GNSS interference detection method and device based on time-frequency domain statistical analysis, equipment and medium

Abstract

The invention relates to a GNSS interference detection method and device based on time-frequency domain statistical analysis, equipment and a medium, and the method comprises the steps: converting an obtained radio frequency signal into a baseband signal, carrying out the sampling and digital processing, obtaining a digital component signal, carrying out the processing of the digital component signal through short-time Fourier transform, and obtaining a GNSS interference detection result. The method comprises the steps of obtaining a spectrogram represented by time and frequency, judging whether a radio frequency signal has suppressing interference based on the spectrogram by utilizing a goodness-of-fit detection method and chi-square statistics, carrying out correlation integration on a digital component signal and a local code to obtain a correlation output signal, carrying out short-time Fourier transform on the correlation output signal, and outputting the processed radio frequency signal. Whether deception jamming exists in the radio frequency signals or not is judged according to the time width difference and the variance of the time-frequency spectrum energy along with the time, and GNSS jamming detection can be effectively achieved through the method.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation technology, and in particular to a GNSS interference detection method, device, equipment and medium based on time-frequency domain statistical analysis. Background Art

[0002] Satellite navigation systems, especially for GNSS, are susceptible to jamming due to low signal power. For example, low-power (10 dB) jammers radiated by low-cost jammers can block any GNSS signals within a 100 m radius around the jammer, resulting in loss of GNSS navigation. In general, jamming devices can take different shapes and sizes and are usually portable and mobile. Civilian mass-market jammers can be powered by a car cigarette lighter socket or a small battery, and their power consumption is relatively low, but they can interfere with GNSS signal reception tens of kilometers away.

[0003] Interference poses an increasingly serious threat to the GNSS system, the global satellite navigation system, so dealing with interference has become an extremely important task, and interference detection is an extremely important part of it. GNSS includes GPS, BDS, GLONASS satellite navigation system and Galileo satellite navigation system. Although the structures of different satellite navigation systems are different, they essentially achieve navigation and positioning functions by receiving signals from distant satellites. The signals transmitted by navigation satellites are very weak when they reach the receiver and are easily interfered with. Summary of the invention

[0004] Based on this, it is necessary to provide a GNSS interference detection method, device, equipment and medium based on time-frequency domain statistical analysis that can accurately detect both suppression interference and deception interference in response to the above technical problems.

[0005] A GNSS interference detection method based on time-frequency domain statistical analysis, the method comprising:

[0006] Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal;

[0007] Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0008] Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, suppressing the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0009] Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal;

[0010] After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time in turn, so as to realize the detection of suppression interference and deception interference.

[0011] In one embodiment, based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference includes:

[0012] Transform the suppression interference detection problem into a statistical hypothesis testing problem;

[0013] Obtaining a plurality of frequency slices based on the spectrogram, calculating a variance value of each of the frequency slices, and using the variance value as an expected value of the corresponding frequency slice;

[0014] Calculate the square of the deviation between the sample in each frequency slice and the corresponding expected value, weight it by the inverse of the corresponding expected value, add the calculation results of all samples, and obtain the test statistic;

[0015] A P value is obtained according to the test statistic, and whether there is suppression interference in the GNSS radio frequency signal is determined through the relationship between the P value and a predefined significance level.

[0016] In one embodiment, in the statistical hypothesis testing problem, the absence of suppression interference in the GNSS radio frequency signal is taken as the null hypothesis, and the presence of suppression interference in the GNSS radio frequency signal is taken as the alternative hypothesis;

[0017] When the P value is greater than or equal to the predefined significance level, the null hypothesis is accepted;

[0018] When the P value is less than the predefined significance level, the alternative hypothesis is accepted.

[0019] In one embodiment, when determining whether there is spoofing interference in the GNSS radio frequency signal:

[0020] According to the time width difference of the relevant output signal after short-time Fourier transform, it is judged whether there is deception interference with a delay greater than 0;

[0021] According to the time variance of the time-frequency spectrum energy of the relevant output signal after short-time Fourier transform, it is judged whether there is deceptive interference with a time delay equal to 0 or approximately 0.

