A deception jamming detection method based on dead position assistance information

Abstract

The present invention relates to a deception interference detection method, and in particular to a deception interference detection method based on dead position auxiliary information, which is used to solve the shortcoming that conventional deception detection methods are difficult to effectively apply to full-star deception detection. The deception interference detection method based on dead position auxiliary information constructs a deception detection statistic Z according to known dead position auxiliary information, and analyzes through theory and simulation experiments that when there is no deception interference signal, the deception detection statistic Z obeys the chi-square distribution. The present invention has a good detection effect on both full-star deception and partial-star deception.

Description

Technical Field

[0001] The present invention relates to a deception interference detection method, and in particular to a deception interference detection method based on dead position auxiliary information. Background Art

[0002] With the vigorous development of satellite navigation systems in recent decades, the corresponding satellite deception methods are also constantly developing and updating, resulting in increasingly prominent security issues in satellite navigation systems. On the one hand, since the structure of ordinary civilian satellite navigation signals is completely open to the public, it is easy for deception devices to simulate and generate fake signals that suppress real signals, thereby misleading the capture and positioning results of the target receiver. On the other hand, although military satellite navigation signals are encrypted, deception devices can still deceive and interfere with target receivers by forwarding real signals and adding signal delays. As a key issue that endangers navigation security, artificial deception attacks will have a significant impact on the positioning services of satellite navigation systems after successfully deceiving target receivers.

[0003] The artificial interference to the target receiver can be roughly divided into suppression and deception. Suppression interference is easy to detect during implementation. It affects the operation of the user receiver by transmitting high-power signals to cause channel blocking. Deception interference is to send a signal similar to the real satellite signal to the target receiver through the deception interference device, inducing the target receiver to capture the forged signal, and finally solve the wrong positioning and timing results. Deception interference is usually more concealed and more harmful. The target receiver without deception detection function often does not detect abnormalities after being deceived, and can even control the target receiver unknowingly. Deception interference can be divided into relay and generate. Compared with the generation of deception interference, forwarding deception interference does not need to know the structure of the real satellite signal, and the implementation process is relatively simple. It even has the same deception interference effect on encrypted military target receivers. In addition, according to the coverage of deception interference, deception interference can also be divided into full-star deception interference and partial-star deception interference.

[0004] In full-star spoofing, the attacker generates or modifies the signals of all visible satellites, thereby controlling the overall positioning and time synchronization functions of the receiver. This deception is large-scale and aims to completely control the target's navigation system. Partial-star spoofing only affects some satellite signals.

[0005] Conventional deception detection methods often face some difficulties, which make them difficult to be effectively applied to all-star deception detection. The main reasons are as follows:

[0006] (1) Conventional spoofing detection methods cannot process and analyze the complex interactions of multi-source signals and can therefore only analyze signals from a single or a few satellites;

[0007] (2) Conventional spoofing detection methods may rely on comparing certain attributes of a single signal, such as signal strength, arrival time, etc., to determine whether it has been tampered with. However, full-satellite spoofing simulates the entire satellite signal environment, making these single judgment criteria insufficient to provide effective detection;

[0008] (3) Conventional spoofing detection methods do not have sufficient processing power to monitor and analyze data from all satellites simultaneously.

[0009] (4) Global spoofing may involve very sophisticated time synchronization issues, as the attacker may precisely adjust the timestamp of each forged signal to create a more precise spoofing effect. This level of time synchronization and accuracy is often difficult to achieve using conventional spoofing detection methods. Summary of the invention

[0010] The purpose of the present invention is to solve the shortcoming that conventional deception detection methods are difficult to effectively apply to full-star deception detection, and to provide a deception interference detection method based on dead position auxiliary information.

