Fractional Fourier transform-based deception signal detection method and computer device

Abstract

The invention relates to the field of deception signal detection, in particular to a deception signal detection method based on fractional Fourier transform and a computer device, and improves the accuracy and reliability of GNSS deception signal detection. The method comprises the following steps: acquiring an intermediate-frequency signal sample; multiplying the obtained intermediate frequency signal sample by a locally generated carrier and a pseudo code, and stripping the carrier and the pseudo code to obtain a related output result; performing Ncoh sampling point accumulation on the correlation output result to obtain a correlation integral result; processing the correlation integral result by using fractional Fourier transform to obtain output results of a correlation domain and a fractional Fourier transform domain; and judging the deception signal according to the output results of the correlation domain and the fractional Fourier transform domain. The method is suitable for GNSS deception signal detection.

Description

Technical Field

[0001] The present invention relates to the field of deception signal detection, and in particular to a deception signal detection method and a computer device based on fractional-order Fourier transform. Background Art

[0002] GNSS (Global Navigation Satellite System) plays an important role in our daily lives. However, the open GNSS civilian signal structure and extremely low ground receiving power make it easy to be forged and vulnerable to spoofing. GNSS spoofing attacks are covert, flexible in strategy, easy to deploy and low in cost, posing a serious threat to GNSS safety-critical applications. As the first step in anti-spoofing, spoofing detection usually determines whether there is spoofing in the current environment by identifying direct or indirect subtle differences between spoofed signals and real signals. According to different detection principles, existing spoofing detection technologies can be divided into the following categories: spoofing detection methods based on navigation information encryption technology, spoofing detection methods based on spatial information processing technology, spoofing detection methods based on navigation result post-processing technology, and spoofing detection methods based on GNSS receiver baseband signal processing technology.

[0003] Deception detection methods based on navigation information encryption require modifications to the signal system at the national level, which is difficult to implement and difficult to achieve in the short term. Detection methods based on spatial information processing technology require processing data from different epochs, which is complex and requires additional hardware resources. Methods based on post-processing of navigation results also require additional hardware resources, and the detection effect is limited by the accuracy of motion sensors. Detection methods based on GNSS receiver baseband signal processing are relatively simple to implement, but the application scenarios of a single detection method are limited, and multiple methods are required for joint detection.

[0004] The most common detection method currently uses an RF front-end monitor with an automatic gain control mechanism to monitor the sudden jump in signal energy when GNSS spoofing occurs to determine the presence of spoofing signals, but this method only works when the spoofing attack is initiated. The correlation domain detection method that monitors the relevant output distortion also has similar limitations. If the spoofing attack is initiated before the GNSS receiver is powered on, the occurrence of coherent peak distortion will not be detected, and this method will fail. The correlation domain multi-peak detection algorithm based on the capture results does not have the ability to detect forwarding and high-end generation spoofing interference with low interference-to-signal ratio, small delay, and Doppler frequency difference.

[0005] In summary, current deception detection technologies have problems such as difficulty in implementation, high cost of use, need for additional hardware resources, poor universality, weak generalization ability, and limited window of action. In addition, with the diversification of deception attack forms and strategies, the existing deception detection technologies are not ideal when dealing with mid- and high-level deception strategies with good concealment. Therefore, how to ensure the accuracy, reliability, and scenario adaptability of deception detection methods is an important challenge currently faced. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a spoofing signal detection method and a computer device based on fractional Fourier transform, thereby improving the accuracy and reliability of GNSS spoofing signal detection.

[0007] The present invention adopts the following technical solutions to achieve the above-mentioned purpose. In a first aspect, the present invention provides a fraud signal detection method based on fractional Fourier transform, comprising:

[0008] S1, obtaining intermediate frequency signal samples;

[0009] S2, multiplying the obtained intermediate frequency signal sample with the locally generated carrier and pseudo code, and obtaining the relevant output result after stripping the carrier and pseudo code;

[0010] S3, perform N on the relevant output results coh Accumulate the sampling points to obtain the relevant integral results;

[0011] S4, using fractional Fourier transform to process the correlation integral result, and obtain output results in correlation domain and fractional Fourier transform domain;

[0012] S5. judging the deceptive signal according to the output results of the correlation domain and the fractional Fourier transform domain;

[0013] Peak value extraction is performed from the correlation domain result. If the peak energy exceeds the set interference threshold, it is directly determined that there is a deception signal. If it does not exceed the interference threshold, it is determined whether the peak energy exceeds the capture threshold. If it does not exceed the capture threshold, it indicates that the satellite signal corresponding to the PRN code does not exist and the detection is completed. If the peak value exceeds the capture threshold, the number of related peaks is determined. If there are two related peaks, it is determined that a deception signal exists. If only one related peak exists, the number of peaks in the fractional Fourier transform domain is determined. If there is only one peak in the fractional Fourier transform domain, it is determined that there is no deception signal. If there are two peaks, it is determined that there is deception interference and the detection is completed.