[0022] In one embodiment, when determining whether there is spoofing interference in the GNSS radio frequency signal:

[0023] A judgment is made based on the time width difference of the relevant output signal after the short-time Fourier transform and a preset time width difference threshold, and if the time width difference is greater than the preset time width difference threshold, then there is deception interference with a delay greater than 0 in the GNSS radio frequency signal;

[0024] If the time width difference is less than or equal to the preset time width difference threshold, the variance of the time-frequency spectrum energy over time is calculated, and the variance is compared with the preset judgment threshold. If the variance of the time-frequency spectrum energy over time is greater than or equal to the judgment threshold, there is deception interference with a delay equal to 0 or approximately 0 in the GNSS radio frequency signal;

[0025] If the variance of the time-frequency spectrum energy over time is less than the judgment threshold, there is no deception interference in the GNSS radio frequency signal.

[0026] In one embodiment, it is determined whether there is a useful signal in the GNSS radio frequency signal according to the peak value of the correlation output signal and a preset decision threshold.

[0027] The present application also provides a GNSS interference detection device based on time-frequency domain statistical analysis, the device comprising:

[0028] A radio frequency signal processing module is used to obtain a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then perform sampling and digital processing to obtain a digital component signal;

[0029] A spectrum obtaining module, used for processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0030] A suppression interference signal detection module is used to determine whether there is suppression interference in the GNSS radio frequency signal based on the spectrum, using a goodness of fit detection method and a chi-square statistic, and if there is suppression interference, suppress the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0031] A correlation output signal obtaining module, used for performing correlation integration between the digitized component signal or the suppressed digitized component signal and a local code to obtain a correlation output signal;

[0032] The deception interference detection module is used to perform short-time Fourier transform on the relevant output signal, and then determine whether there is deception interference in the GNSS radio frequency signal through the time width difference and the variance of the time-frequency spectrum energy over time, so as to realize the detection of suppression interference and deception interference.

[0033] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal;

[0035] Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0036] Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, suppressing the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0037] Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal;

[0038] After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time in turn, so as to realize the detection of suppression interference and deception interference.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0040] Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal;

[0041] Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0042] Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, suppressing the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0043] Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal;

[0044] After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time in turn, so as to realize the detection of suppression interference and deception interference.

[0045] The above-mentioned GNSS interference detection method, device, equipment and medium based on time-frequency domain statistical analysis convert the acquired GNSS radio frequency signal into a baseband signal, and then sample and digitize it to obtain a digitized component signal, use short-time Fourier transform to process the digitized component signal to obtain a spectrum represented by time and frequency, and based on the spectrum, use the goodness of fit detection method and chi-square statistics to judge whether the GNSS radio frequency signal has suppression interference, if there is suppression interference, suppress the suppression interference on the digitized component signal to obtain a suppressed digitized component signal, correlate and integrate the digitized component signal or the suppressed digitized component signal with the local code to obtain a correlated output signal, perform short-time Fourier transform on the correlated output signal, and judge whether there is deception interference in the GNSS radio frequency signal by the time width difference and the variance of the time-frequency spectrum energy over time. The present method can effectively realize GNSS interference detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 1 is a flow chart of a GNSS interference detection method based on time-frequency domain statistical analysis in one embodiment;

[0047] Figure 2 A schematic diagram of a scenario involving GNSS interference in one embodiment;

[0048] Figure 3 A flowchart of the implementation steps of a GNSS interference detection method in one embodiment;

[0049] Figure 4 A schematic diagram of a signal spectrum and time-frequency analysis of continuous frequency modulation interference in one embodiment;

[0050] Figure 5 The output and time-frequency spectrum of a correlator in one embodiment under the conditions of no interference and interference;

[0051] Figure 6 is a structural block diagram of a GNSS interference detection device based on time-frequency domain statistical analysis in one embodiment;

[0052] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] In this application, if Figure 1As shown, a GNSS interference detection method based on time-frequency domain statistical analysis is provided, which specifically includes the following steps:

[0055] Step S100, acquiring a GNSS radio frequency signal, converting the GNSS radio frequency signal into a baseband signal, and then sampling and digitizing the signal to obtain a digital component signal.

[0056] Step S110, using short-time Fourier transform to process the digitized component signal to obtain a spectrum represented by time and frequency.

[0057] Step S120, based on the spectrum, using the goodness of fit detection method and the chi-square statistic, it is determined whether the GNSS radio frequency signal has suppression interference. If there is suppression interference, the digitized component signal is suppressed to obtain a suppressed digitized component signal.

[0058] Step S130, performing correlation integration on the digitized component signal or the suppressed digitized component signal and the local code to obtain a correlation output signal.