[0011] In order to solve the deficiencies of the above-mentioned prior art, the present invention provides the following technical solutions:

[0012] A deception interference detection method based on dead position assistance information, which is special in that it includes the following steps:

[0013] Step 1: The receiver collects navigation satellite signals, obtains pseudo-range observations, and calculates the positioning coordinates X of the receiver at the measurement epoch k. k ;

[0014] Step 2: Based on the positioning coordinates X of the receiver at time k-1 k-1 , calculate the estimated position increment of the receiver in the east and north directions in the ENU coordinate system and

[0015] Step 3: Estimate the position increment obtained in step 2 and The mechanical sensors installed on the left and right wheels of the vehicle where the receiver is located are used for dead reckoning to calculate the predicted position increment of the receiver in the east and north directions in the ENU coordinate system. and

[0016] Step 4: Use the difference between the estimated position increment and the predicted position increment of the receiver in the east and north directions at the measurement epoch k to construct the deception detection statistic Z, and assume that when there is no deception interference signal, the deception detection statistic Z constructed by K measurement epochs obeys a chi-square distribution with 2K degrees of freedom; when there is a deception interference signal, the constructed deception detection statistic Z obeys a non-central chi-square distribution with 2K degrees of freedom, and the non-central parameter is λ, as follows:

[0017] H0 (no deception):

[0018] H1 (deception):

[0019] Step 5: Determine the false alarm probability, and calculate the detection threshold η based on the false alarm probability and the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis;

[0020] Step 6: Determine the size of the detection statistic Z and the detection threshold value η. If Z>η, it means that there is a deception interference signal in the navigation satellite signal; otherwise, it means that there is no deception interference signal.

[0021] Furthermore, the step 2 is specifically as follows:

[0022] The position increment estimate Δr of the receiver in the ECEF coordinate system is obtained by subtracting the positioning coordinates at the measurement epoch k from the measurement epoch k-1. e :

[0023] Δr e =X k -X k-1 =[Δx k , Δy k , Δz k ]

[0024] Where Δx k , Δy k , Δz k are the estimated position increments of the receiver in the x, y, and z directions in the ECEF coordinate system, respectively;

[0025] Transform the position increment vector from the ECEF coordinate system to the ENU coordinate system to obtain the position increment estimate of the receiver in the ENU coordinate system

[0026]

[0027] Where R is the rotation matrix, They are the estimated values ​​of the receiver’s position increment in the east, north and sky directions in the ENU coordinate system, respectively.

[0028] Furthermore, the step 3 is specifically as follows:

[0029] The output results of the mechanical sensors installed on the left and right wheels of the vehicle are converted into the left wheel movement distance and the right wheel movement distance, respectively expressed as Δ LR and Δ RR , the expression is described as follows:

[0030]

[0031]

[0032] Where W R is the distance between the left and right wheels of the vehicle, ω is the change in the azimuth of the movement, R′ is the distance from the center of the left or right wheel to the center of rotation when the vehicle is moving, Δ is the movement distance of the vehicle, and ω is the change in the azimuth of the movement of the vehicle;

[0033] Calculates the predicted value of the receiver's position increment in the east and north directions and They are:

[0034]

[0035]

[0036] In the formula, e k 、e k-1 are the vehicle's position in the east direction at measurement epoch k and measurement epoch k-1, respectively; k-1 is the azimuth of the vehicle at the time of measuring epoch k-1, i.e., the direction of travel of the vehicle; n k 、n k-1 are the vehicle’s position in the north direction at measurement epoch k and measurement epoch k-1, respectively.

[0037] Further, in step 4, the difference between the estimated position increment and the predicted position increment of the receiver in the east and north directions at the measurement epoch k is used to construct the deception detection statistic Z, which is specifically:

[0038] The difference ΔE between the estimated and predicted position increments of the receiver in the east and north directions at epoch k is k and ΔN k Respectively expressed as:

[0039]

[0040]

[0041] Then construct the deception detection statistic Z as follows:

[0042]

[0043] Where K is the number of measurement epochs involved in the detection, and are the error variances of the position increment estimates in the easting and northing directions, respectively.

