A method for detecting deceptive jamming based on correlation value characteristics

Abstract

The application discloses a kind of based on correlation value feature's deception jamming detection method, it is found in the theory derivation of correlation value of deception signal and real signal, under the traction type deception jamming, due to the existence of Doppler frequency difference, correlation value presents the characteristics of periodic signal, spectrum analysis is carried out to the periodic characteristic of correlation value, and deception detection is carried out in combination with peak detection;The application finds the influence of relative Doppler frequency between deception signal and real signal on receiver tracking stage by theoretical analysis, and it is verified by theoretical derivation and simulation that, in the case where relative Doppler frequency exists, the correlation value of receiver will show the characteristics of periodic change.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology, and specifically relates to a method for detecting spoofing interference in satellite navigation receivers. Background Technology

[0002] Satellite navigation is the most widely used navigation method today. With the continuous development of satellite navigation systems in various countries, the current satellite signal systems are rich and diverse, with numerous frequency points, and navigation and positioning accuracy is constantly improving. However, due to the public nature of satellite signals and their relatively low power at ground level, receivers are easily interfered with. With the development of software receivers and wireless equipment, deception interference of satellite signals has become possible in reality. Deception interference can cause targets to obtain incorrect positioning results unknowingly, posing a threat to safety. Therefore, researching real-time deception detection algorithms is of great significance for satellite navigation systems to ensure the accuracy and reliability of navigation and positioning.

[0003] As deception detection algorithms continue to improve, deception techniques are also constantly evolving. For even minor changes in deception, the performance of existing deception detection algorithms often drops sharply. In the intermediate frequency (IF) signal processing stage, Signal Quality Monitoring (SQM) is a commonly used deception detection method. Traditional SQM metrics use three output metrics, making the detection metrics susceptible to noise and resulting in a relatively low detection probability. These metrics achieve detection through the asymmetry introduced during correlation peak dragging. For covert deception attacks, the deceptive device slowly drags the target receiver's tracking point away from the true signal. Due to the slow dragging rate, the correlation peak asymmetry is weak, further reducing the detection rate of traditional SQM algorithms.

[0004] Spoofing interference detection relies on understanding the characteristics of its impact on the receiver to find suitable detection quantities and determine the detection threshold. Therefore, understanding the impact of spoofing interference on the receiver is crucial for determining the detection quantity and method. However, in general spoofing interference impact analysis, it's often assumed that the spoofing interference signal and the real signal have approximately the same carrier Doppler frequency. While this simplification highlights the impact of the spoofing interference signal code delay, it makes it difficult to analyze the impact of carrier frequency differences on the target receiver. Currently, spoofing detection targeting the Doppler frequency is limited and requires the target receiver to perform specific circular movements, significantly restricting its application. Summary of the Invention

[0005] To address the problems in the prior art, this invention provides a deception interference detection method based on correlation value characteristics. Through theoretical derivation, this invention proposes that under uniform traction interference, the receiver correlation value will exhibit periodic changes, and frequency domain analysis and peak detection are used to detect deception interference in the receiver.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A deception interference detection method based on correlation value characteristics is proposed. Through theoretical deduction of the correlation value between the deception signal and the real signal, it is found that under traction deception interference, due to the existence of Doppler frequency difference, the correlation value exhibits the characteristics of a periodic signal. Spectral analysis is performed on the periodic characteristics of the correlation value, and deception detection is carried out in combination with peak detection.

[0008] Before detecting deception interference, it is necessary to accumulate the correlator output values ​​for the specified window length. For GPS L1 C / A signals, the receiver integration time is typically set to 1ms, resulting in a coherent integration data output frequency of 1000Hz. When the accumulated data reaches the specified window length, window data processing begins. First, least-squares fitting is performed on the window data, and the original data is subtracted from the fitting result for detrending. Next, FFT transformation is performed on the detrended data, and the amplitude is adjusted to obtain the spectral amplitude corresponding to each frequency point. Then, the mean of the spectral amplitude is calculated, and the peak value detection threshold is obtained through a set peak-to-average power ratio (PAPR). Finally, the peak value of the spectral amplitude is compared with the threshold. If the peak value is greater than the threshold, the receiver alarms; otherwise, no alarm is triggered. Real-time processing of the receiver is achieved through continuous accumulation and looping.