[0059] Step S140, after performing short-time Fourier transform on the relevant output signal, determine whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time, so as to detect suppression interference and deception interference.

[0060] Interference against satellite navigation systems can be divided into two categories: suppression interference and deception interference. Among them, suppression interference is a high-power signal from other communication systems or transmitting equipment, while deception interference refers to other devices forging pseudo-GNSS signals based on the structure of GNSS signals to mislead the target receiver, such as Figure 2 As shown. It is difficult to complete the detection of two types of interference by analyzing the time domain or frequency domain alone or the detection effect is poor. The method based on time-frequency domain analysis can parse the signal from the time domain and frequency domain dimensions at the same time, so as to complete the interference detection more intuitively. At the same time, no matter what type of interference it is, when there is interference, the signal with interference will be different from the original signal in statistical information such as mean, mean square and variance. Based on this, in this application, a method for interference detection of global navigation satellite system signals (GNSS) based on time-frequency (TF) domain statistical analysis is proposed.

[0061] Furthermore, the suppression and deception interference scenarios are explained, which are the application scenarios of this method. Suppression interference uses higher-power signals in the frequency band related to satellite signals, while the satellite signal reaching the ground receiving end is generally less than -160dBw, causing the GNSS signal to be submerged in noise and interference, affecting the receiver's ability to extract the carrier's position, speed, and time information from the signal. Unlike suppression interference, which directly affects the operation of the receiver from the perspective of signal power, deception signals use deception devices to generate signals with the same structure and similar power as the GNSS signal, deceiving and misleading the receiver into producing erroneous position and time information.

[0062] In step S100, after receiving the GNSS RF signal, the GNSS RF signal is mixed with two local oscillators to down-convert the RF signal into baseband signals of in-phase component and quadrature component, wherein the phase offset of these oscillators is 90°. Afterwards, the received signal is sampled and digitized by an analog-to-digital converter to obtain a digitized component signal for subsequent digital signal processing.

[0063] Generally, the digital signal processing part includes signal capture and signal tracking. In the signal capture stage, the digitized component signal is correlated with the local code. When the output value of the correlator is greater than the set decision threshold, the signal is considered to be successfully captured. Parameters such as code phase and carrier frequency in the correlation output are extracted as the initial values ​​of the tracking stage. In the tracking stage, the measurement values ​​such as pseudorange and carrier phase can be calculated, and the navigation message can be demodulated to complete the subsequent navigation positioning.

[0064] Furthermore, the digitized component signal is expressed as:

[0065] r[i]=s p [i]+w[i]+u[i] (1)

[0066] In formula (1), s p [i] represents the useful GNSS signal received, w[i] represents the complex Gaussian noise, where the mean μ = 0, and the variance of the real and imaginary parts are both σ 2 / 2, u[i] is the interference signal, including the suppression interference signal u s [i] and the deceptive jamming signal u d [i].

[0067] Compared with deceptive jamming, suppression jamming is easier to detect because its power is much greater than the power of the useful satellite navigation signal and the signal structure is more different. Deceptive jamming has the same structure as the real navigation signal, with similar power, and deceptive jamming is limited to the signal capture and tracking stage. Therefore, for the intermediate frequency digital signal of the GNSS receiver, suppression jamming is detected first, and deceptive jamming is detected in the subsequent related capture stage.

[0068] Since the signal is analyzed from two dimensions, time domain and frequency domain, in the time-frequency domain analysis, the relationship between the frequency of the signal and the time transformation can be clearly described, in step S110, the digitized component signal is processed by short-time Fourier transform to obtain a spectrogram represented by time and frequency.

[0069] The Short Time Fourier Transform (STFT) is one of the most commonly used methods in time-frequency analysis. This method adds a window function to the Fast Fourier Transform. Therefore, the STFT is also called the Windowed Fourier Transform. One assumption of the STFT is that the signal in each short window is stationary, which means that its characteristics, such as frequency content, remain unchanged during each window. For any input signal r(n), let h(mn) be the window function centered at time m (where m is the time variable). Sliding short windows on the time axis and Fourier transforming each window will produce the STFT. The standard discrete form of the STFT of r[n] is:

[0070]

[0071] In formula (2), h[m] is a window function of length L and unit power, m varies between -(L-1) / 2 and (L-1) / 2, and N is the total number of observed samples.