[0044] Furthermore, in step 4, the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis is:

[0045]

[0046] Where Γ(K) is the value of the gamma function at point K;

[0047] The probability density function p(Z|H1) of the deception detection statistic Z under the H1 hypothesis is:

[0048]

[0049] In the formula, The order is The first kind of modified Bessel function;

[0050] The non-central parameter λ is:

[0051]

[0052] In the formula, They represent the deceptive positioning deviation in the east direction between the measurement epoch k-1 and the measurement epoch k, They represent the deceptive positioning deviation in the north direction at the measurement epoch k-1 and the measurement epoch k respectively.

[0053] Furthermore, the step 4 also includes:

[0054] The change factor of the positioning deviation influence coefficient between the measurement epoch k-1 and the measurement epoch k is defined as α k ;

[0055] The pseudo-range positioning equations used by GPS are as follows:

[0056]

[0057] Among them, ρ i is the pseudorange measurement value from the ith satellite to the receiver, x, y, z are the coordinates of the receiver, x i ,y i 、z i is the coordinate of the ith satellite, c is the speed of light, and δt is the deviation between the receiver clock and the satellite clock;

[0058] The following equation is derived from the least squares solution of the pseudorange positioning equations:

[0059]

[0060] In the formula, ε e , ε n , ε u are the positioning deviations of the receiver in the east, north and sky directions respectively, ε δt is the clock deviation, ε ρ is the pseudorange error, G is the Jacobian matrix, G T is the transpose of the Jacobian matrix, and D is the estimation matrix used to estimate the position error;

[0061] Get the change factor α k ,as follows:

[0062]

[0063] Where n is the satellite PRN number, N is the total number of satellites participating in pseudorange positioning, D(1, n) and D(2, n) represent the nth element in the first row and the nth element in the second row of the matrix D, respectively. The superscript k and superscript k-1 represent the measurement epoch k and the measurement epoch k-1, respectively.

[0064] According to α k The value of is used to obtain the response capability to deception interference at different measurement times to further confirm the false alarm probability.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] (1) The present invention provides a deception interference detection method based on dead position auxiliary information. A deception detection statistic Z is constructed according to the known dead position auxiliary information. The deception detection statistic Z obeys the chi-square distribution when there is no deception interference signal through theoretical and simulation experiments. The present invention has a good detection effect on both full-star deception and partial-star deception.

[0067] (2) The present invention provides a deception interference detection method based on positioning auxiliary information, which provides a deception detection mechanism of multi-source data fusion by combining the position data of the receiver and the physical movement data of the vehicle mechanical sensor, thereby increasing the security of the present invention.

[0068] (3) The present invention provides a deception interference detection method based on dead position assistance information by adjusting the measurement epoch number K, the detection threshold value η and the change factor α of the positioning deviation influence coefficient. k , providing a certain degree of adaptability and flexibility. This allows the present invention to be optimized according to specific application scenarios and the nature of deception threats to achieve the best detection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flowchart of a deception interference detection method based on dead position assistance information of the present invention;

[0070] Figure 2 : is a ROC (Receiver Operating Characteristic) curve diagram of the detector under different non-central parameters λ in an embodiment of the present invention;

[0071] Figure 3 It is a curve diagram of the deception detection statistic Z and the acquisition time in the scenario of no deception interference and the scenario of all-satellite deception interference when the measurement epoch number K is 1 in the embodiment of the present invention;

[0072] Figure 4 A curve diagram of the deception detection statistic Z and the acquisition time when the measurement epoch number K is 1, the number of deception interference signals is 1, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip, respectively, in an embodiment of the present invention;

[0073] Figure 5 A curve diagram of the deception detection statistic Z and the acquisition time when the measurement epoch number K is 1, the number of deception interference signals is 3, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip respectively in the embodiment of the present invention;

