerometer; />
Figure SMS_254
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure SMS_255
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure SMS_247
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure SMS_250
The expression is as follows:
Figure SMS_256
,
wherein,,
Figure SMS_257
、/>
Figure SMS_258
observation of the ith satellite at time kThe pseudoranges and the observed pseudorange rates,
Figure SMS_259
m is the number of observation satellites; the system state equation and measurement equation are:
Figure SMS_260
,
wherein,,
Figure SMS_263
and->
Figure SMS_266
State vectors representing the times k and k-1, respectively,/->
Figure SMS_269
State noise vector representing time k-1, < ->
Figure SMS_262
The state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure SMS_265
Representation ofkTime measurement matrix->
Figure SMS_268
Is thatkObserving noise vector at moment, < >>
Figure SMS_271
Is thatkTime state noise vector, ">
Figure SMS_261
And->
Figure SMS_264
All assuming zero mean Gaussian white noise, covariance was calculated by +. >
Figure SMS_267
And->
Figure SMS_270
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure SMS_272
And state prediction vector +.>
Figure SMS_273
Construction of an innovation vector->
Figure SMS_274
The method comprises the following steps: />
Figure SMS_275
。
In some embodiments, in said step S3:
when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure SMS_276
the speed measurement of the carrier, which is calculated by the global navigation satellite system GNSS receiver at this moment k, is +.>
Figure SMS_277
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized likelihood ratio formula by using the velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistic in the static state to obtain a deception jamming detection formula in the static state:
Figure SMS_278
,
wherein,,
Figure SMS_279
detection statistics set for nth detectionN is the number of data in the detection window, +.>
Figure SMS_280
For detecting window length, +.>
Figure SMS_281
For GNSS observation variance>
Figure SMS_282
Representing->
Figure SMS_283
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure SMS_284
Less than a set threshold->
Figure SMS_285
When the GNSS receiver is determined not to be deceptively interfered, the information sequence of the deceptively interfered period is acquired >
Figure SMS_286
As the reference noise, for detecting the deception jamming in the motion state, the average value of the innovation sequences in the period of non-deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows: />
Figure SMS_287
,
Wherein,,
Figure SMS_288
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure SMS_289
For a window length that is not disturbed by fraud, +.>
Figure SMS_290
For the j-th guard at k momentInnovation of the star.
In some embodiments, in said step S4:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure SMS_291
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; order the
Figure SMS_292
，/>
Figure SMS_293
Is in a deceptive state, is compromised>
Figure SMS_294
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model:
Figure SMS_295
,
Wherein the length of the detection sequence is M,
Figure SMS_296
is the known reference noise variance->
Figure SMS_297
Zero mean gaussian noise>
Figure SMS_298
For variance introduced by fraud signals>
Figure SMS_299
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is:
Figure SMS_300
Figure SMS_301
,
estimation using MLE method
Figure SMS_302
The method comprises the following steps of:
Figure SMS_303
,
for a pair of
Figure SMS_304
And (3) deriving to obtain:
Figure SMS_305
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure SMS_306
,
if it is
Figure SMS_307
The corresponding MLE is +.>
Figure SMS_308
Keeping with the parameter constraint, then +.>
Figure SMS_309
MLE of (2) is
Figure SMS_310
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure SMS_311
Wherein the superscript + means +_>
Figure SMS_312
Positive, it is MLE;
when (when)
Figure SMS_313
When it is judged->
Figure SMS_314
Estimate +.>
Figure SMS_315
Substituting log-likelihood ratio can obtain:
Figure SMS_316
,
order the
Figure SMS_318
，/>
Figure SMS_321
Is a monotonically increasing function for->
Figure SMS_323
Reverse->
Figure SMS_319
If there is
Figure SMS_320
Or->
Figure SMS_322
Judging as->
Figure SMS_324
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure SMS_317
；
At the position of
Figure SMS_325
Under the condition of innovationThe sequence satisfies zero mean variance +.>
Figure SMS_326
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution:
Figure SMS_327
,
wherein,,
Figure SMS_328
,/>
Figure SMS_329
，/>
Figure SMS_330
then->
Figure SMS_331
The probability density function of (2) is:
Figure SMS_332
according to
Figure SMS_333
Is used for calculating a threshold and a threshold +.>
Figure SMS_334
The method comprises the following steps:
Figure SMS_335
wherein,,
Figure SMS_336
is an inverse Gaussian right tail probability function, +.>
Figure SMS_337
For false alarm probability, when statistics +>
Figure SMS_338
Greater than a set threshold->
Figure SMS_339
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
The method is described in detail in connection with fig. 1 (a flow chart of a fraud detection method of a combined navigation system comprising a global satellite navigation system GNSS and an inertial navigation system INS).
First, constructing zero-speed detection statistics:
as a more preferable embodiment, the step utilizes the three-dimensional acceleration and angular velocity measurement value output by the IMU as detection statistics to construct a generalized likelihood ratio formula, and the following zero-velocity detection formula is obtained:
Figure SMS_340
,
wherein,,
Figure SMS_342
、/>
Figure SMS_348
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure SMS_353
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure SMS_347
And angular velocity->
Figure SMS_351
N is the number of data in the detection window, +.>
Figure SMS_344
Stress constant->
Figure SMS_354
For the average acceleration value within the length of the detection window, < >>
Figure SMS_346
For the average acceleration +.>
Figure SMS_352
Amplitude of>
Figure SMS_349
Representing matrix transpose->
Figure SMS_356
For detecting window length, +.>
Figure SMS_345
，/>
Figure SMS_355
Representation->
Figure SMS_343
Is used for the two-norm operation of (c),
Figure SMS_350
representation->
Figure SMS_341
Is calculated by a binary norm of (2);
when the detection statistics are
Figure SMS_357
Less than a set threshold- >
Figure SMS_358
The carrier is considered to be in a stationary state.