[0009] Theoretical derivation of this invention:

[0010] When a receiver is subjected to deception interference, the received signal contains both the real signal and the deception signal. Assuming a satellite signal is affected by deception interference, the received signal model can be represented as a superposition of the real signal and the deception signal, i.e.:

[0011] s(t)=s au (t)+s sp (t) (1)

[0012] In the formula, t represents time, s au (t) and s sp (t) represent the real signal and the spoofed signal from a single satellite, respectively. The intermediate frequency signal received by the receiver includes the spoofed code, data code, and carrier wave, which can be specifically described as:

[0013] s sp (t)=A sp C(t+τ sp )D(t+τ sp )cos(2π(f IF +f d,sp (t+τ) sp )+φ sp (2)

[0014] s au (t)=A auC(t+τ au )D(t+τ au )cos(2π(f IF +f d,au (t+τ) au )+φ au (3)

[0015] In the formula, A au A is the signal amplitude of the actual signal. sp The signal amplitude of the deception signal; C is the ranging code, which uses GPS L1 C / A code in this paper; D is the data code, i.e., the navigation message; τ au For the actual signal delay, τ sp Delay for deception signal; f IF f is the center frequency of the carrier. d,au f is the Doppler frequency of the actual signal. d,sp The Doppler frequency of the deception signal; φ au The initial carrier phase of the real signal, φ sp The initial carrier phase of the deception signal is π, where π is the mathematical constant pi.

[0016] For intermediate frequency (IF) signals, the receiver internally performs coherent integration and low-pass filtering with the locally replicated ranging code through the co-directional branch (I) and quadrature branch (Q). At this point, the receiver's instantaneous branch output I... P The expression is:

[0017]

[0018] In the formula, i P (t) represents the mixing result of the same-direction branch, R. au (τ) represents the value of the autocorrelation function of the true signal, R sp (τ) represents the value of the autocorrelation function of the deception signal; This represents the difference between the actual signal carrier frequency and the locally replicated signal carrier frequency. This represents the difference between the carrier frequency of the deception signal and the carrier frequency of the locally replicated signal; This represents the difference in carrier phase between the real signal and the locally replicated signal. T represents the difference in carrier phase between the spoofing signal and the locally replicated signal; coh t1 represents the coherent integration time, which is typically 1 ms for C / A codes; t1 represents the integration start time.

[0019] Similarly, the coherent integral value Q of the Q-path P Represented as:

[0020]

[0021] In the formula, q P(t) represents the mixing result of the orthogonal branch instantaneous path.

[0022] At this time, the output P of the correlator in the instantaneous branch is:

[0023]

[0024] In the formula, V au V is the sum of the true signal amplitudes, and V is the product of the true signal amplitude, the autocorrelation function, and the attenuation due to frequency error, i.e., the coefficient before the first term cosine function in the formula. sp It is the sum of the amplitudes of the deception signals, that is, the coefficient before the second term of the cos function in equation (6); The sum of the phases of the real signal and the deceptive signal is represented by the values ​​in parentheses of the first and second terms of the equation, respectively; f △ θ △ These are the differences in Doppler frequency and initial phase between the real signal and the deceptive signal, respectively:

[0025]

[0026] Among them, the difference in Doppler frequencies f △ The traction deception rate is caused by the Doppler frequency effect, and the traction rate v can be obtained from this. pull The resulting difference in Doppler frequencies is:

[0027]

[0028] In the formula, f L1 Let c be the L1 carrier frequency and c be the speed of light.

[0029] When a spoofing signal is present, due to its power advantage, the receiver begins to track the spoofing signal, and the local carrier frequency is adjusted to match the carrier frequency of the spoofing signal. At this time, the correlator output is a frequency of f. △ The periodic signal. The theoretical derivation neglects the presence of noise. In practice, due to factors such as temperature, mechanical vibration, and crystal oscillator effects, the receiver exhibits various types of noise. It can be assumed that the noise term η output by the correlator has a mean of zero and a variance of σ. 2 It follows a normal distribution.

[0030] Therefore, when the receiver is spoofed, the correlator output is a periodic signal with white noise, and when the receiver is not spoofed, the correlator output is a constant signal with white noise.

[0031] In a simulated receiver subjected to spoofing, no spoofing signal is present for the first 10 seconds. Then, at the 10-second mark, a spoofing signal with a 6dB power advantage and a 1m / s pull speed is directly introduced. For example... Figure 2As shown, after adding the deception signal, the correlator output has a clear periodic signal, which is consistent with the analysis results of Equation (6).