[0074] Figure 6 A curve diagram of the deception detection statistic Z and the acquisition time when the number of measurement epochs K is 5, the number of deception interference signals is 1, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip, respectively, in an embodiment of the present invention;

[0075] Figure 7 A curve diagram of the deception detection statistic Z and the acquisition time when the number of measurement epochs K is 5, the number of deception interference signals is 3, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip respectively in an embodiment of the present invention;

[0076] Figure 8 A curve diagram of the deception detection statistic Z and the acquisition time when the number of measurement epochs K is 10, the number of deception interference signals is 1, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip, respectively, in an embodiment of the present invention;

[0077] Fig. 9 A curve diagram of the deception detection statistic Z and the acquisition time when the number of measurement epochs K is 10, the number of deception interference signals is 3, and the forwarding deception delay is 0.1 chip, 0.2 chip, and 0.3 chip respectively in an embodiment of the present invention;

[0078] Fig.10Graph showing the detection probability and forwarding deception delay corresponding to different measurement epoch numbers K in a single-satellite deception interference scenario in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be further described below in conjunction with the accompanying drawings and exemplary embodiments.

[0080] Reference Figure 1 , a deception interference detection method based on dead position assistance information, comprising the following steps:

[0081] Step 1: The receiver collects navigation satellite signals, obtains pseudo-range observations, and calculates the positioning coordinates X of the receiver at the measurement epoch k. k =[x k ,y k , z k ];

[0082] Step 2: Based on the positioning coordinates X of the receiver at time k-1 k-1 =[x k-1 ,y k-1 , z k-1 ], calculate the estimated position increment of the receiver in the east and north directions in the ENU (EastNorth Up) coordinate system and

[0083] The estimated value of the receiver's position increment in the ECEF (Earth-Centered Earth-Fixed) coordinate system is obtained by subtracting the previous and next measurement epochs:

[0084] Δr e =X k -X k-1 =[Δx k , Δy k , Δz k ]

[0085] Where Δx k , Δy k , Δz k are the estimated position increments of the receiver in the x, y, and z directions in the ECEF coordinate system, respectively;

[0086] Transform the position increment vector from the ECEF coordinate system to the ENU coordinate system to obtain the estimated position increment of the receiver in the ENU coordinate system:

[0087]

[0088] Where R is the rotation matrix, are the estimated values ​​of the receiver’s position increment in the east, north, and sky directions in the ENU coordinate system, respectively;

[0089] Step 3: Estimate the position increment obtained in step 2 and The mechanical sensors installed on the left and right wheels of the vehicle where the receiver is located are used for dead reckoning to calculate the predicted position increment of the receiver in the east and north directions in the ENU coordinate system. and Specifically:

[0090] The output results of the mechanical sensors (speed sensors) installed on the left and right wheels of the vehicle are converted into the left wheel movement distance and the right wheel movement distance, respectively expressed as Δ LR and Δ RR ,as follows:

[0091]

[0092]

[0093] Where W R is the distance between the left and right wheels of the vehicle, Δ LR and ΔR R are the moving distances of the left wheel and the right wheel respectively, ω is the change in the moving azimuth, and R′ is the distance from the center of the left / right wheel to the center of rotation when the vehicle is moving;

[0094] The calculation formulas for the vehicle's moving distance Δ and the moving azimuth change ω are:

[0095]

[0096]

[0097] Get the predicted value of the receiver's position increment in the east and north directions and They are:

[0098]

[0099]

[0100] In the formula, e k 、e k-1 are the positions of the vehicle in the east direction at the measurement epoch k-1; k-1 is the azimuth of the vehicle at the time of measuring epoch k-1, i.e., the direction of travel of the vehicle; n k 、n k-1 are the vehicle positions in the north direction at the measurement epoch k-1 respectively;

[0101] Step 4: Assume that the difference between the estimated position increment and the predicted position increment of the receiver in the east and north directions at epoch k is ΔE k and ΔN k Respectively expressed as:

[0102]