Second, constructing a filtering model:
the filtering model adopts common Kalman filtering: the Kalman filtering model comprises a state equation and a measurement equation; the state equation is composed of an INS error propagation equation and a GNSS receiver clock error modelObtaining and adopting discrete first-order approximation for a continuous process; the measurement equation is derived from a GNSS observation model. At any one measurement time i, the state quantity of the Kalman filtering system is as follows
Figure SMS_359
The estimated value of the state quantity at the moment is obtained by weighting the predicted value and the observed value through Kalman gain.
As a more preferred embodiment, the specific steps include:
the state vector at the kth measurement time is:
Figure SMS_360
,
wherein,,
Figure SMS_362
representing a position error vector, consisting of longitude, latitude and altitude;
Figure SMS_364
representing an earth reference velocity error vector; />
Figure SMS_366
Is an attitude error vector; />
Figure SMS_363
The zero offset error vector is a gyroscope; />
Figure SMS_365
The zero offset error vector of the accelerometer; />
Figure SMS_367
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure SMS_368
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure SMS_361
Respectively show the carrier seatRight, front, upper components in the frame;
measurement vector at kth measurement time
Figure SMS_369
The expression is as follows:
Figure SMS_370
,
wherein,,
Figure SMS_371
、/>
Figure SMS_372
（/>
Figure SMS_373
m is the number of observed satellites) are the observed pseudorange and the observed pseudorange rate of the ith satellite at time k, respectively. />
The state equation and measurement equation of the system can be written as:
Figure SMS_374
,
wherein,,
Figure SMS_376
and->
Figure SMS_379
State vectors representing the times k and k-1, respectively,/->
Figure SMS_382
State noise vector representing time k-1, < ->
Figure SMS_377
The state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure SMS_378
Representation ofkTime measurement matrix->
Figure SMS_381
Is thatkObserving noise vector at moment, < >>
Figure SMS_384
Is thatkTime state noise vector, ">
Figure SMS_375
And->
Figure SMS_380
All assuming zero mean Gaussian white noise, covariance was calculated by +.>
Figure SMS_383
And->
Figure SMS_385
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure SMS_386
And state prediction vector +.>
Figure SMS_387
Can construct the innovation vector->
Figure SMS_388
The method comprises the following steps:
Figure SMS_389
,
thirdly, constructing static state spoofing detection statistics:
when the carrier is detected to be stationary, i.e. the speed in three directions in the body coordinate system b of the carrier is zero, i.e
Figure SMS_390
And the carrier velocity measurement at time k, which is calculated by the GNSS receiver, is +.>
Figure SMS_391
At this time, GNSS receiver deception jamming detection can be performed, and a generalized likelihood ratio formula is constructed by using the carrier speed output after the receiver is resolved as detection statistics, so as to obtain the following stationary state deception jamming detection formula:
Figure SMS_392
,
Wherein,,
Figure SMS_393
for the N-th detection statistics set, N is the number of data in the detection window, and +.>
Figure SMS_394
For detecting window length, +.>
Figure SMS_395
For GNSS observation variance>
Figure SMS_396
Representing->
Figure SMS_397
Is calculated by a binary norm of (2);
when the detection statistics are
Figure SMS_398
Less than a set threshold->
Figure SMS_399
When the GNSS receiver is considered not to be deceptively interfered, the new information sequence of the period of no deceptively interfered is acquired>
Figure SMS_400
As reference noise for detecting the deception jamming of the motion state, the mean value of the innovation under the condition of no deception is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure SMS_401
,
wherein,,
Figure SMS_402
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure SMS_403
For a window length that is not disturbed by fraud, +.>
Figure SMS_404
Is the innovation of the j satellite at the moment k.
Fourth, constructing motion state spoofing detection statistics:
after the reference noise variance is obtained in the static state, when the carrier is in the motion state, the actual deception jamming rate cannot be kept constant, the deception rate is regarded as a random variable meeting non-zero mean Gaussian distribution, and the pseudo range rate innovation of the j-th visible satellite at the k moment in the motion state is set as
Figure SMS_405
The method comprises the steps of carrying out a first treatment on the surface of the When the GNSS receiver is not deceptively interfered, the pseudo-range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the GNSS receiver is deceptively interfered, the innovation variance is larger than the reference noise variance due to jitter of deception rate; let- >
Figure SMS_406
，/>
Figure SMS_407
Is in a deceptive state, is compromised>
Figure SMS_408
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model:
Figure SMS_409
,
where M is the length of the detection sequence,
Figure SMS_410
is the known reference noise variance->
Figure SMS_411
Zero mean gaussian noise>
Figure SMS_412
For variance introduced by fraud signals>
Figure SMS_413
Unknown zero-mean gaussian distribution signals;
the generalized likelihood ratio is then:
Figure SMS_414
（1）
Figure SMS_415
（2）
estimation using MLE method
Figure SMS_416
The logarithm of the formula (2) can be obtained:
Figure SMS_417
to the above pair
Figure SMS_418
And (3) deriving:
Figure SMS_419
the MLE estimate of the variance of the spoof signal, which makes the derivative 0, is:
Figure SMS_420
but if it
Figure SMS_421
The corresponding MLE should be +.>
Figure SMS_422
This is consistent with the constraint of the parameters, therefore, < ->
Figure SMS_423
MLE of (c) is:
Figure SMS_424
order the
Figure SMS_425
Wherein the superscript "+" indicates if +.>
Figure SMS_426
Is positive, then it is MLE;
when (when)
Figure SMS_427
Then judge->
Figure SMS_428
Estimate +.>
Figure SMS_429
Substituting log-likelihood ratio can obtain: />
Figure SMS_430
,
Order the
Figure SMS_433
，/>
Figure SMS_434
Is a monotonically increasing function for->
Figure SMS_436
Reverse->
Figure SMS_432
If there is
Figure SMS_435
Or->
Figure SMS_437
Judging as->
Figure SMS_438
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure SMS_431
；
At the position of
Figure SMS_439
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure SMS_440
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution:
Figure SMS_441
,
wherein,,
Figure SMS_442
,/>
Figure SMS_443
，/>
Figure SMS_444
then->
Figure SMS_445
The probability density function of (2) is:
Figure SMS_446
,
in practical application, the false alarm probability is very small, so that the false alarm probability is very low
Figure SMS_447
Is to be +.>
Figure SMS_448
Completely regarding that the Gaussian distribution has no influence on the calculation of the threshold; last threshold->
Figure SMS_449
The calculation can be as follows:
Figure SMS_450
,
wherein the method comprises the steps of
Figure SMS_451
Is an inverse Gaussian right tail probability function, +.>
Figure SMS_452
Is a false alarm probability when the statistic +.>
Figure SMS_453
Greater than a set threshold->
Figure SMS_454
The carrier GNSS receiver is considered to be deceptively disturbed.
