CN116931004A - GNSS slowly-varying deception detection method based on weighted Kalman gain - Google Patents

GNSS slowly-varying deception detection method based on weighted Kalman gain Download PDF

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CN116931004A
CN116931004A CN202311196076.1A CN202311196076A CN116931004A CN 116931004 A CN116931004 A CN 116931004A CN 202311196076 A CN202311196076 A CN 202311196076A CN 116931004 A CN116931004 A CN 116931004A
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kalman gain
gnss
varying
weighted
method based
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CN202311196076.1A
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张晓宇
靳小琴
李寿鹏
檀盼龙
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Nankai University
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Nankai University
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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
    • G01S19/015—Arrangements for jamming, spoofing or other methods of denial of service of such systems
    • 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
    • G01S19/396—Determining accuracy or reliability of position or pseudorange measurements

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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 relates to the technical field of navigation system deception detection, and provides a GNSS slowly-varying deception detection method based on weighted Kalman gain. Comprising the following steps: acquiring and accumulating observation normalization information of the satellite in the measurement updating process through a sliding window to acquire accumulated normalization information; calculating a weighted Kalman gain according to the accumulated normalized information, and carrying out state update on the satellite through the weighted Kalman gain to obtain a filtering state vector; traversing all visible satellites at the measurement updating moment, and calculating the normalized innovation square sum of all satellites according to the filtering state vectors of a plurality of satellites to obtain test statistics; giving a false alarm rate, and calculating to obtain a detection threshold through the false alarm rate; and comparing the test statistic with the detection threshold, if the test statistic is larger than the detection threshold, then deception jamming exists, otherwise deception jamming does not exist. The invention suppresses the pollution of harmful news to the combined navigation filter, and improves the holding time of accurate news after deception invasion.