[0103]

[0104] Then the deception detection statistic Z is:

[0105]

[0106] Where K is the number of measurement epochs involved in the detection, and are the error variances of the position increment estimates in the east and north directions, respectively;

[0107] If the pseudo-range observations collected by the receiver at a certain moment contain deception values ​​from deception interference signals, the position increment estimate calculated from the pseudo-range measurements is and The size of is not only affected by the pseudorange observation error, but also by the more significant deception interference signal. Therefore, the difference between the receiver's position increment estimate and the position increment prediction value is ΔE k and ΔN k The mean of will deviate from zero;

[0108] If the deception detection statistic Z calculated after the receiver is positioned at a certain moment is relatively large, it means that there is satellite signal deception interference within the measurement epoch number K;

[0109] Assume that when there is no deception jamming signal, the deception detection statistic Z constructed at K measurement epochs obeys a chi-square distribution with 2K degrees of freedom; when there is a deception jamming signal, the constructed deception detection statistic Z obeys a non-central chi-square distribution with 2K degrees of freedom, and the non-central parameter is λ, as follows:

[0110] H0 (no deception):

[0111] H1 (deception):

[0112] Then the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis is expressed as:

[0113]

[0114] Where Γ(K) is the value of the gamma function at point K; p(Z|H0) is used to set the detection threshold η;

[0115] The probability density function p(Z|H1) of the deception detection statistic Z under the H1 hypothesis is expressed as:

[0116]

[0117] In the formula, The order is The first kind of modified Bessel function;

[0118] When the receiver is deceptively interfered at a certain moment, the positioning deviation in the east direction is described as follows: e , the variance is The normal distribution of the positioning deviation in the north direction is described as follows: n , the variance is The normal distribution of the receiver's deception position increment deviation in the east and north directions obtained by measuring epoch k-1 and time k is respectively:

[0119] East direction:

[0120] North direction:

[0121] Where N[] represents normal distribution;

[0122] Therefore, the non-central parameter λ is defined as:

[0123]

[0124] In the formula, They represent the deceptive positioning deviation in the east direction at the measurement epoch k-1 and the measurement epoch k, respectively. They represent the deceptive positioning deviation in the north direction at the measurement epoch k-1 and the measurement epoch k respectively;

[0125] The noncentral parameter λ is used to describe the noncentrality of the noncentral chi-square distribution followed by the deception detection statistic Z in the presence of deception. The size of the noncentral parameter λ directly affects the sensitivity of deception detection. A larger λ value means a higher detection sensitivity.

[0126] The change factor of the positioning deviation influence coefficient between the measurement epoch k-1 and the measurement epoch k is defined as α k ;

[0127] The pseudorange positioning equations used by the Global Positioning System (GPS) are as follows:

[0128]

[0129] Among them, ρ i is the pseudorange measurement value from the ith satellite to the receiver, x, y, z are the coordinates of the receiver, x i ,y i 、z i is the coordinate of the ith satellite, c is the speed of light, and δt is the deviation between the receiver clock and the satellite clock;

[0130] The following equation is derived from the least squares solution of the pseudorange positioning equations:

[0131]

[0132] In the formula, ε e , ε n , ε u are the positioning deviations of the receiver in the east, north and sky directions respectively, ε δt is the clock deviation, ε ρ is the pseudorange error, G is the Jacobian matrix, G T is the transpose of the Jacobian matrix, and D is the estimation matrix used to estimate the position error;

[0133] When the geometric distribution between the receiver and the positioning satellite is determined, the Jacobian matrix G will be uniquely determined and will not be affected by other factors. At this time, the estimated matrix D calculated by the Jacobian matrix G is also regarded as a known quantity, so the change factor α is obtained. k as follows:

[0134]

