CN114740501A - Satellite navigation deception signal detection method based on frequency domain parameters - Google Patents

Satellite navigation deception signal detection method based on frequency domain parameters Download PDF

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Publication number
CN114740501A
CN114740501A CN202210247236.XA CN202210247236A CN114740501A CN 114740501 A CN114740501 A CN 114740501A CN 202210247236 A CN202210247236 A CN 202210247236A CN 114740501 A CN114740501 A CN 114740501A
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signal
satellite navigation
power spectrum
frequency power
time domain
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左健存
李光洁
马佳军
詹强
吴丹丹
常远培
薛颖
张宇
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Shanghai Polytechnic 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
    • G01S19/13—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
    • 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
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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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 discloses a satellite navigation deception signal detection method based on frequency domain parameters; which comprises the following steps: step 1: receiving satellite navigation signal L1 through GPS receiving antenna, analyzing and processing to obtain navigation signal carrier-to-noise ratio
Figure DEST_PATH_IMAGE002
(ii) a Step 2: to pair
Figure 31277DEST_PATH_IMAGE002
Preprocessing to obtain time domain signal
Figure DEST_PATH_IMAGE004
(ii) a And 3, step 3: for time domain signals
Figure 886100DEST_PATH_IMAGE004
Processing the low-frequency power spectrum to obtain a low-frequency power spectrum signal X [ k ]](ii) a And 4, step 4: for X [ k ]]Performing Kalman filtering: and 5: according to
Figure DEST_PATH_IMAGE006
And (4) judging whether the satellite signal has a deception signal or not by using the characteristic parameter root mean square value. The invention can realize higher detection accuracy rate through the common satellite navigation deception signal detection equipment.