[0032] like Figure 1 As shown, a deception interference detection method based on correlation value features includes the following steps:

[0033] Step (1): A sliding window is used to select relevant values. For GPS L1 C / A signals, the receiver coherent integration time is generally set to 1ms, so the output frequency of the coherent integration data is 1000Hz. When the accumulated data reaches the length of the window, window data processing begins. The sliding distance is equal to the window length, and there is no data overlap between adjacent windows. Since it is necessary to determine whether there is a periodic signal through frequency domain analysis later, it is necessary to ensure that the window period is greater than the period of the periodic signal. Let the window period be T. w The traction speed v is obtained from the Doppler frequency effect. pull The resulting difference in Doppler frequencies is expressed as:

[0034]

[0035] In the formula, f △ f is the difference between the Doppler frequencies and the initial phase of the real signal. L1 Where L1 is the carrier frequency, and c is the speed of light;

[0036] According to equation (8), the traction speed v pull The following conditions must be met:

[0037]

[0038] When the sliding window length is set to 1000, that is, the window period is 1s, the minimum traction rate that can be detected at this time is 0.19m / s according to equation (9);

[0039] Step (2) Perform time-domain processing of sliding window data: Since the influence of DC component on the spectrum needs to be avoided during spectrum analysis, detrending processing is required in the time domain; perform second-order polynomial fitting based on least squares method, subtract the fitting result from the original signal, remove the DC component, and retain the noise;

[0040] Step (3), Frequency Domain Analysis: The amplitude of the signal at each frequency spectrum is obtained through Fast Fourier Transform (FFT). Assuming that a sequence x(n) of length M is transformed into a sequence X(k) by FFT, the number of FFT transformation points is the same as the number of points in the time domain sequence, denoted as N. The frequency resolution Δf is:

[0041]

[0042] In the formula, Fs is the sampling frequency in Hz; the amplitude corresponding to the k-th frequency point is:

[0043]

[0044] Step (4), Peak detection: Through sufficient statistical analysis of Gaussian white noise, it is determined that the maximum peak-to-average power ratio (PAR) of Gaussian white noise is 12 dB with a 99% probability; calculate the mean μ of the frequency domain amplitude, and set the threshold Th as the mean multiplied by the PAR:

[0045] Th=PAR*μ (12)

[0046] When the peak value of the frequency domain amplitude is greater than the threshold value, the receiver is considered to be subjected to uniform traction deception interference.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) This invention discovered the influence of the relative Doppler frequency between the deception signal and the real signal on the receiver tracking stage through theoretical analysis. Theoretical derivation and simulation verification showed that the receiver correlation value would exhibit periodic changes when the relative Doppler frequency exists.

[0049] (2) This invention proposes a new deception detection method, which detects uniform traction interference deception by detecting periodic signals from correlation values. It can still maintain good detection performance for traction deception with small traction rates. When the window period is set to 1s, theory and simulation show that the minimum traction rate it can detect is 0.2m / s.

[0050] (3) This invention does not require any changes to the existing structure of the receiver, nor does it require any additional hardware assistance. It is easy to implement on commercial receivers and has certain engineering application value. Attached Figure Description

[0051] Figure 1 This is a diagram of the algorithm structure of the present invention;

[0052] Figure 2 This is the output diagram of the correlator under simulated traction deception in this invention;

[0053] Figure 3 This is the time-frequency domain diagram of the correlator output within a single window in this invention, wherein: Figure 3 (a) No deception, Figure 3 (b) Deception exists;

[0054] Figure 4 This is the output detection diagram of the correlator in this invention;

[0055] Figure 5 This is a diagram showing the detection results at the critical point of the deceptive traction rate in this invention, wherein: Figure 5(a) Traction speed 0.1 m / s, Figure 5 (b) Traction speed 0.2 m / s;

[0056] Figure 6 The TEXBAT detection result image shows: Figure 6 (a) Scenario 2 Static Deception Figure 6 (b) Scenario 5 Dynamic Deception. Detailed Implementation

[0057] The present invention will be further described below with reference to embodiments.

[0058] Simulation Experiment

[0059] Algorithm testing was conducted by generating various simulated deception scenarios using a deception interference software simulation platform.

[0060] When deception does not exist, then as follows: Figure 3 As shown in (a), the time domain is noise, and the peak amplitude in the frequency domain is less than the threshold value. The receiver does not alarm when a spoofing signal is present. Figure 3 As shown in (b), the time domain presents a periodic signal, and the peak value of the frequency domain amplitude exceeds the threshold value by a large margin. At this time, the receiver alarms and successfully detects the traction spoofing interference.