[0135] Where n is the satellite PRN (Pseudo-Random Noise) number, N is the total number of satellites participating in pseudorange positioning, D(1, n) and D(2, n) represent the nth element in the first row and the nth element in the second row of the matrix D, respectively, and the superscript k and superscript k-1 represent the measurement epoch k and the measurement epoch k-1, respectively;

[0136] Change factor α k For evaluating the responsiveness to deceptive interference, a larger α k The value indicates that the positioning deviation caused by deception interference is more significant, which helps to improve the detection probability of deception signals;

[0137] If the previous and next measurements are subject to the same deception interference, the change factor α kThe larger the value, the greater the difference in the deception positioning offset between the previous and next moments. If the horizontal positioning accuracy (Horizontal Dilution Of Precision, HDOP) remains unchanged, the non-central parameter λ will also increase, making it easier to detect deception. When the calculated change factor α k If it is small, you can try to increase the number of measurement epochs K to increase the change factor α k ;

[0138] Table 1 shows the change factor α at different levels when the horizontal positioning error standard deviation is 2 meters and the delay is small. k The corresponding range of non-central parameter λ is:

[0139] Table 1

[0140]

[0141]

[0142] Step 5: After determining the false alarm probability, the detection threshold η is calculated based on the false alarm probability and the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis;

[0143] After determining the number of detection epochs K, the detection probability is actually a function of the false alarm probability. Under a certain false alarm probability, the detection probability is only related to the non-central parameter λ of the non-central chi-square distribution. Therefore, under the assumption that there is no deception signal, that is, under the H0 hypothesis, the probability that the deception detection statistic Z is greater than the detection threshold η is equal to the false alarm probability.

[0144] Step 6: Determine the size of the detection statistic Z and the detection threshold value η. If Z>η, it means that there is a deception interference signal in the navigation satellite signal; otherwise, it means that there is no deception interference signal.

[0145] The ROC curve of the embodiment of the present invention under different non-central parameters λ is used to analyze the detection performance of the embodiment of the present invention. The ROC curve represents the recognition ability of the deception detector under different detection threshold values, which is generated by the false alarm probability on the x-axis and the detection probability with different detection threshold values ​​η on the y-axis, as shown in FIG. Figure 2 As shown;

[0146] When the measurement epoch number K is 5 or 10, the change factor α k Increase, the detection performance of the spoofing interference signal with a delay of 1 code chip in the embodiment of the present invention is greatly improved; the size of the non-central parameter λ is mainly related to the variation factor α k , the spoofed delay chips, and the error variance of the position increment estimate in the east and north directions and These parameters directly affect the distribution of the deception detection statistic Z; the change factor α at the current and subsequent measurement times k The larger the deception delay code chip is, the larger the non-central parameter λ will be. and and pseudorange measurement error σ 2 It is related to the geometric distribution of satellites. The pseudorange measurement error σ 2 The smaller the diagonal elements of the weight matrix H, the and The smaller it is, the larger the non-central parameter λ is; therefore, the factors affecting the performance of the detector can be summarized as follows: k The higher the value, the better the detection performance of the present invention for small-delay deception interference; and the detection performance is improved as the variance of pseudo-range measurement noise decreases.

[0147] Experimental verification:

[0148] Two deception scenarios are set, one is the full-satellite deception interference scenario (the deception signal simulates the signals of all visible satellites), and the other is the partial-satellite deception interference scenario (partial-satellite deception only simulates the signals of some visible satellites). Deception simulation experiments are carried out in different deception scenarios, and the relationship between the obtained deception detection statistic Z and the detection threshold value η is analyzed. The simulation experiment environment settings are shown in Table 2 below:

[0149] Table 2

[0150]