[0014] Furthermore, step S1 specifically includes:

[0015] In the spoofing scenario, the signal y received by the receiver antenna total (t) is:

[0016] y total (t) = y(t) + y s (t)+ν(t), ν(t) represents the received noise, and y(t) represents the real signal;

[0017] N is the number of satellites, A i is the true signal amplitude of the i-th satellite, D i (t) is the navigation message bit stream of the satellite signal, C i (t) is the corresponding spreading code sequence, τ i (t) is the code phase corresponding to the real signal of the i-th satellite, φ i (t) is the carrier phase corresponding to the real signal, ω c is the nominal carrier angular frequency;

[0018] y S (t) indicates a deceptive signal;

[0019] N s =N, that is, the number of satellite signals contained in the spoofed signal is equal to the corresponding number of satellite signals in the real signal, and each spoofed signal has the same spreading code C as the real signal i (t) and the estimated navigation message bit stream A si is the amplitude of the spoofing signal from the i-th satellite, τ si (t) is the code phase corresponding to the spoofing signal of the i-th satellite, φsi(t) is the carrier phase corresponding to the spoofing signal;

[0020] Simplifying the signal received by the receiver antenna and extracting the frequency change from the phase change, we get:

[0021]

[0022] In the formula, f c is the nominal frequency, f d is the Doppler frequency;

[0023] The signal from which the frequency variation is extracted is down-converted and then n The AD sampling is performed for the period, and the intermediate frequency signal samples are obtained:

[0024]

[0025] In the formula, A IF is the intermediate frequency signal sample amplitude, f IF is the frequency of the intermediate frequency signal sample, ν IF (it n) is the noise component in the intermediate frequency signal sample.

[0026] Furthermore, the relevant output results are:

[0027]

[0028] In the formula, f and f s Represent the real signal frequency difference and the deceptive signal frequency difference respectively, and They represent the real signal difference and the spoof signal difference respectively.

[0029] Furthermore, the relevant integral result is:

[0030]

[0031] Where m represents the mth correlation integral result, T coh is the coherent integration time, v coh [m] is the noise term after integration,

[0032] Furthermore, step S4 specifically includes:

[0033] The fractional Fourier transform is: K p (t,u) represents the transformation kernel of fractional Fourier transform;

[0034]

[0035] p represents the order of the fractional Fourier transform, which is an arbitrary real number and

[0036] The fractional Fourier transform result is:

[0037]

[0038] in

[0039] In a second aspect, the present invention provides a computer device, including a memory, wherein the memory stores program instructions, and when the program instructions are executed, the above-mentioned spoofing signal detection method based on fractional Fourier transform is executed.

[0040] The beneficial effects of the present invention are:

[0041] The present invention abandons the reliance on signal system changes such as navigation information encryption, and cuts in from the perspective of GNSS baseband signal processing, and can achieve effective deception detection under the existing signal structure. This design greatly reduces the difficulty and time cost of technical implementation, making the method easier to deploy and apply in existing GNSS systems.

[0042] The present invention optimizes the baseband signal processing algorithm of the GNSS receiver and avoids the reliance on additional hardware resources. Compared with the existing complex spatial information processing and navigation result post-processing methods, the present invention is simpler to implement, reduces power consumption and usage costs, has a wide range of application potential, and is particularly suitable for single-antenna receiver application scenarios with limited power consumption and resources.

[0043] By introducing fractional Fourier transform, the present invention significantly enhances the feature extraction and analysis capabilities of spoofing signals. It can not only detect the spoofing signal features hidden in traditional time domain and correlation domain signals, but also more accurately identify the mid-to-high-end hidden spoofing signal sources through the extended features of the time-frequency domain, thereby greatly improving the robustness and accuracy of detection.

[0044] The method of the present invention can extract the dynamic characteristics of the signal and generate a time-frequency matrix or image, effectively expanding the application scenarios and time windows of the detection. Regardless of whether the spoofing attack occurs before or after the receiver is powered on, the present invention can maintain effective detection capabilities and ensure accuracy and reliability at different attack times and scenarios.