Aiming at the deception jamming method designed by the invention, three groups of simulation experiments are carried out for verification, and the three groups of simulation experiments are respectively as follows:
experiment 1: the average value of the received signal of a single satellite is 0.2
Figure SMS_455
Variance 0.1%>
Figure SMS_456
The method comprises the steps of carrying out a first treatment on the surface of the Mean 0.4->
Figure SMS_457
Variance 0.1
Figure SMS_458
The method comprises the steps of carrying out a first treatment on the surface of the Mean 0.4->
Figure SMS_459
Variance 0.05%>
Figure SMS_460
And comparing the detection algorithm of the innovation sequence SPRT with the detection time of the invention during the slow change type deception of the deception rate, and returning to the reset time required under the deception-free state by the two methods after deception interference disappears, thereby verifying the detection sensitivity of the invention to the slow change type deception interference. The impact of the spoofing rate mean and variance settings on both algorithms is observed simultaneously.
Experiment 2: the average value of the received signals of a plurality of satellites is 0.4
Figure SMS_461
Variance 0.1%>
Figure SMS_462
When the deception rate is deception, the detection performance of the invention on multi-star deception is verified.
Experiment 3: the mean value and variance of the deception rate are changed, and the change relation of the deception detection rate with the mean value and variance of the deception rate is verified.
The experimental relevant parameter settings are shown in table 1.
TABLE 1
Figure SMS_463
In the experiment, the shielding angle is set as
Figure SMS_464
Keeping the number of visible satellites at 8, in experiments 1 and 3, PRN01 satellite was deceptively applied, and in experiment 2 PRN01 and PRN04 satellite were deceptively applied simultaneously.
The settings of the amount of slow-varying spoofing in experiments 1 and 2 are shown in table 2.
TABLE 2
Figure SMS_465
The pseudorange rate reference noise variance statistics for 8 satellites in view are shown in table 3.
TABLE 3 Table 3
Figure SMS_466
Applying an average of 0.2 to satellite PRN01 beginning at 50s
Figure SMS_468
Variance 0.1%>
Figure SMS_472
The method comprises the steps of carrying out a first treatment on the surface of the Mean 0.4->
Figure SMS_475
Variance 0.1%>
Figure SMS_470
The method comprises the steps of carrying out a first treatment on the surface of the Mean 0.4->
Figure SMS_473
Variance 0.05%>
Figure SMS_476
The time dependence of the SPRT algorithm and the fraud test statistic of the present invention is shown in FIGS. 2a-2b, 3a-3b and 4a-4 b. From the three groups of graphs, the sensitivity of the detection method to the deception jamming is high, and when the deception jamming disappears after 150s, the detection statistics of the detection method are respectively lower than the detection threshold value within 16s, 16s and 15 s. For the new sequence SPRT algorithm, after the deception jamming disappears, the detection result is still deception, and the state is continued until the detection is finished. Furthermore, the SPRT algorithm was applied to a mean value of 0.2m/s, 0.1 +.>
Figure SMS_478
The detection time of the variance spoofing rate is longest and is 27s; for a mean value of 0.4m/s, 0.1 +. >
Figure SMS_467
And 0.05->
Figure SMS_471
The detection time of the variance spoofing rate is the same, 10s. The average value of the spoofing rate has a certain influence on the detection rate of the SPRT algorithm. The invention is applicable to the average value of 0.2m/s and the ratio of 0.1->
Figure SMS_474
Variance and 0.4m/s mean, 0.1 +.>
Figure SMS_477
The detection time of the variance spoofing rate is 7s and is 20s faster than that of the SPRT algorithm; for a mean value of 0.4m/s, 0.05 +.>
Figure SMS_469
The detection time of the variance spoofing rate is 11s, which is 1s slower than the SPRT algorithm. The invention is mainly influenced by the variance of the deception rate, and in the actual situation, the variance of the deception rate cannot be controlled, and the larger the deception rate is, the larger the jitter generated by the deception rate is, so that the invention has obvious advantages in the detection speed under the real deception jamming environment.