[0151] The simulation results in the full star deception scenario are as follows Figure 3 As shown in the figure, in the experiment, the number of measurement epochs K is selected as 1, the false alarm probability is 0.001, and the position of the deception jammer is assumed to remain unchanged. Since the positioning coordinates of the receiver after being deceived all point to the deception jammer, the size of the deception detection statistic Z is mainly affected by the position of the deception jammer and the actual position increment of the receiver. Figure 3 It can be seen from the simulation results that the deception detection statistic Z is significantly different from the deception detection statistic Z without deception, and its size increases with the increase of the receiver's deception positioning offset distance, and is greater than the detection threshold value η (shown by the black dotted line in the figure). Under full-star deception, when the forwarding deception delay in each deception signal is the same, the size of the deception detection statistic Z is only related to the actual movement distance of the receiver. In the test case, the deception detection statistic Z under full-star deception at each measurement epoch is significantly greater than the detection threshold value η. As long as the receiver is constantly moving, the detection probability of full-star deception can reach 100%, and is not affected by the deception delay.

[0152] The simulation results of the detection quantity Z under partial star deception are as follows: Figures 4 to 9As shown in the figure. In the experiment, different measurement epochs K are selected and the false alarm probability is fixed to 0.001. Figure 4 and Figure 5 For the measurement epoch number K being 1, the distribution of the deception detection statistic Z is simulated when the number of deception jammer signals is 1 and 3; Figure 6 and Figure 7 For the measurement epoch number K being 5, the distribution of the deception detection statistic Z is obtained by simulation when the number of deception interference signals is 1 and 3. Figure 8 and Fig. 9 The distribution of the deception detection statistic Z when the number of deception interference signals is 1 and 3 is simulated for the measurement epoch number K of 10. It can be seen that as the measurement epoch number K increases, the number of missed detections of the deception detection statistic Z decreases.

[0153] Errors ranging from 0 to 2 chips are added to different satellites, and the average value of the detection probability under single-satellite deception is then calculated. Fig.10 The simulation results of the average value of the single-satellite deception detection probability under different measurement epoch numbers K show that the detection performance of the detection scheme is better as the measurement epoch number K and the deception delay increase. When the measurement epoch number K is large enough, the detection probability of the present invention for forwarding deception interference with a deception delay of more than 0.3 code chips can reach more than 80%.

Claims

1. A deception interference detection method based on dead position assistance information, characterized in that: The steps include: Step 1: The receiver collects navigation satellite signals, obtains pseudo-range observations, and calculates the positioning coordinates X of the receiver at the measurement epoch k. k ; Step 2: Based on the positioning coordinates X of the receiver at time k-1 k-1 , calculate the estimated position increment of the receiver in the east and north directions in the ENU coordinate system and Step 3: Estimate the position increment obtained in step 2 and The mechanical sensors installed on the left and right wheels of the vehicle where the receiver is located are used for dead reckoning to calculate the predicted position increment of the receiver in the east and north directions in the ENU coordinate system. and Step 4: Use the difference between the estimated position increment and the predicted position increment of the receiver in the east and north directions at the measurement epoch k to construct the deception detection statistic Z, and assume that when there is no deception interference signal, the deception detection statistic Z constructed by K measurement epochs obeys a chi-square distribution with 2K degrees of freedom; when there is a deception interference signal, the constructed deception detection statistic Z obeys a non-central chi-square distribution with 2K degrees of freedom, and the non-central parameter is λ, as follows: H0 (no deception): H1 (deception): Step 5: Determine the false alarm probability, and calculate the detection threshold η based on the false alarm probability and the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis; Step 6: Determine the size of the detection statistic Z and the detection threshold value η. If Z>η, it means that there is a deception interference signal in the navigation satellite signal; otherwise, it means that there is no deception interference signal.

2. The deception interference detection method based on dead position assistance information according to claim 1 is characterized in that: The step 2 is specifically as follows: The position increment estimate Δr of the receiver in the ECEF coordinate system is obtained by subtracting the positioning coordinates at the measurement epoch k from the measurement epoch k-1. e : Δr e =X k -X k-1 =[Δx k ,Δy k ,Δz k ] Where Δx k , Δy k , Δz k are the estimated position increments of the receiver in the x, y, and z directions in the ECEF coordinate system, respectively; Transform the position increment vector from the ECEF coordinate system to the ENU coordinate system to obtain the position increment estimate of the receiver in the ENU coordinate system Where R is the rotation matrix, They are the estimated values ​​of the receiver’s position increment in the east, north and sky directions in the ENU coordinate system, respectively.