[0045] The method proposed in the present invention has strong adaptability and can be applied to various complex and diverse deception attack strategies. It not only enhances the supplement to existing detection technologies, but also has broader application prospects and can provide more efficient and reliable protection solutions for GNSS safety-critical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a conceptual diagram of GNSS spoofing detection provided by an embodiment of the present invention;

[0047] Figure 2 is a GNSS spoofing detection signal processing block diagram provided by an embodiment of the present invention;

[0048] Figure 3 is a GNSS spoofing detection signal processing flow chart provided by an embodiment of the present invention;

[0049] Figure 4 It is a flow chart of a deception interference detection strategy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0051] The deception scenario and the basic deception detection concept proposed by the present invention are shown as follows: Figure 1 As shown in the figure, in a typical spoofing attack scenario, the real GNSS signal and the spoofing attack signal exist at the same time, so the spoofing identification can be performed by discovering the subtle relationship between them. For the target receiver, it is very important to obtain the ability to distinguish between the real signal and the spoofing signal. As shown in the figure, the spoofing signal source first receives the real GNSS signal and parses it, and generates a spoofing signal based on the satellite parameters at this time. For the victim receiver, the dynamics of the real signal it receives from the GNSS satellite is determined by the relative motion between the receiver and the satellite. On the other hand, the dynamics of the signal it receives from the spoofing signal source includes two parts: the relative motion between the spoofing signal source and the satellite and the relative motion between the spoofing signal source and the victim receiver. In daily civilian scenarios, the second part of the dynamics is unknown to the spoofing attacker. In addition, the spoofing signal source usually needs to induce the receiver to a false position or induce it to perform unreasonable maneuvers, which will introduce additional dynamics in the spoofing signal. Therefore, we can use this feature to extract the dynamic characteristics of the GNSS signal by the fractional Fourier transform method, distinguish the difference in dynamic characteristics between the real signal and the spoofing signal to complete the spoofing signal detection.

[0052] The GNSS spoofing detection signal processing block diagram based on fractional Fourier transform is as follows: Figure 2 As shown in the figure, the phase detector and loop filter in the traditional GNSS receiver are replaced by FrFT (Fractional Fourier Transform) and the corresponding parameter estimation method. The baseband processing algorithm based on fractional Fourier transform extends the traditional two-dimensional correlation domain method to three dimensions, and obtains the output results of the correlation domain and FrFT domain at the same time. Compared with the correlation domain, the FrFT domain result has better resolution and contains potential signal dynamic information. It has excellent resolution for spoofing signals with low signal-to-noise ratio, small delay and Doppler frequency difference. The results of the two domains are then sent to the spoofing detection module for decision-making. At the same time, the original observation information output by the baseband signal processing module can also be used as a detection feature for auxiliary judgment.

[0053] The fraud signal detection method based on fractional Fourier transform provided by the present invention is as follows: Figure 3 As shown, including:

[0054] S1, obtaining intermediate frequency signal samples;

[0055] In the spoofing scenario, the signal y received by the receiver antenna total (t) is:

[0056] y total (t) = y(t) + y s (t)+ν(t), ν(t) represents the received noise, and y(t) represents the real signal;

[0057] N is the number of satellites, A i is the true signal amplitude of the i-th satellite, D i (t) is the navigation message bit stream of the satellite signal, C i (t) is the corresponding spreading code sequence, τ i (t) is the code phase corresponding to the real signal of the i-th satellite, φ i (t) is the carrier phase corresponding to the real signal, ω c is the nominal carrier angular frequency;

[0058] y S (t) indicates a deceptive signal;

[0059] N s =N, that is, the number of satellite signals contained in the spoofed signal is equal to the corresponding number of satellite signals in the real signal, and each spoofed signal has the same spreading code C as the real signal i (t) and the estimated navigation message bit stream A si is the amplitude of the spoofing signal from the i-th satellite, τ si (t) is the code phase corresponding to the spoofing signal of the i-th satellite, φsi(t) is the carrier phase corresponding to the spoofing signal;

[0060] Simplifying the signal received by the receiver antenna and extracting the frequency change from the phase change, we get:

[0061]

[0062] In the formula, f c is the nominal frequency, f d is the Doppler frequency;

[0063] The signal from which the frequency variation is extracted is down-converted and then n The AD sampling is performed for the period, and the intermediate frequency signal samples are obtained:

[0064]

[0065] In the formula, A IF is the intermediate frequency signal sample amplitude, f IF is the frequency of the intermediate frequency signal sample, νIF (it n ) is the noise component in the intermediate frequency signal sample.