[0061] Figure 4 This is an overall detection diagram for a simulated deception scenario with a power advantage of 6dB and a traction speed of 5m / s. Plotting and alarm analysis were performed for 10 seconds before and after the deception was introduced. It can be seen that the algorithm's recognition results were accurate during this period, with no false alarms or missed detections. The deception signal was introduced at 10 seconds, and an alarm was triggered at 11 seconds. The algorithm's detection time corresponds to the time of one window of data; the selected window length for this data set was 1000, corresponding to a 1-second detection time.

[0062] Figure 5 A deception scenario was simulated that could detect the critical value of the deception traction rate, with a power advantage of 6dB and traction rates of 0.1m / s and 0.2m / s, respectively.

[0063] Depend on Figure 5 It can be seen that when the window period is 1s, the lowest detectable traction rate is 0.2m / s, which is consistent with the theoretical calculation of equation (9).

[0064] Actual test experiment

[0065] TEXBAT (Texas Spoofing Test Battery) is a high-fidelity GPS L1 C / A code spoofing dataset collected by a team at the University of Texas at Austin. This publicly available dataset contains various real-world spoofing scenarios. This invention selects two traction-type spoofing scenarios from the data for algorithm verification. The specific scenarios are shown in Table 1:

[0066] Table 1: TEXBAT Scenario Description

[0067]

[0068] With a peak-to-average power ratio (PAPR) of 14 dB (meaning the ratio of peak to mean amplitude is 25.1), the peak threshold is 25.1 μ. The sliding window length is 1000. The detection results are as follows: Figure 6 As shown, in scenario 2, traction begins at 135s, by... Figure 6 (a) It can be seen that the deception detection method proposed in this invention has a very good detection effect on traction-type deception. For the dynamic process of scenario 5, the deception traction process lasts 145–205 seconds, caused by… Figure 6 (b) It can be seen that the deception detection method can always trigger an alarm during the traction process.

[0069] The false alarm rate and false alarm rate obtained from the deception-induced process are shown in Table 2.

[0070] Table 2. Results of TEXBAT Data Spoofing Detection

[0071]

[0072] The comprehensive test results show that the detection algorithm proposed in this paper can effectively detect the traction process of deceptive signals. When the window length is set to 1000 (1s), the false negative rate of both simulation and measured data is extremely low, and the detection of the deceptive traction process is well achieved.

[0073] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A deception interference detection method based on correlation value features, characterized in that, Includes the following steps: Step (1): Select relevant values ​​using a sliding window. For GPS L1 C / A signals, start window data processing when the accumulated data reaches the window length. The sliding distance is equal to the window length, and there is no data overlap between two adjacent windows. Since it is necessary to determine whether there is a periodic signal through frequency domain analysis later, it is necessary to ensure that the window period is greater than the period of the periodic signal. The traction speed v is obtained from the Doppler frequency effect. pull The resulting Doppler frequency difference f △ Represented as: In the formula, f L1 Where L1 is the carrier frequency, and c is the speed of light; Let the window period be T. w According to equation (8), the traction speed v pull The following conditions must be met: Step (2) Perform time-domain processing of sliding window data: Since the influence of DC component on the spectrum needs to be avoided during spectrum analysis, detrending processing is required in the time domain; perform second-order polynomial fitting based on least squares method, subtract the fitting result from the original signal, remove the DC component, and retain the noise; Step (3), Frequency Domain Analysis: The amplitude of the signal at each frequency spectrum is obtained through Fast Fourier Transform (FFT). Assuming that a sequence x(n) of length M is transformed into a sequence X(k) by FFT, the number of FFT transformation points is the same as the number of points in the time domain sequence, denoted as N. The frequency resolution Δf is: In the formula, Fs is the sampling frequency in Hz; the amplitude corresponding to the k-th frequency point is: Step (4), Peak detection: Through sufficient statistical analysis of Gaussian white noise, it is determined that the maximum peak-to-average power ratio (PAR) of Gaussian white noise is 12 dB with a 99% probability; calculate the mean μ of the frequency domain amplitude, and set the threshold Th as the mean multiplied by the PAR: Th=PAR*μ (12) When the peak value of the frequency domain amplitude is greater than the threshold value, the receiver is considered to be subjected to uniform traction deception interference.

2. The deception interference detection method based on correlation value features according to claim 1, characterized in that, In step (1), the coherent integration time of the receiver is set to 1ms, and the output frequency of the coherent integration data is 1000Hz.

3. The deception interference detection method based on correlation value features according to claim 1, characterized in that, In step (1), when the sliding window length is set to 1000, that is, the window period is 1s, the minimum traction rate that can be detected according to equation (9) is 0.19m / s.

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