[0072] There is a trade-off between the time resolution and frequency resolution of the STFT. A larger window size results in better frequency resolution, but poorer time resolution. Conversely, a shorter window length results in finer time resolution, but coarser frequency resolution. Therefore, the window length L of the window function is an important parameter that affects the time-frequency analysis of the signal, and will directly affect the effect of subsequent interference detection.

[0073] In step S120, based on the spectrum, a goodness of fit detection method and a chi-square statistic are used to determine whether there is suppression interference in the GNSS radio frequency signal, including: converting the suppression interference detection problem into a statistical hypothesis testing problem, obtaining multiple frequency slices based on the spectrum, calculating the variance value of each frequency slice, and using the variance value as the expected value of the corresponding frequency slice, calculating the square of the deviation value between the sample in each frequency slice and the corresponding expected value, and weighting it by the inverse of the corresponding expected value, adding the calculation results of all samples to obtain a test statistic, and obtaining a P value based on the test statistic, and determining whether there is suppression interference in the GNSS radio frequency signal through the relationship between the P value and a predefined significance level.

[0074] Specifically, the P value represents probability in statistical hypothesis testing, and is the probability value used to determine whether interference exists, that is, hypothesis H0: interference definitely exists (P>=a), hypothesis H1: interference definitely does not exist (P<-a). By calculating and determining the P value, and then comparing the P value with the significance level a value of the test statistic, if P>=a, hypothesis H0 is established, otherwise hypothesis H1 is established.

[0075] In this embodiment, a goodness of fit test (GoF) method using chi-square statistics is used to determine whether the expected frequency is significantly different from the observed frequency.

[0076] GoF is often used to indicate whether a particular model of the sample distribution fits the observed data. Suppose the observed data R has V different cells, and the observed value of the vth cell is O v Assume that the expected theoretical value of the vth unit is T v , for the null hypothesis, the observed values ​​and the expected theoretical values ​​are close enough to each other; the alternative hypothesis indicates that the observed values ​​deviate significantly from the expected theoretical values. The GoF test statistic is a function of the square of the deviation of the observed numbers from their expected values, weighted by the inverse of their expected values, which can be expressed mathematically as:

[0077]

[0078] For large sample sizes N, the test statistic X follows a chi-square distribution with V-1 degrees of freedom under the null hypothesis. Therefore, the p-value of the test is calculated as:

[0079]

[0080] When the significance level α is given, the null hypothesis and alternative hypothesis about the p-value can be expressed as:

[0081] H0:α X(R) ≥α (5)

[0082] H1:α X(R) <α

[0083] If the test provides a p-value equal to or greater than the predefined significance level, then H0 is accepted; otherwise, then H1 is accepted.

[0084] In this embodiment, considering that in actual situations, the received useful GNSS signal will be submerged below the noise floor. Therefore, when there is no suppression interference, the received signal can be simplified to r[i]=w[i]; when there is suppression interference, r[i]=w[i]+u s [i].

[0085] In the absence of jamming suppression, due to linear summation, each sample r(n,k;w) in frequency slice k is a zero-mean complex Gaussian random variable, i.e., E[r(n,k;w)] = 0, and the variance of the STFT transform is:

[0086]

[0087] The modulus square of the signal after STFT transform, also known as the spectrogram, is defined as:

[0088] SP(n,k) = |r(n,k) 2 (7)

[0089] Therefore, the spectrogram samples approximately follow a chi-square distribution and can be detected using GoF. In this method, the interference detection is described as a statistical hypothesis testing problem, where the null hypothesis H0 and the alternative hypothesis H1 are respectively:

[0090] H0: r[i] = w[i] (8)

[0091] H1: r[i] = w[i] + u s [i]

[0092] That is, the null hypothesis H0 is that there is no jamming suppression, and the alternative hypothesis H1 is that there is jamming suppression.

[0093] In the GoF detection, the PDF (probability density function) of the observed samples is compared with a preset statistical model to achieve hypothesis testing.