[0066] S2, multiplying the obtained intermediate frequency signal sample with the locally generated carrier and pseudo code, and obtaining the relevant output result after stripping the carrier and pseudo code;

[0067] The relevant output is:

[0068]

[0069] In the formula, f and f s Represent the real signal frequency difference and the deceptive signal frequency difference respectively, and They represent the real signal difference and the spoof signal difference respectively.

[0070] S3, perform N on the relevant output results coh Accumulate the sampling points to obtain the relevant integral results;

[0071] The relevant integral results are:

[0072]

[0073] Where m represents the mth correlation integral result, T coh is the coherent integration time, v coh [m] is the noise term after integration,

[0074] S4, using fractional Fourier transform to process the correlation integral result, and obtain output results in correlation domain and fractional Fourier transform domain;

[0075] The fractional Fourier transform is: K p (t,u) represents the transformation kernel of fractional Fourier transform;

[0076]

[0077] p represents the order of the fractional Fourier transform, which is an arbitrary real number and

[0078] The fractional Fourier transform result is:

[0079]

[0080] in

[0081] S5. judging the deceptive signal according to the output results of the correlation domain and the fractional Fourier transform domain;

[0082] The judgment process is as follows Figure 4 As shown, firstly, the peak value is extracted from the correlation domain result. If the peak energy exceeds the set interference threshold, it can be directly determined that there is a spoofing signal. If it does not exceed the interference threshold, it is determined again whether the peak energy exceeds the capture threshold. If it does not exceed the capture threshold, it indicates that the satellite signal corresponding to the PRN code does not exist and the detection ends. If the peak value exceeds the capture threshold, the number of related peaks is further determined. If there are two related peaks, it is determined that a spoofing signal exists. If only one related peak exists, the number of peaks in the FrFT domain is further determined. If there is only one peak in the FrFT domain, the possibility of a spoofing signal can be ruled out. If there are two peaks, it is determined that there is spoofing interference and the detection ends.

[0083] The present invention also provides a fraud signal detection system, which mainly consists of the following parts:

[0084] GNSS receiver RF front-end module: used to receive GNSS satellite signals, including antenna, low-noise amplifier, filter and automatic gain control unit, etc.

[0085] Baseband signal processing module: receives and processes signals from the front-end module. This module integrates a fractional Fourier transform algorithm for time-frequency domain signal feature extraction and can also realize traditional signal capture and tracking functions. In the baseband signal processing process, the present invention uses fractional Fourier transform to expand the feature space of traditional methods. Fractional Fourier transform is a mathematical transformation tool that can provide a continuous transition between the time domain and the frequency domain. By adjusting the transform order, richer signal features can be extracted in different transform domains.

[0086] Spoofing signal detection module: Analyzes the correlation and time-frequency feature outputs extracted by the baseband signal processing module to determine whether the signal is abnormal, and then detects the presence of spoofing signals.

[0087] The present invention starts from the perspective of GNSS baseband signal processing, does not rely on changes to the signal system, and can achieve effective deception detection under the existing signal structure, thereby reducing the difficulty and time expectation of technical implementation.

[0088] Secondly, the existing deception detection methods based on spatial information processing and navigation result post-processing are complex in calculation and require additional hardware resources. In addition, the detection effect of navigation result post-processing methods is limited by the accuracy of motion sensors, and the power consumption and use cost are high, which is not suitable for large-scale popularization and difficult to be widely used. The present invention starts from the inside of the GNSS receiver and modifies the GNSS baseband signal processing algorithm. It does not require additional hardware resources, is low-cost, easy to implement, and has a wide range of application scenarios.

[0089] Finally, the existing time domain and correlation domain detection methods based on GNSS receiver baseband signal processing rely on limited parameter types. They perform well when facing low-end GNSS spoofing signal sources, but their effectiveness is greatly reduced when dealing with mid- and high-end signal sources with more hidden attacks. At the same time, the existing methods also have problems such as limited action windows and application scenarios. The present invention innovatively introduces fractional Fourier transform into the GNSS receiver baseband signal processing algorithm, improves and enhances the performance of spoofing signal feature analysis and extraction, and expands the signal feature parameters in the time and frequency domains while retaining the signal features in the time domain and correlation domains.

[0090] Moreover, the result of fractional Fourier transform can not only extract the parameter of Doppler frequency change rate in GNSS signal, but also because of the existence of Doppler frequency change rate, the signal energy will be concentrated in a certain rotation angle of FrFT, so it has better Doppler frequency resolution in FrFT domain. These characteristics make the spoofing signal and the real signal show more significant difference in FrFT domain, thus improving the sensitivity of spoofing signal detection.