Description

GNSS slowly-varying deception detection method based on weighted Kalman gain
Technical Field
The invention relates to the technical field of navigation system deception detection, in particular to a GNSS slowly-varying deception detection method based on weighted Kalman gain.
Background
A global satellite navigation system (Global Navigation Satellite System, GNSS) is a global navigation system that enables a ground-based receiving device to determine its position, velocity and time by transmitting satellite signals. With the wide application of GNSS in various fields, such as autopilot, smart agriculture, disaster monitoring, etc., it has become an indispensable position sensor in modern society.
However, the fraud detection and its improvement method based on the GNSS/INS combined filter innovation adopts the conventional fixed gain extended kalman filter (extended Kalman filter, EKF), when the initial induced deviation of the slow fraud on the filter innovation is smaller than the measurement deviation of the GNSS itself, the system cannot distinguish the fraud directly from the innovation, so that the harmful innovation is added to the filter measurement update to the greatest extent, resulting in the combined filter output deviating from the true value rapidly, and thus effective fraud detection cannot be implemented.
Since the structure of GNSS signals is public, it is vulnerable to intentional spoofing, particularly slow varying spoofing, which can cause the GNSS receiver to report erroneous location and time information, with a significant loss to the user.
Disclosure of Invention
The present invention is directed to solving at least one of the technical problems existing in the related art. Therefore, the invention provides a GNSS slowly-varying cheating detection method based on weighted Kalman gain.
The invention provides a GNSS slowly-varying deception detection method based on weighted Kalman gain, which comprises the following steps:
s1: in the measurement updating process, acquiring observation normalization information of a satellite through a sliding window, and accumulating the observation normalization information to obtain accumulated normalization information;
s2: calculating a weighted Kalman gain according to the accumulated normalized information, and carrying out state update on the satellite through the weighted Kalman gain to obtain a filtering state vector;
s3: traversing all visible satellites at the measurement updating moment, and calculating the normalized innovation square sum of all satellites according to the filtering state vectors of a plurality of satellites to obtain test statistics;
s4: setting a false alarm rate, and calculating to obtain a detection threshold through the false alarm rate;
s5: and comparing the test statistic with the detection threshold, if the test statistic is larger than the detection threshold, then deception jamming exists, and if the test statistic is smaller than or equal to the detection threshold, then deception jamming does not exist.
According to the GNSS slowly-varying deception detection method based on the weighted Kalman gain, the expression of the accumulated normalized innovation is as follows:
wherein ,for accumulating normalized information->For the length of the sliding window +.>For satellite serial number>For measuring the update time +_>Normalizing the moment of innovation for the acquisition of observations +.>Is->The satellite is->The observations at the moment normalize the innovation.
According to the GNSS slowly-varying deception detection method based on the weighted Kalman gain, the acquisition of the observation normalization information in the step S1The time period isTime to->Time of day.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, the expression of the weighted Kalman gain in the step S2 is as follows:
wherein ,for weighting Kalman gain, +.>Is the traditional Kalman gain->Is the upper bound of the confidence interval.
According to the GNSS slowly-varying cheating detection method based on weighted Kalman gain, the expression of the test statistic in the step S3 is as follows:
wherein ,for test statistics, +.>To traverse the number of satellites in view within the altitude range.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, in the step S4, the calculation formula of the detection threshold is as follows:
wherein ,for a given false alarm rate, +.>For the detection threshold +.>For the chi-square distribute the degrees of freedom->As a first of the independent variables, a second of the independent variables,is the second argument.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, provided by the invention, the GNSS slowly-varying cheating detection method further comprises the following steps:
s6: and when the judgment result in the step S5 is that no deception jamming exists, the measurement update of the next detection period is shifted to carry out detection.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, the step S1 further comprises the following steps:
s11: and normalizing the accumulated normalized information again.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, provided by the invention, the reliability and the accuracy of the GNSS are ensured by adopting an effective cheating detection means, so that the existence of cheating signals is rapidly found, a coping strategy is timely adopted, and the efficient detection of slowly-varying cheating with initial induced deviation smaller than the measurement deviation of the GNSS is realized.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a GNSS slow-varying spoofing detection method based on weighted kalman gain according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. 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 following examples are illustrative of the invention but are not intended to limit the scope of the invention.
In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In describing embodiments of the present invention, it should be noted that, unless explicitly stated and limited otherwise, the terms "coupled," "coupled," and "connected" should be construed broadly, and may be either a fixed connection, a removable connection, or an integral connection, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in embodiments of the present invention will be understood in detail by those of ordinary skill in the art.
In embodiments of the invention, unless expressly specified and limited otherwise, a first feature "up" or "down" on a second feature may be that the first and second features are in direct contact, or that the first and second features are in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
An embodiment of the present invention is described below with reference to fig. 1.
The invention provides a GNSS slowly-varying deception detection method based on weighted Kalman gain, which comprises the following steps:
s1: in the measurement updating process, acquiring observation normalization information of a satellite through a sliding window, and accumulating the observation normalization information to obtain accumulated normalization information;
the expression of the accumulated normalized information is as follows:
wherein ,for accumulating normalized information->For the length of the sliding window +.>For satellite serial number>For measuring the update time +_>Normalizing the moment of innovation for the acquisition of observations +.>Is->The satellite is->The observations at the moment normalize the innovation.
Wherein the acquisition period of the observation normalization information in step S1 isTime to->Time of day.
Wherein, step S1 further comprises:
s11: and normalizing the accumulated normalized information again.
S2: calculating a weighted Kalman gain according to the accumulated normalized information, and carrying out state update on the satellite through the weighted Kalman gain to obtain a filtering state vector;
further, in step S2, a weighted kalman gain is calculated according to the accumulated normalization information obtained in step S11 after the re-normalization.
The expression of the weighted kalman gain in step S2 is:
wherein ,for weighting Kalman gain, +.>Is the traditional Kalman gain->Is the upper bound of the confidence interval.
S3: traversing all visible satellites at the measurement updating moment, and calculating the normalized innovation square sum of all satellites according to the filtering state vectors of a plurality of satellites to obtain test statistics;
wherein, the expression of the test statistic in step S3 is:
wherein ,for test statistics, +.>To traverse the number of satellites in view within the altitude range.