[0094] In this embodiment, the theoretical chi-square distribution of the spectrogram samples is a continuous distribution with unknown degrees of freedom. To obtain the expected value of each cell, there are two methods to convert the continuous distribution into a discrete probability, where each cell is equivalent to a period of a periodic function, or equivalent to a profile in the time-frequency diagram of three-dimensional frequency slices (parallel to the TF plane formed by the time axis <T axis> and the frequency axis <F axis>), and there are infinitely many such profiles, or can be understood as a random experiment sample in a random process. The first method is to directly use the STFT transform of the interference-free signal. Through the averaging operation of a large number of independent tests, the expected number and boundaries of each cell are calculated for hypothesis testing. The second method is to use the mean and variance of the expected variance distribution, which can be calculated according to the noise power estimate value, as shown in formula (6). The noise power estimate value is a common observed value in global navigation satellite system receivers and is usually used to determine the threshold in various interference detection methods. And the noise power estimate can be used for any parameter setting in the detection (including the total sample size or window length). The method of calculating the expected PDF does not affect the performance of the TF domain method. Therefore, for a simple implementation, the method using the noise power estimate is used to obtain the expected value.

[0095] Therefore, when the spectrogram samples approximately follow the chi-square distribution. The GoF test is then applied to each frequency slice of the spectrogram result. All samples in each frequency slice are classified into predefined intervals based on their values ​​to obtain the observed PDF. The method for generating the expected PDF is the noise power estimation method. The test statistic is then calculated using these observed and expected PDFs based on formula (2). The detection problem of suppressed interference is solved by comparing the p-value of the GoF test with the predefined significance level.

[0096] Next, the deceptive interference is detected. Prior to this, the GNSS RF signal with suppressed interference is suppressed before subsequent detection.

[0097] In step S130, when judging whether there is deception interference in the GNSS radio frequency signal: judging whether there is deception interference with a delay greater than 0 based on the time width difference of the relevant output signal after the short-time Fourier transform, judging whether there is deception interference with a delay equal to 0 or approximately 0 based on the variance of the time-frequency spectrum energy of the relevant output signal after the short-time Fourier transform over time.

[0098] Furthermore, when judging whether there is spoofing interference in the GNSS radio frequency signal: judging based on the time width difference of the relevant output signal after the short-time Fourier transform and the preset time width difference threshold, if the time width difference is greater than the preset time width difference threshold, there is spoofing interference with a delay greater than 0 in the GNSS radio frequency signal. If the time width difference is less than or equal to the preset time width difference threshold, the variance of the time-frequency spectrum energy over time is calculated and judged with the preset judgment threshold. If the variance of the time-frequency spectrum energy over time is greater than or equal to the judgment threshold, there is spoofing interference with a delay equal to 0 or approximately 0 in the GNSS radio frequency signal. If the variance of the time-frequency spectrum energy over time is less than the judgment threshold, there is no spoofing interference in the GNSS radio frequency signal.

[0099] Specifically, after completing the suppression interference detection, the deception interference detection is performed in the relevant capture phase. Formula (8) is rewritten as:

[0100] H0:r[i]=s p [i] (9)

[0101] H1:r[i]=s p [i]+u d [i]

[0102] In formula (9), the useful navigation signal s p [i] can be expressed as:

[0103]

[0104] Similarly, the deceptive jamming signal has the same structure as the real navigation signal:

[0105]

[0106] In (10) and (11), C (.) represents the power of the real navigation signal or interference signal, c (.) represents the pseudo-random spreading code, d (.) is the navigation data code; f d is the Doppler shift of the signal, φ (.) is the true phase of the signal.

[0107] In the signal acquisition phase, the received signal is coherently integrated with the local code to obtain:

[0108] R(t,Δt)=R s (t,Δt)+R u (t,Δt) (12)

[0109] Among them, R s (t, Δt) represents the cross-correlation function between the useful navigation signal and the local code, R u (t, Δt) represents the cross-correlation function between the spoof signal and the local code, which can be expressed as:

[0110]

[0111] In formula (13), A (.) represents the peak value of the correlation peak between the real navigation signal, the spoofing signal and the local code; Δt represents the code phase difference between the local code and the navigation signal; T is the C / A code chip width of the satellite signal; δ is the delay of the spoofing signal relative to the real navigation signal, and satisfies 0≤δ<2T. Therefore, the correlator, that is, the GNSS RF signal is correlated with the local code to obtain the correlation output signal, and the outputs in the absence of interference and the presence of interference are respectively:

[0112]

[0113] Next, the relevant output signal is short-time Fourier transformed to convert the time domain signal into a two-dimensional function of time-frequency. When there is no deceptive interference, the time width in the time-frequency domain is 2T. When there is interference and δ is not close to 0, it can be clearly observed in the time-frequency domain that the time width τ of the energy distribution is greater than 2T. Therefore, let Δτ = τ-2T, and set an appropriate threshold Th τ When Δτ>Th τ When Δτ≤Th τ , conduct subsequent testing.