3. The deception interference detection method based on dead position assistance information according to claim 2 is characterized in that: The step 3 is specifically as follows: The output results of the mechanical sensors installed on the left and right wheels of the vehicle are converted into the left wheel movement distance and the right wheel movement distance, respectively expressed as Δ LR and Δ RR , the expression is described as follows: Where W R is the distance between the left and right wheels of the vehicle, ω is the change in the azimuth of the movement, R′ is the distance from the center of the left or right wheel to the center of rotation when the vehicle is moving, Δ is the movement distance of the vehicle, and ω is the change in the azimuth of the movement of the vehicle; Calculates the predicted value of the receiver's position increment in the east and north directions and They are: In the formula, e k 、e k-1 are the vehicle's position in the east direction at measurement epoch k and measurement epoch k-1, respectively; k-1 is the azimuth of the vehicle at the time of measuring epoch k-1, i.e., the direction of travel of the vehicle; n k 、n k-1 are the vehicle's position in the north direction at measurement epoch k and measurement epoch k-1, respectively.

4. A deception interference detection method based on dead position assistance information according to any one of claims 1 to 3, characterized in that: In step 4, the deception detection statistic Z is constructed by using the difference between the estimated position increment and the predicted position increment of the receiver in the east and north directions at the measurement epoch k: The difference ΔE between the estimated and predicted position increments of the receiver in the east and north directions at epoch k is k and ΔN k Respectively expressed as: Then construct the deception detection statistic Z as follows: Where K is the number of measurement epochs involved in the detection, and are the error variances of the position increment estimates in the easting and northing directions, respectively.

5. The deception interference detection method based on dead position assistance information according to claim 4 is characterized in that: In step 4, the probability density function p(Z|H0) of the deception detection statistic Z under the H0 hypothesis is: Where Γ(K) is the value of the gamma function at point K; The probability density function p(Z|H1) of the deception detection statistic Z under the H1 hypothesis is: In the formula, The order is The first kind of modified Bessel function; The non-central parameter λ is: In the formula, They represent the deceptive positioning deviation in the east direction at the measurement epoch k-1 and the measurement epoch k, respectively. They represent the deceptive positioning deviation in the north direction at the measurement epoch k-1 and the measurement epoch k respectively.

6. The deception interference detection method based on dead position assistance information according to claim 4 is characterized in that: The step 4 also includes: The change factor of the positioning deviation influence coefficient between the measurement epoch k-1 and the measurement epoch k is defined as α k ; The pseudo-range positioning equations used by GPS are as follows: Among them, ρ i is the pseudorange measurement value from the ith satellite to the receiver, x, y, z are the coordinates of the receiver, x i ,y i 、z i is the coordinate of the ith satellite, c is the speed of light, and δt is the deviation between the receiver clock and the satellite clock; The following equation is derived from the least squares solution of the pseudorange positioning equations: In the formula, ε e , ε n , ε u are the positioning deviations of the receiver in the east, north and sky directions respectively, ε δt is the clock deviation, ε ρ is the pseudorange error, G is the Jacobian matrix, G T is the transpose of the Jacobian matrix, and D is the estimation matrix used to estimate the position error; Get the change factor α k ,as follows: Where n is the satellite PRN number, N is the total number of satellites participating in pseudorange positioning, D(1, n) and D(2, n) represent the nth element in the first row and the nth element in the second row of the matrix D, respectively, and the superscript k and superscript k-1 represent the measurement epoch k and the measurement epoch k-1, respectively; According to α k The value of is used to obtain the response capability to deception interference at different measurement times to further confirm the false alarm probability.

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