Description

Satellite navigation deception signal detection method based on frequency domain parameters
Technical Field
The invention belongs to the technical field of satellite navigation jamming signal detection, and particularly relates to a satellite navigation deception signal detection method based on frequency domain parameters.
Background
Along with the gradual improvement of a satellite navigation system, the detection requirement on satellite navigation deception signals is gradually increased, and the deception signals are strong in concealment and more harmful. Currently, common methods in detecting spoofed signals are signal power detection, angle-of-arrival detection, time-of-arrival detection, and the like. Meanwhile, a high-end navigation receiver is mostly adopted for a deception signal detection method, and an expensive antenna array is equipped, so that the detection accuracy rate is emphasized on the deception signal detection, and higher requirements are also put forward on detection equipment.
Disclosure of Invention
The invention aims to solve the problem of providing a satellite navigation deception signal detection method based on frequency domain parameters, which can realize higher detection accuracy through common satellite navigation deception signal detection equipment.
The invention adopts the following technical scheme:
a satellite navigation deception signal detection method based on frequency domain parameters comprises the following steps:
step 1: the satellite navigation signal L1 is received by the GPS receiving antenna, and the navigation signal carrier-to-noise ratio x [ n ] is obtained after analysis and processing.
Step 2: pre-treating x [ n ]:
initializing n ═ 0, x [ n ];
sampling x [ n +1], n being n +1, and k being n;
when x [ n ] < x [ n +1], x [ n +1] exchange positions, k is k +1, otherwise, go to the step (r);
when k is less than or equal to N and N is more than or equal to N, M is x [ (N +1)/2], otherwise, the step (II) is carried out;
after pretreatment, time domain signal x' n is obtained
And step 3: and (2) carrying out low-frequency power spectrum processing on the time domain signal x' [ n ]:
Figure BDA0003545522520000011
r (τ) is the autocorrelation function of x' n, μ is expected, and σ is the standard deviation;
Figure BDA0003545522520000012
obtaining the low-frequency power spectrum signal X [ k ].
And 4, step 4: kalman filtering of X [ k ]
Figure BDA0003545522520000013
Figure BDA0003545522520000021
Figure BDA0003545522520000022
Figure BDA0003545522520000023
Figure BDA0003545522520000024
Figure BDA0003545522520000025
And
Figure BDA0003545522520000026
representing the posterior state estimates at time k-1 and k, respectively
Figure BDA0003545522520000027
The prior state estimate at time k is the intermediate calculation result of the filtering, i.e. the result of k time predicted from the optimal estimate at the last time (k-time), is the result of the prediction equation.
Pk-1And Pk: representing the posterior estimated covariance at time k-1 and k, respectively (i.e.
Figure BDA0003545522520000028
And
Figure BDA0003545522520000029
represents the uncertainty of the state) is one of the results of the filtering.
Figure BDA00035455225200000210
A priori estimated covariance at time k: (
Figure BDA00035455225200000211
Covariance of (d) is the intermediate calculation result of the filtering.
H: the Kalman filter is a linear relation, is responsible for converting a measured value of m dimensions into n dimensions, conforms to the mathematical form of the state variable and is one of the preconditions of the filtering.
X [ k ]: the measured value (observed value), is the input to the filtering.
Kk: a filter gain matrix, which is an intermediate calculation result of the filtering, Kalman gainGain, or kalman coefficient.
A is a state transition matrix, which is actually a guess model for the target state transition. For example, in moving object tracking, a state transition matrix is often used to model the motion of an object, which may be uniform linear motion or uniform acceleration. When the state transition matrix does not conform to the state transition model of the target, the filtering may quickly diverge.
Process excitation noise covariance (covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. The value of Q is difficult to determine because we cannot observe the process signal directly. The Kalman filter is used for estimating state variables of a discrete time process, namely noise brought by a prediction model. A state transition covariance matrix.
And R, measuring the noise covariance. When the filter is actually implemented, the measured noise covariance R is typically observed and is a known condition of the filter.
B is a matrix that converts the input to a state.
Figure BDA00035455225200000212
The residual errors of actual observation and predicted observation are corrected with Kalman gain to obtain the posterior, and the signal is obtained
Figure BDA00035455225200000213
And 5: according to
Figure BDA00035455225200000214
And the characteristic parameter root mean square value can judge whether the satellite signal has a deception signal.
Compared with the prior art, the invention has the advantages that the detection accuracy is obviously improved through the signal low-frequency power spectrum conversion and the signal filtering algorithm in the step 3 and the step 4, the detection method is simple, the detection equipment is easy to obtain, and the practicability is strong.
Drawings
Fig. 1 is a diagram of an original example of a satellite navigation true signal of embodiment 1.
Fig. 2 is a diagram of an original example of a satellite navigation spoofing signal of embodiment 1.
Fig. 3 is a diagram showing an example of signals after satellite navigation real signal processing according to embodiment 1.
Fig. 4 is a diagram showing an example of signals after satellite navigation spoofing signal processing of embodiment 1.
Detailed Description
The invention is further illustrated by the following figures and examples, which should not be construed as limiting the scope of the invention.
Example 1
Firstly, a real satellite navigation signal is received through a receiver, and the real satellite navigation signal is preprocessed to obtain a time domain signal x1[n]Which corresponds to fig. 1. Similarly, preprocessing the received deception satellite navigation signal to obtain a time domain signal x2[n]Which corresponds to fig. 2.
The time-domain signal x is then further processed according to step 21[n]Obtain a signal x1′[n]Processing a time-domain signal x2[n]Obtain a signal x2′[n]Then x is respectively put1′[n]，x2′[n]Processing the frequency domain according to the step 3 to obtain a low-frequency power spectrum X1[k]，X2[k]。
Last X1[k]，X2[k]Respectively performing Kalman filtering treatment according to the step 4 to obtain
Figure BDA0003545522520000031
Which correspond to fig. 3 and 4, respectively. According to the signal
Figure BDA0003545522520000032
The root mean square value of (2) is set to be an appropriate root mean square value to be used for judging whether a deception signal exists or not. And setting a threshold value to be 15 according to the signal characteristics, wherein when the root mean square value of the processed signal is larger than 15, the processed signal is a deceptive signal, and otherwise, the processed signal is a real signal. The detection rate of the deception signal of the invention is 92.8% by checking 18000 groups of data collected by a receiver, compared with the detection rate based on the noise-carrying capacityThe detection accuracy of the method is higher than 80% of that of an estimation detection method (see: GNSS sounding detection based on receiver C/N0 estimators, ION GNSS12 Conference, Session C5, Nashville, TN, Sep 18-21,2012).
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be limited only by the attached claims.