[0114] When δ=0 or close to 0, Δτ≤Th τ However, the deception interference still exists and needs to be continued for subsequent detection. After short-time Fourier transform, the signal is represented by R[n,k]=STFT[R(t,Δt)], and the time-frequency signal spectrum is SR(n,k)=|R[n,k]| 2 .

[0115] In formula (14), when there is deceptive interference and it is impossible to judge whether there is interference by the time width of the time-frequency domain energy distribution, it can be judged by the variance of the time-frequency energy distribution over time in the time-frequency domain. The variance of the time-frequency spectrum energy over time is expressed as:

[0116]

[0117] In formula (15), M0 and M are the actual starting time slice and the ending time slice of the time-frequency transform of R(t,Δt), respectively. Combined with formula (14), when there is deception interference, When there is less interference signal, there will be a more obvious increase. Set the threshold Th TF ,when When , it is judged that interference exists, otherwise, no interference exists.

[0118] In this embodiment, it is also determined whether there is a useful signal in the GNSS radio frequency signal based on the peak value of the relevant output signal and a preset decision threshold.

[0119] like Figure 3 As shown in FIG. 1 , the implementation process of the interference detection method for GNSS RF signals is shown. At the receiving end front end, the RF signal is down-converted into in-phase and quadrature components by mixing the input signal with two local oscillators, whose phases are offset by 90°. The received signal is then sampled and digitized by an analog-to-digital converter.

[0120] Then, the detection of suppressed interference is performed first. The signal is transformed (STFT) and the spectrogram is calculated; when there is no interference, the spectrogram sample approximately follows the chi-square distribution and the expected value is obtained based on the noise power estimate. The goodness of fit test is used in each frequency slice, and the observed value is compared with the expected value to calculate the statistic. The interference detection problem is converted into a statistical hypothesis testing problem. The suppressed interference detection problem is solved by comparing the probability value of the GoF detection with the predefined significance level.

[0121] In the correlation capture and tracking phase, the signal is detected for deception interference. The intermediate frequency signal is correlated and integrated with the local code. The output peak is compared with the decision threshold to determine whether there is a signal. When a signal exists, the output of the correlator is transformed by STFT and the spectrum is calculated. First, check the time width of the spectrum in the time domain. Ideally, in the absence of deception interference, the time width of the energy contained in the spectrum is two chip lengths. When the satellite signal is subject to deception interference with a time delay, the time width of the spectrum will be widened. Set a threshold and compare it with the width change value to determine whether there is delayed deception interference.

[0122] Then, the signal in the receiver is further detected to see if there is no delay or small delay deception interference. When the time width transformation value is less than the threshold, the sum of the energy of each column in the time-frequency slice is calculated. Interference detection is performed through the variance of the distribution of time-frequency energy in the time-frequency domain over time.

[0123] Figure 4 The figure is a schematic diagram of the signal spectrum and time-frequency analysis of continuous frequency modulation interference. Time-frequency domain statistical information interference detection can detect low interference-to-noise ratio (JNR) narrowband interference and some broadband interference such as continuous wave interference, hyperbolic frequency modulation interference, chirp interference with sawtooth, etc. Compared with narrowband interference, broadband interference refers to interference with a larger ratio of bandwidth to the bandwidth of the transmitted signal or a larger absolute bandwidth.

[0124] Figure 5 The output and time-frequency spectrum of the correlator in the absence of interference and with interference. When there is a significant time delay between the spoofing signal and the satellite signal, the time spectrum in the time-frequency domain is significantly widened. The presence of a spoofing signal can be determined based on the time width in the time-frequency domain. When the time width cannot be used to determine, the distribution of the time-frequency energy over time can be further observed to determine whether there is interference in the receiving system.

[0125] In this embodiment, a formula may also be used to represent the detection process of the GNSS radio frequency signal in this method.

[0126] Specific, definition They are respectively the presence and absence of real navigation signal states; Indicates the presence of suppressive interference, U N It means that there is no suppression interference; P N Indicates the presence or absence of deception interference. The system state is represented by S = <U,R e ,P> indicates.