Applying an average of 0.4 to satellites PRN01 and PRN04 at 50s simultaneously
Figure SMS_479
Variance 0.1%>
Figure SMS_480
The detection result of the invention is shown in figure 5. As can be seen from the figure, when spoofing is applied to a plurality of satellites, the method can better detect which satellites are spoofed, and the detection statistics corresponding to the satellite signals which are not spoofed are always at a low value and are not affected by the spoofed satellite signals.
The satellite PRN01 was applied at 50s with a mean of 0.2m/s and 0.4m/s, respectively, with a variance ranging from 0-0.15
Figure SMS_481
The deception jamming of deception rate, simulate and analyze the detection rate of the proposed algorithm, the calculation formula of the detection rate P is: />
Figure SMS_482
,
Wherein the method comprises the steps of
Figure SMS_483
The data frequency of the GNSS receiver; />
Figure SMS_484
The number of experiments to successfully detect the presence of spoofing; />
Figure SMS_485
For duration of the spoofing disturbance. The deception rate means were chosen to be 0.2m/s and 0.4m/s, respectively, and the effect of deception rate variance on detection rate was analyzed as shown in FIG. 6. It can be seen from the graph that the fraud detection rate of the present invention increases gradually as the fraud rate variance increases. For false alarm probability of 10-4, the variance of deception jamming is less than 0.09 +.>
Figure SMS_486
When the detection probability is less than 93.07%; when the variance is 0.14 (/ so)>
Figure SMS_487
The detection probability was 100% in the above case. The detection rate of the method is not influenced by the average value of the deception rate, and particularly has good detection performance on deception interference with slow change of the detection rate.
The second aspect of the invention discloses a spoofing interference detection device of an integrated navigation system. The integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, wherein the inertial navigation system INS comprises a measurement inertial unit IMU; the device comprises: a first processing unit configured to: acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not; a second processing unit configured to: constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model; a third processing unit configured to: constructing fraud detection statistics in the quiescent state from the current system state in response to the carrier being in the quiescent state, the fraud detection statistics in the quiescent state being used to determine whether the carrier is under fraud interference in the quiescent state, and further determining reference noise in response to the carrier not being under the fraud interference in the quiescent state; a fourth processing unit configured to: calculating the variance of the reference noise, and constructing fraud interference statistics under a motion state by using the variance of the reference noise, wherein the fraud interference statistics under the motion state are used for judging whether the carrier is subjected to fraud interference under the motion state; wherein the carrier is a vehicle.
In some embodiments, the first processing unit is specifically configured to:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure SMS_488
,
wherein,,
Figure SMS_489
、/>
Figure SMS_490
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure SMS_502
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure SMS_493
And angular velocity->
Figure SMS_500
N is the number of data in the detection window, +.>
Figure SMS_496
Stress constant->
Figure SMS_503
For the average acceleration value within the length of the detection window, < >>
Figure SMS_495
For the average acceleration +.>
Figure SMS_499
Amplitude of>
Figure SMS_497
Representing matrix transpose->
Figure SMS_498
For detecting window length, +.>
Figure SMS_492
，/>
Figure SMS_504
Representation->
Figure SMS_494
Is used for the two-norm operation of (c),
Figure SMS_501
representation->
Figure SMS_491
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure SMS_505
Less than a set threshold->
Figure SMS_506
When the carrier is determined to be in the stationary state.
In some embodiments, the second processing unit is specifically configured to:
the integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure SMS_507
,
Wherein,,
Figure SMS_509
representing a position error vector, consisting of longitude, latitude and altitude;
Figure SMS_513
representing an earth reference velocity error vector; />
Figure SMS_515
Is an attitude error vector; />
Figure SMS_510
The zero offset error vector is a gyroscope; />
Figure SMS_511
The zero offset error vector of the accelerometer; />
Figure SMS_514
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure SMS_516
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure SMS_508
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure SMS_512
The expression is as follows:
Figure SMS_517
,
Figure SMS_518
,/>
wherein,,
Figure SMS_521
and->
Figure SMS_524
State vectors representing the times k and k-1, respectively,/->
Figure SMS_527
State noise vector representing time k-1, < ->
Figure SMS_520
The state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure SMS_523
Representation ofkTime measurement matrix->
Figure SMS_526
Is thatkObserving noise vector at moment, < >>
Figure SMS_529
Is thatkTime state noise vector, ">
Figure SMS_519
And->
Figure SMS_522
All assuming zero mean Gaussian white noise, covariance was calculated by +.>
Figure SMS_525
And->
Figure SMS_528
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure SMS_530
And state prediction vector +.>
Figure SMS_531
Construction of an innovation vector- >
Figure SMS_532
The method comprises the following steps: />
Figure SMS_533
。
In some embodiments, the third processing unit is specifically configured to:
when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure SMS_534
the speed measurement of the carrier, which is calculated by the global navigation satellite system GNSS receiver at this moment k, is +.>
Figure SMS_535
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized likelihood ratio formula by using the velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistic in the static state to obtain a deception jamming detection formula in the static state:
Figure SMS_536
,
wherein,,
Figure SMS_537
for the N-th detection statistics set, N is the number of data in the detection window, and +.>
Figure SMS_538
For detecting window length, +.>
Figure SMS_539
For GNSS observation variance>
Figure SMS_540
Representing->
Figure SMS_541
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure SMS_542
Less than a set threshold->
Figure SMS_543
When the GNSS receiver is determined not to be deceptively interfered, the information sequence of the deceptively interfered period is acquired>
Figure SMS_544
As the reference noise, for detecting the deception jamming in the motion state, the average value of the innovation sequences in the period of non-deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure SMS_545
,
Wherein,,
Figure SMS_546
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure SMS_547
For a window length that is not disturbed by fraud, +.>
Figure SMS_548
Is the innovation of the j satellite at the moment k.