Claims (4)

1. A satellite navigation deception signal detection method based on frequency domain parameters is characterized by comprising the following steps:
step 1: receiving satellite navigation signal L1 through GPS receiving antenna, and obtaining navigation signal carrier-to-noise ratio x [ n ] after analysis and processing;
and 2, step: preprocessing x [ n ] to obtain a time domain signal x' n ];
and step 3: carrying out low-frequency power spectrum processing on the time domain signal X' n to obtain a low-frequency power spectrum signal X k;
and 4, step 4: kalman filtering X [ k ]:
Figure FDA0003545522510000011
Figure FDA0003545522510000012
Figure FDA0003545522510000013
Figure FDA0003545522510000014
Figure FDA0003545522510000015
wherein:
Figure FDA0003545522510000016
and
Figure FDA0003545522510000017
respectively representing posterior state estimated values at the k-1 moment and the k moment;
Figure FDA0003545522510000018
a priori state estimation value at the moment k;
Pk-1and Pk: respectively representing the posterior estimation covariance of the k-1 moment and the k moment;
Figure FDA0003545522510000019
a priori estimating covariance at the time k;
h: is a state variable to measurement transition matrix;
x [ k ]: a measurement value, which is the filtered input;
Kk: a filter gain matrix;
a: a state transition matrix;
q: process excitation noise covariance;
r: measuring a noise covariance;
b: is a matrix that converts an input into a state;
Figure FDA00035455225100000110
the residual errors of actual observation and predicted observation are corrected and the priori prediction is obtained together with Kalman gain to obtain the posteriorSignal
Figure FDA00035455225100000111
And 5: according to
Figure FDA00035455225100000112
And (4) judging whether the satellite signal has a deception signal or not by using the characteristic parameter root mean square value.
2. The method of claim 1, wherein in step 2, the preprocessing step comprises:
initializing n as 0, x [ n ];
sampling x [ n +1], n is n +1, and k is n;
when x [ n ] < x [ n +1], x [ n +1] exchange positions, and k is k +1, otherwise, go to step (r);
when k is less than or equal to N and N is more than or equal to N, M is equal to x [ (N +1)/2], otherwise, turning to the step (II);
and preprocessing to obtain a time domain signal x' n.
3. The method for detecting satellite navigation spoofing signals of claim 1, wherein in step 3, the time domain signal x' n is processed by low frequency power spectrum:
Figure FDA0003545522510000021
r (τ) is the autocorrelation function of x' n, μ is expected, and σ is the standard deviation;
Figure FDA0003545522510000022
obtaining the low-frequency power spectrum signal X [ k ].
4. The method of detecting satellite navigation spoofing signals of claim 1,in step 5, the filtered signal is filtered
Figure FDA0003545522510000023
The root mean square value of (A) is set as a threshold value
Figure FDA0003545522510000024
The root mean square value of the characteristic parameter is larger than the signal characteristic set threshold value, namely, the satellite signal is proved to have a deception signal.
CN202210247236.XA 2022-03-14 2022-03-14 Satellite navigation deception signal detection method based on frequency domain parameters Pending CN114740501A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116502071A (en) * 2023-06-26 2023-07-28 武汉能钠智能装备技术股份有限公司 Key signal detection system and method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116502071A (en) * 2023-06-26 2023-07-28 武汉能钠智能装备技术股份有限公司 Key signal detection system and method
CN116502071B (en) * 2023-06-26 2023-11-17 武汉能钠智能装备技术股份有限公司 Key signal detection system and method

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