[0091] In addition, the method proposed in the present invention extracts the signal dynamic characteristic information from the received signal and generates the corresponding correlation domain matrix and time-frequency domain matrix respectively, which are sent to the spoofing signal detection module in the form of digital matrix or image, and the peak search or target detection method is used to determine whether there is a spoofing signal based on the two results. The introduction of new features greatly expands the application scenarios and action windows of traditional methods, ensuring accurate and reliable spoofing signal detection at different attack launch time points.

[0092] In summary, the present invention significantly improves the accuracy, reliability and adaptability of spoofing signal detection while reducing the difficulty and cost of implementation, and provides a new technical solution for the security application of GNSS.

[0093] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A fraudulent signal detection method based on fractional Fourier transform, characterized in that: include: S1, obtaining intermediate frequency signal samples; S2, multiplying the obtained intermediate frequency signal sample with the locally generated carrier and pseudo code, and obtaining the relevant output result after stripping the carrier and pseudo code; S3, perform N on the relevant output results coh Accumulate the sampling points to obtain the relevant integral results; S4, using fractional Fourier transform to process the correlation integral result, and obtain output results in correlation domain and fractional Fourier transform domain; S5. judging the deceptive signal according to the output results of the correlation domain and the fractional Fourier transform domain; Peak value extraction is performed from the correlation domain result. If the peak energy exceeds the set interference threshold, it is directly determined that there is a deception signal. If it does not exceed the interference threshold, it is determined whether the peak energy exceeds the capture threshold. If it does not exceed the capture threshold, it indicates that the satellite signal corresponding to the PRN code does not exist and the detection is completed. If the peak value exceeds the capture threshold, the number of related peaks is determined. If there are two related peaks, it is determined that a deception signal exists. If only one related peak exists, the number of peaks in the fractional Fourier transform domain is determined. If there is only one peak in the fractional Fourier transform domain, it is determined that there is no deception signal. If there are two peaks, it is determined that there is deception interference and the detection is completed.

2. The method for detecting spoofing signals based on fractional Fourier transform according to claim 1, characterized in that: Step S1 specifically includes: In the spoofing scenario, the signal y received by the receiver antenna total (t) is: y total (t) = y(t) + y s (t)+ν(t), ν(t) represents the received noise, and y(t) represents the real signal; N is the number of satellites, A i is the true signal amplitude of the i-th satellite, D i (t) is the navigation message bit stream of the satellite signal, C i (t) is the corresponding spreading code sequence, τ i (t) is the code phase corresponding to the real signal of the i-th satellite, φ i (t) is the carrier phase corresponding to the real signal, ω c is the nominal carrier angular frequency; y S (t) indicates a deceptive signal; N s =N, that is, the number of satellite signals contained in the spoofed signal is equal to the corresponding number of satellite signals in the real signal, and each spoofed signal has the same spreading code C as the real signal i (t) and the estimated navigation message bit stream A si is the amplitude of the spoofing signal from the i-th satellite, τ si (t) is the code phase corresponding to the spoofing signal of the i-th satellite, φsi(t) is the carrier phase corresponding to the spoofing signal; Simplifying the signal received by the receiver antenna and extracting the frequency change from the phase change, we get: In the formula, f c is the nominal frequency, f d is the Doppler frequency; The signal from which the frequency variation is extracted is down-converted and then n The AD sampling is performed for the period, and the intermediate frequency signal samples are obtained: In the formula, A IF is the intermediate frequency signal sample amplitude, f IF is the frequency of the intermediate frequency signal sample, ν IF (it n ) is the noise component in the intermediate frequency signal sample.

3. The method for detecting spoofing signals based on fractional Fourier transform according to claim 2, characterized in that: The relevant output is: In the formula, f and f s Represent the real signal frequency difference and the deceptive signal frequency difference respectively, and They represent the real signal difference and the spoof signal difference respectively.

4. The method for detecting spoofing signals based on fractional Fourier transform according to claim 3, characterized in that: The relevant integral results are: Where m represents the mth correlation integral result, T coh is the coherent integration time, v coh [m] is the noise term after integration, 5. The method for detecting spoofing signals based on fractional Fourier transform according to claim 4, characterized in that: Step S4 specifically includes: The fractional Fourier transform is: K p (t,u) represents the transformation kernel of fractional Fourier transform; p represents the order of the fractional Fourier transform, which is an arbitrary real number and The fractional Fourier transform result is: in 6. A computer device comprising a memory, wherein the memory stores program instructions, characterized in that: When the program instructions are executed, the fraud signal detection method based on fractional Fourier transform as described in any one of claims 1 to 5 is executed.

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