According to the apparatus of the second aspect of the present invention, the fourth processing unit is specifically configured to:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure SMS_549
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; let->
Figure SMS_550
，/>
Figure SMS_551
Is in a deceptive state, is compromised>
Figure SMS_552
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model:
Figure SMS_553
,
wherein the length of the detection sequence is M,
Figure SMS_554
is the known reference noise variance->
Figure SMS_555
Zero mean gaussian noise>
Figure SMS_556
For variance introduced by fraud signals >
Figure SMS_557
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is:
Figure SMS_558
Figure SMS_559
,
estimation using MLE method
Figure SMS_560
The method comprises the following steps of:
Figure SMS_561
,
for a pair of
Figure SMS_562
And (3) deriving to obtain:
Figure SMS_563
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure SMS_564
,
if it is
Figure SMS_565
The corresponding MLE is +.>
Figure SMS_566
Keeping with the parameter constraint, then +.>
Figure SMS_567
MLE of (2) is
Figure SMS_568
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure SMS_569
Wherein the superscript + means +_>
Figure SMS_570
Positive, it is MLE;
when (when)
Figure SMS_571
When it is judged->
Figure SMS_572
Estimate +.>
Figure SMS_573
Substituting log-likelihood ratio can obtain:
Figure SMS_574
,/>
order the
Figure SMS_576
，/>
Figure SMS_578
Is a monotonically increasing function for->
Figure SMS_580
Reverse->
Figure SMS_577
If there is
Figure SMS_579
Or->
Figure SMS_581
Judging as->
Figure SMS_582
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure SMS_575
；
At the position of
Figure SMS_583
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure SMS_584
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution:
Figure SMS_585
,
wherein,,
Figure SMS_586
,/>
Figure SMS_587
，/>
Figure SMS_588
then->
Figure SMS_589
The probability density function of (2) is:
Figure SMS_590
,
according to
Figure SMS_591
Is used for calculating a threshold and a threshold +.>
Figure SMS_592
The method comprises the following steps:
Figure SMS_593
wherein,,
Figure SMS_594
is an inverse Gaussian right tail probability function, +.>
Figure SMS_595
For false alarm probability, when statistics +>
Figure SMS_596
Greater thanThreshold set +.>
Figure SMS_597
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps in the fraud interference detection method of the integrated navigation system according to the first aspect of the disclosure.
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 7, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a structural diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the present application solution is applied, and a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a fraud detection method of an integrated navigation system according to the first aspect of the present disclosure.
In summary, the technical scheme provided by the invention models the deception rate of jitter as a random variable obeying non-zero mean Gaussian distribution, and constructs detection statistics of the variance of the innovation sequence by comparing the statistical reference noise variance in a static deception-free state with the variance of the innovation sequence in a motion state, so as to detect deception. The designed detection scheme mainly detects variance variation, so that the detection scheme is insensitive to deception rate average value and has good detection performance on deception interference with slow detection rate. The obvious technical effects brought by the invention are mainly as follows: (1) Compared with the traditional SPRT (Sequential Probability Ratio Test ) method for deception detection by utilizing the variation of the mean value of the information, the method for deception detection by using the variance of the information sequence has higher sensitivity to the deception detection by using the slowly varying type, can timely detect the generation and disappearance of deception, is particularly suitable for discontinuous slowly varying type deception detection, and can improve the utilization rate of GNSS signals. (2) When multiple satellites are deceptively detected, the method can better identify the deceptively detected satellites, and detection statistics corresponding to the satellite signals which are not deceptively detected are not affected by the deceptively detected satellite signals. And the deception detection rate is higher when the deception rate variance is smaller, is less influenced by deception rate mean change, and has better detection rate performance on deception interference with slow change of the detection rate.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims (10)

1. The cheating interference detection method of the integrated navigation system is characterized in that the integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, and the inertial navigation system INS comprises a measurement inertial unit IMU; the method comprises the following steps:
s1, acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not;
S2, constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model;
s3, responding to the carrier in the static state, constructing fraud detection statistics in the static state according to the current system state, wherein the fraud detection statistics in the static state are used for determining whether the carrier is interfered by fraud in the static state, and responding to the carrier not being interfered by the fraud in the static state, further determining reference noise;
s4, calculating the variance of the reference noise, and constructing fraud interference statistics in a motion state by using the variance of the reference noise, wherein the fraud interference statistics in the motion state are used for judging whether the carrier is subjected to fraud interference in the motion state;
wherein the carrier is a vehicle.