Further, the number of satellites in view is determined to be greater than 1 in altitude, and in some embodiments, for clarity of the traversal, an altitude greater than 5 is selected for observation.
S4: setting a false alarm rate, and calculating to obtain a detection threshold through the false alarm rate;
the calculation formula of the detection threshold in step S4 is as follows:
wherein ,for a given false alarm rate, +.>For the detection threshold +.>For the chi-square distribute the degrees of freedom->As a first of the independent variables, a second of the independent variables,is the second argument.
S5: and comparing the test statistic with the detection threshold, if the test statistic is larger than the detection threshold, then deception jamming exists, and if the test statistic is smaller than or equal to the detection threshold, then deception jamming does not exist.
Wherein, still include:
s6: and when the judgment result in the step S5 is that no deception jamming exists, the measurement update of the next detection period is shifted to carry out detection.
In some embodiments, GNSS slow-varying fraud detection simulation is performed for a flight process of a certain unmanned aerial vehicle, in the simulation, the unmanned aerial vehicle starts at 120 DEG E and 30 DEG N, then moves horizontally at a speed of 100m/s, the yaw angle is 40 DEG, the simulation time is 200s, fraud is generated from the 100 th s, the position offset in the latitude direction is caused, the speed offset of the fraud is 0.1m/s, the GNSS update period is 0.1s, the INS update period is 0.01s, and simulation parameters are shown in table 1.
Table 1 simulation parameters for GNSS slow-varying spoofing detection for unmanned aerial vehicle flight
Further, GNSS and INS data are acquired by: the GNSS signal processing unit is used for receiving real-time GNSS signals, obtaining GNSS measurement information through calculation and outputting timing signals; the INS information output unit is used for receiving the timing signals, synchronizing with the GNSS signal processing unit and outputting INS angular speed and acceleration information; the information fusion processing unit is used for fusing GNSS measurement information and INS angular velocity and acceleration information, executing state update and measurement update, and outputting information; and the deception detection unit is used for executing the GNSS slowly-varying deception detection method based on the weighted Kalman gain according to the innovation information.
Further, a sliding window 10 is set first, and all normalized information observation vectors of the 1 st satellite in the sliding window are collected at the moment of measurement and update as follows:
and accumulating the normalized information observation vector of the sliding window, and normalizing again to obtain accumulated normalized information as 0.6092.
Further, the weighted kalman gain of the 1 st satellite participating in measurement update at the measurement update time is calculated as follows:
and secondly, performing measurement updating again after obtaining the weighted Kalman gain, and obtaining a state vector filtered by the 1 st satellite.
Further, the steps are circularly executed, all visible satellite observations at the measurement update time are traversed, the number of the visible satellites is recorded as 10, and the current time spoofing detection is executed, and the specific steps are as follows: firstly, calculating normalized innovation square sum of all satellites in a time window to obtain inspection statisticsThe quantity is 36.3449, and the false alarm rate is given byThe detection threshold is 29.5883, the comparison test statistic 36.3449 is larger than the detection threshold 29.5883, deception jamming is detected, and the process is ended.
Furthermore, the weighted Kalman gain weight in the deception detection process shows that the filtering weight of an individual satellite is close to 0 in the filtering updating process, harmful information is restrained, in addition, the simulation result of the information is normalized, the maximum information deviation absolute value reaches 4.28965, deception detection is facilitated, in addition, the final execution result of the slow deception detection shows that at 100s, the detection statistic is close to the detection threshold but does not exceed the threshold, and the detection statistic rapidly exceeds the detection threshold until 101s, and the slow deception detection method provided by the invention can timely detect the slow deception, so that the safety of navigation users is ensured.
According to the GNSS slowly-varying cheating detection method based on the weighted Kalman gain, pollution of harmful news to the combined navigation filter is effectively restrained through the weighted Kalman gain, so that the holding time of accurate news after cheating invasion is prolonged, the accumulation degree of the news is further improved, the sensitivity of cheating detection is further improved on the basis, and particularly, the slowly-varying cheating with initial induced deviation smaller than the GNSS self-measurement deviation can be reliably detected.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. The GNSS slowly-varying deception detection method based on the weighted Kalman gain is characterized by comprising the following steps of:
s1: in the measurement updating process, acquiring observation normalization information of a satellite through a sliding window, and accumulating the observation normalization information to obtain accumulated normalization information;
s2: calculating a weighted Kalman gain according to the accumulated normalized information, and carrying out state update on the satellite through the weighted Kalman gain to obtain a filtering state vector;
s3: traversing all visible satellites at the measurement updating moment, and calculating the normalized innovation square sum of all satellites according to the filtering state vectors of a plurality of satellites to obtain test statistics;
s4: setting a false alarm rate, and calculating to obtain a detection threshold through the false alarm rate;
s5: and comparing the test statistic with the detection threshold, if the test statistic is larger than the detection threshold, then deception jamming exists, and if the test statistic is smaller than or equal to the detection threshold, then deception jamming does not exist.
2. The GNSS slow-varying spoofing detection method based on weighted kalman gain of claim 1 wherein the expression of the accumulated normalized innovation is:
wherein ,for accumulating normalized information->For the length of the sliding window +.>For satellite serial number>For measuring the update time +_>Normalizing the moment of innovation for the acquisition of observations +.>Is->The satellite is->The observations at the moment normalize the innovation.
3. The method for GNSS slow-varying spoofing detection based on weighted kalman gain according to claim 2, wherein the acquisition period of the observed normalized innovation in step S1 isTime to->Time of day.
4. The GNSS slow-varying spoofing detection method based on weighted kalman gain according to claim 2, wherein the expression of the weighted kalman gain in step S2 is:
wherein ,for weighting Kalman gain, +.>Is the traditional Kalman gain->Is the upper bound of the confidence interval.
5. The GNSS slow-varying spoofing detection method based on weighted kalman gain according to claim 2, wherein the expression of the test statistic in step S3 is:
wherein ,for test statistics, +.>To traverse the number of satellites in view within the altitude range.
6. The GNSS slow-varying spoofing detection method based on weighted kalman gain according to claim 1, wherein the calculation formula of the detection threshold in step S4 is:
wherein ,for a given false alarm rate, +.>For the detection threshold +.>For the chi-square distribute the degrees of freedom->As the first argument，/>Is the second argument.
7. The GNSS slow varying spoofing detection method based on weighted kalman gain of claim 1 further comprising:
s6: and when the judgment result in the step S5 is that no deception jamming exists, the measurement update of the next detection period is shifted to carry out detection.
8. The GNSS slow-varying spoofing detection method based on weighted kalman gain of claim 1 wherein step S1 further comprises:
s11: and normalizing the accumulated normalized information again.
CN202311196076.1A 2023-09-18 2023-09-18 GNSS slowly-varying deception detection method based on weighted Kalman gain Pending CN116931004A (en)

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Application publication date: 20231024