2. The method for detecting fraud in an integrated navigation system according to claim 1, wherein:
in the step S1:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure QLYQS_1
,
Wherein,,
Figure QLYQS_9
、/>
Figure QLYQS_7
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure QLYQS_14
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure QLYQS_8
Sum angular velocity
Figure QLYQS_18
，/>
Figure QLYQS_10
For detecting the number of data in a window, < >>
Figure QLYQS_16
Stress constant->
Figure QLYQS_4
For the average acceleration value within the length of the detection window, < >>
Figure QLYQS_15
For the average acceleration +.>
Figure QLYQS_2
Amplitude of>
Figure QLYQS_17
Representing matrix transpose->
Figure QLYQS_5
For detecting window length, +.>
Figure QLYQS_13
，/>
Figure QLYQS_6
Representation->
Figure QLYQS_11
Is a binary norm operation of->
Figure QLYQS_3
Representation->
Figure QLYQS_12
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure QLYQS_19
Less than a set threshold->
Figure QLYQS_20
Determining that the carrier is in the stationary state;
in the step S2:
the integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure QLYQS_23
wherein, the method comprises the steps of, wherein,/>
Figure QLYQS_26
representing a position error vector, consisting of longitude, latitude and altitude; />
Figure QLYQS_29
Representing an earth reference velocity error vector; />
Figure QLYQS_22
Is an attitude error vector; />
Figure QLYQS_25
The zero offset error vector is a gyroscope; />
Figure QLYQS_27
The zero offset error vector of the accelerometer;
Figure QLYQS_30
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure QLYQS_21
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure QLYQS_24
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure QLYQS_28
The expression is as follows:
Figure QLYQS_31
wherein->
Figure QLYQS_32
、/>
Figure QLYQS_33
Respectively->
Figure QLYQS_34
Time->
Figure QLYQS_35
Observed pseudo-range and observed pseudo-range rate for a satellite, +.>
Figure QLYQS_36
M is the number of observation satellites; the system state equation and measurement equation are:
Figure QLYQS_37
，
wherein,,
Figure QLYQS_40
and->
Figure QLYQS_41
State vectors representing the times k and k-1, respectively,/->
Figure QLYQS_44
Representing the state noise vector at time k-1,
Figure QLYQS_39
the state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; />
Figure QLYQS_43
Representation ofkTime measurement matrix->
Figure QLYQS_46
Is thatkObserving noise vector at moment, < >>
Figure QLYQS_48
Is thatkTime state noise vector, ">
Figure QLYQS_38
And->
Figure QLYQS_42
All assuming zero mean Gaussian white noise, covariance was calculated by +.>
Figure QLYQS_45
And->
Figure QLYQS_47
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure QLYQS_49
And state prediction vector +.>
Figure QLYQS_50
Construction of an innovation vector->
Figure QLYQS_51
The method comprises the following steps: />
Figure QLYQS_52
。
3. The method for detecting fraud in a combined navigation system according to claim 2, wherein in said step S3: when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure QLYQS_53
The speed measurement of the carrier, which is calculated by the global navigation satellite system GNSS receiver at this moment k, is +.>
Figure QLYQS_54
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized likelihood ratio formula by using the velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistic in the static state to obtain a deception jamming detection formula in the static state:
Figure QLYQS_55
，
wherein,,
Figure QLYQS_56
for the N-th detection statistics set, N is the number of data in the detection window, and +.>
Figure QLYQS_57
For detecting window length, +.>
Figure QLYQS_58
For GNSS observation variance>
Figure QLYQS_59
Representing->
Figure QLYQS_60
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure QLYQS_61
Less than a set threshold->
Figure QLYQS_62
When the GNSS receiver is determined not to be deceptively interfered, the information sequence of the deceptively interfered period is acquired>
Figure QLYQS_63
As saidThe reference noise is used for detecting deception jamming in the motion state, the mean value of the innovation sequences in the period of no deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure QLYQS_64
,
wherein,,
Figure QLYQS_65
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +. >
Figure QLYQS_66
For a window length that is not disturbed by fraud, +.>
Figure QLYQS_67
Is the innovation of the j satellite at the moment k.
4. A method of detecting fraud in a integrated navigation system according to claim 3, characterized in that in said step S4:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure QLYQS_69
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; order the
Figure QLYQS_72
，/>
Figure QLYQS_75
Is in a deceptive state, is compromised>
Figure QLYQS_70
For deception states, then deception jamming in the motion state is detected as a binary hypothesis model: />
Figure QLYQS_71
Wherein, the length of the detection sequence is M, < + >>
Figure QLYQS_74
Is the known reference noise variance->
Figure QLYQS_77
Zero mean gaussian noise>
Figure QLYQS_68
For variance introduced by fraud signals>
Figure QLYQS_73
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is: / >
Figure QLYQS_76
,
Figure QLYQS_78
;
Estimation using MLE method
Figure QLYQS_79
The method comprises the following steps of:
Figure QLYQS_80
,
for a pair of
Figure QLYQS_81
And (3) deriving to obtain:
Figure QLYQS_82
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure QLYQS_83
,
if it is
Figure QLYQS_84
The corresponding MLE is +.>
Figure QLYQS_85
Keeping with the parameter constraint, then +.>
Figure QLYQS_86
MLE of (2) is
Figure QLYQS_87
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure QLYQS_88
Wherein the superscript + means +_>
Figure QLYQS_89
Positive, it is MLE;
when (when)
Figure QLYQS_90
When it is judged->
Figure QLYQS_91
Estimate +.>
Figure QLYQS_92
Substituting log-likelihood ratio can obtain:
Figure QLYQS_93
,
order the
Figure QLYQS_96
Figure QLYQS_97
Is a monotonically increasing function for->
Figure QLYQS_99
Reverse->
Figure QLYQS_95
If there is
Figure QLYQS_98
Or->
Figure QLYQS_100
Judging as->
Figure QLYQS_101
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure QLYQS_94
;
At the position of
Figure QLYQS_102
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure QLYQS_103
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution: />
Figure QLYQS_104
,
Wherein,,
Figure QLYQS_105
,/>
Figure QLYQS_106
，/>
Figure QLYQS_107
then->
Figure QLYQS_108
The probability density function of (2) is: />
Figure QLYQS_109
,
According to
Figure QLYQS_110
Is used for calculating a threshold and a threshold +.>
Figure QLYQS_111
The method comprises the following steps:
Figure QLYQS_112
,
wherein,,
Figure QLYQS_113
is an inverse Gaussian right tail probability function, +.>
Figure QLYQS_114
For false alarm probability, when statistics +>
Figure QLYQS_115
Greater than a set threshold->
Figure QLYQS_116
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
5. The cheating interference detection device of the integrated navigation system is characterized in that the integrated navigation system comprises a global satellite navigation system GNSS receiver and an inertial navigation system INS, and the inertial navigation system INS comprises a measurement inertial unit IMU; the device comprises:
A first processing unit configured to: acquiring three-dimensional acceleration and angular velocity measurement values output by the measuring inertial unit IMU as detection statistics, and constructing zero-speed detection statistics based on the detection statistics, wherein the zero-speed detection statistics are used for determining whether a carrier is in a static state or not;
a second processing unit configured to: constructing a filtering model, wherein the filtering model is used for estimating the current system state and constructing fraud detection statistics, and the filtering model is a Kalman filtering model;
a third processing unit configured to: constructing fraud detection statistics in the quiescent state from the current system state in response to the carrier being in the quiescent state, the fraud detection statistics in the quiescent state being used to determine whether the carrier is under fraud interference in the quiescent state, and further determining reference noise in response to the carrier not being under the fraud interference in the quiescent state;
a fourth processing unit configured to: calculating the variance of the reference noise, and constructing fraud interference statistics under a motion state by using the variance of the reference noise, wherein the fraud interference statistics under the motion state are used for judging whether the carrier is subjected to fraud interference under the motion state;
Wherein the carrier is a vehicle.
6. The integrated navigation system fraud detection apparatus of claim 5, wherein:
the first processing unit is specifically configured to:
and constructing a generalized likelihood ratio formula by using the three-dimensional acceleration and the angular velocity measurement value output by the measuring inertial unit IMU as the detection statistic to obtain a zero-speed detection formula:
Figure QLYQS_117
,
wherein,,
Figure QLYQS_125
、/>
Figure QLYQS_124
measuring variances of the three-dimensional acceleration and the angular velocity measurements output by the measuring inertial unit IMU, respectively,/>
Figure QLYQS_134
For the nth detection, the detection statistics are measurement values of the inertial measurement unit IMU at all times in a detection time period, and specifically include triaxial acceleration +.>
Figure QLYQS_126
And angular velocity->
Figure QLYQS_133
，/>
Figure QLYQS_121
For detecting the number of data in a window, < >>
Figure QLYQS_130
Stress constant->
Figure QLYQS_119
For the average acceleration value within the length of the detection window, < >>
Figure QLYQS_129
For the average acceleration +.>
Figure QLYQS_122
Amplitude of>
Figure QLYQS_132
Representing matrix transpose->
Figure QLYQS_123
For detecting window length, +.>
Figure QLYQS_131
，/>
Figure QLYQS_120
Representation of
Figure QLYQS_127
Is a binary norm operation of->
Figure QLYQS_118
Representation->
Figure QLYQS_128
Is calculated by a binary norm of (2);
wherein, when the detection statistics are
Figure QLYQS_135
Less than a set threshold->
Figure QLYQS_136
Determining that the carrier is in the stationary state;
The second processing unit is specifically configured to:
the integrated navigation system adopts the Kalman filtering model, and the state vector of the kth measurement time is as follows:
Figure QLYQS_137
,
wherein,,
Figure QLYQS_139
representing a position error vector, consisting of longitude, latitude and altitude;
Figure QLYQS_143
representing the earth reference velocity error vector, +.>
Figure QLYQS_145
Is an attitude error vector; />
Figure QLYQS_140
The zero offset error vector is a gyroscope;
Figure QLYQS_141
the zero offset error vector of the accelerometer; />
Figure QLYQS_144
Receiver clock difference sum Zhong Piao; wherein the subscript->
Figure QLYQS_146
Respectively representing east, north and heaven components in a local geographic coordinate system, subscript +.>
Figure QLYQS_138
Respectively representing right, front and upper components in a carrier coordinate system; measurement vector at kth measurement time +.>
Figure QLYQS_142
The expression is as follows:
Figure QLYQS_147
,
wherein,,
Figure QLYQS_148
、/>
Figure QLYQS_149
the observed pseudo-range and the observed pseudo-range rate of the ith satellite at the k moment are respectively +.>
Figure QLYQS_150
，
Figure QLYQS_151
To observe the number of satellites; the system state equation and measurement equation are: />
Figure QLYQS_152
,
Wherein,,
Figure QLYQS_154
and->
Figure QLYQS_158
State vectors representing the times k and k-1, respectively,/->
Figure QLYQS_161
Representing the state noise vector at time k-1,
Figure QLYQS_155
the state transition matrix at the moment k-1 is obtained by an error equation of the inertial navigation system INS and a clock error model of the global satellite navigation system GNSS receiver, and a first order approximation is adopted from continuous to discrete process; / >
Figure QLYQS_157
Representation ofkTime measurement matrix->
Figure QLYQS_160
Is thatkObserving noise vector at moment, < >>
Figure QLYQS_163
Is thatkTime state noise vector, ">
Figure QLYQS_153
And->
Figure QLYQS_156
All assuming zero mean Gaussian white noise, covariance was calculated by +.>
Figure QLYQS_159
And->
Figure QLYQS_162
A representation;
based on Kalman filtering, the measurement vector of the current moment is utilized
Figure QLYQS_164
And state prediction vector +.>
Figure QLYQS_165
Construction of an innovation vector->
Figure QLYQS_166
The method comprises the following steps: />
Figure QLYQS_167
。
7. The apparatus for detecting fraud in a integrated navigation system of claim 6, wherein the third processing unit is specifically configured to:
when the carrier is detected to be in the static state, the speeds of the carrier in three directions under a vehicle body coordinate system b system are zero,
Figure QLYQS_168
the speed measurement of the carrier, which is calculated by the global navigation satellite system GNSS receiver at this moment k, is +.>
Figure QLYQS_169
Performing deception jamming detection on the global satellite navigation system GNSS receiver, and constructing a generalized sense by using a velocity measurement value of the carrier obtained by the calculation of the global satellite navigation system GNSS receiver as deception detection statistics in the static stateAnd (3) a likelihood ratio formula to obtain a deception jamming detection formula under the static state:
Figure QLYQS_170
,
wherein,,
Figure QLYQS_171
for the N-th detection statistics set, N is the number of data in the detection window, and +. >
Figure QLYQS_172
For detecting window length, +.>
Figure QLYQS_173
For GNSS observation variance>
Figure QLYQS_174
Representation->
Figure QLYQS_175
Is calculated by a binary norm of (2);
fraud detection statistics while in the stationary state
Figure QLYQS_176
Less than a set threshold->
Figure QLYQS_177
When the GNSS receiver is determined not to be deceptively interfered, the information sequence of the deceptively interfered period is acquired>
Figure QLYQS_178
As the reference noise, for detecting the deception jamming in the motion state, the average value of the innovation sequences in the period of non-deception jamming is zero, and the variance of the innovation sequences of different satellites is counted as follows:
Figure QLYQS_179
,
wherein,,
Figure QLYQS_180
for the variance of the j-th satellite innovation, M is the statistics of the number of the innovation, and +.>
Figure QLYQS_181
For a window length that is not disturbed by fraud, +.>
Figure QLYQS_182
Is the innovation of the j satellite at the moment k.
8. The apparatus for detecting fraud in a integrated navigation system of claim 7, wherein the fourth processing unit is specifically configured to:
determining the reference noise variance in the static state, and taking the deception rate as a random variable meeting non-zero mean Gaussian distribution when the carrier is in the motion state, and setting the pseudo-range rate innovation of the j-th visible satellite at the k moment in the motion state as
Figure QLYQS_183
The method comprises the steps of carrying out a first treatment on the surface of the When the global satellite navigation system GNSS receiver is not interfered by the deception, the pseudo range rate innovation variance is the corresponding reference noise variance counted under the static state, and when the global satellite navigation system GNSS receiver is interfered by the deception, the deception rate jitter makes the innovation variance larger than the reference noise variance; order the
Figure QLYQS_184
,/>
Figure QLYQS_185
Is in a deceptive state, is compromised>
Figure QLYQS_186
For deceptionSpoofing states, then spoofing in the motion state is detected as a binary hypothesis model:
Figure QLYQS_187
,
wherein the length of the detection sequence is M,
Figure QLYQS_188
is the known reference noise variance->
Figure QLYQS_189
Zero mean gaussian noise>
Figure QLYQS_190
For variance introduced by fraud signals>
Figure QLYQS_191
Unknown zero-mean gaussian distribution signals; the generalized likelihood ratio is: />
Figure QLYQS_192
,
Figure QLYQS_193
,
Estimation using MLE method
Figure QLYQS_194
The method comprises the following steps of:
Figure QLYQS_195
,
for a pair of
Figure QLYQS_196
And (3) deriving to obtain:
Figure QLYQS_197
,
let the derivative be 0, the MLE estimate that yields the variance of the spoofing signal is:
Figure QLYQS_198
,
if it is
Figure QLYQS_199
The corresponding MLE is +.>
Figure QLYQS_200
Keeping with the parameter constraint, then +.>
Figure QLYQS_201
MLE of (2) is
Figure QLYQS_202
The method comprises the steps of carrying out a first treatment on the surface of the Let->
Figure QLYQS_203
Wherein the superscript + means +_>
Figure QLYQS_204
Positive, it is MLE;
when (when)
Figure QLYQS_205
When it is judged->
Figure QLYQS_206
Estimate +.>
Figure QLYQS_207
Substituting log-likelihood ratio can obtain:
Figure QLYQS_208
,
order the
Figure QLYQS_210
Figure QLYQS_212
Is a monotonically increasing function for->
Figure QLYQS_214
Reverse->
Figure QLYQS_211
If there is
Figure QLYQS_213
Or->
Figure QLYQS_215
Judging as->
Figure QLYQS_216
The fraud detection statistic is an MLE estimate of the fraud signal variance>
Figure QLYQS_209
；
At the position of
Figure QLYQS_217
Under the condition that the innovation sequence meets zero mean variance of +.>
Figure QLYQS_218
When the number of samples M is sufficient, the innovation variance progressively satisfies the gaussian distribution:
Figure QLYQS_219
,
wherein,,
Figure QLYQS_220
,/>
Figure QLYQS_221
，/>
Figure QLYQS_222
then->
Figure QLYQS_223
The probability density function of (2) is:
Figure QLYQS_224
,
according to
Figure QLYQS_225
Is used for calculating a threshold and a threshold +. >
Figure QLYQS_226
The method comprises the following steps:
Figure QLYQS_227
,
wherein,,
Figure QLYQS_228
is an inverse Gaussian right tail probability function, +.>
Figure QLYQS_229
For false alarm probability, when statistics +>
Figure QLYQS_230
Greater than a set threshold->
Figure QLYQS_231
And determining that the global navigation satellite system GNSS receiver of the carrier is deceptively disturbed.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing a method of fraud detection for an integrated navigation system according to any of claims 1-4 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a fraud detection method of an integrated navigation system according to any of claims 1-4.
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