[0127] When the output peak of the correlator is less than the decision threshold V m When , it means there is no real useful signal, that is Other corresponding system states are:

[0128]

[0129]

[0130] In the above GNSS interference detection method based on time-frequency domain statistical analysis, after the RF signal is down-converted and quantized, the suppression interference signal is first detected. When there is no suppression interference, the time-frequency domain spectrum of the received signal roughly follows the chi-square distribution. The goodness of fit test (GoF) method is used to determine whether the received signal is significantly different from the ideal signal without suppression interference. When there is a large difference between the time-frequency domain spectrum of the received signal and the ideal signal, it is determined that there is suppression interference. The deception signal has the same structure as the navigation signal and is submerged in the noise together with the received signal, so it is detected in the capture correlation stage of the receiver. After the received signal is detected by suppression interference, the signal is correlated with the local code by the correlation integrator and STFT transformation is performed. When there is no interference, the time width of the time-frequency domain spectrum is two code chips. By comparing the time width in the time-frequency domain, it can be preliminarily determined whether there is deception interference. When there is deception interference, the output of the correlator is the superposition signal of the useful navigation signal and the deception signal and the local code, which is manifested in the time-frequency domain as a difference in the distribution of signal energy over time. The variance of the time-frequency spectrum energy distribution over time is used to determine whether there is deception interference. This method can effectively determine whether there is suppression interference and deception interference.

[0131] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0132] In one embodiment, Figure 6 As shown, a GNSS interference detection device based on time-frequency domain statistical analysis is provided, including: a radio frequency signal processing module 200, a spectrum obtaining module 210, a suppression interference signal detection module 220, a related output signal obtaining module 230 and a deception interference detection module 240, wherein:

[0133] The radio frequency signal processing module 200 is used to obtain the GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then perform sampling and digital processing to obtain a digital component signal;

[0134] A spectrum obtaining module 210 is used to process the digitized component signal using short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0135] The suppression interference signal detection module 220 is used to determine whether the GNSS radio frequency signal has suppression interference based on the spectrum, using a goodness of fit detection method and a chi-square statistic. If suppression interference exists, the digitized component signal is suppressed to obtain a suppressed digitized component signal;

[0136] A correlation output signal obtaining module 230 is used to perform correlation integration between the digitized component signal or the suppressed digitized component signal and the local code to obtain a correlation output signal;

[0137] The deception interference detection module 240 is used to perform short-time Fourier transform on the relevant output signal, and then determine whether there is deception interference in the GNSS radio frequency signal through the time width difference and the variance of the time-frequency spectrum energy over time, so as to realize the detection of suppression interference and deception interference.

[0138] For the specific definition of the GNSS interference detection device based on time-frequency domain statistical analysis, please refer to the definition of the GNSS interference detection method based on time-frequency domain statistical analysis above, which will not be repeated here. Each module in the above-mentioned GNSS interference detection device based on time-frequency domain statistical analysis can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0139] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a GNSS interference detection method based on time-frequency domain statistical analysis is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0140] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0141] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0142] Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal;

[0143] Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0144] Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, suppressing the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0145] Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal;

[0146] After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time in turn, so as to realize the detection of suppression interference and deception interference.

[0147] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0148] Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal;

[0149] Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency;

[0150] Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, suppressing the suppression interference on the digitized component signal to obtain a suppressed digitized component signal;

[0151] Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal;

[0152] After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time in turn, so as to realize the detection of suppression interference and deception interference.

[0153] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0154] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A GNSS interference detection method based on time-frequency domain statistical analysis, characterized in that: The method comprises: Acquire a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then sample and digitize the signal to obtain a digital component signal; Processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency; Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference, and if there is suppression interference, performing suppression interference suppression on the digitized component signal to obtain a suppressed digitized component signal; Correlation integration of the digitized component signal or the suppressed digitized component signal with a local code is performed to obtain a correlation output signal; After performing short-time Fourier transform on the relevant output signal, it is judged whether there is deception interference in the GNSS radio frequency signal by using the time width difference and the variance of the time-frequency spectrum energy over time, so as to realize the detection of suppression interference and deception interference.

2. The GNSS interference detection method based on time-frequency domain statistical analysis according to claim 1, characterized in that: Based on the spectrum, using a goodness of fit detection method and a chi-square statistic, determining whether the GNSS radio frequency signal has suppression interference includes: Transform the suppression interference detection problem into a statistical hypothesis testing problem; Obtaining a plurality of frequency slices based on the spectrogram, calculating a variance value of each of the frequency slices, and using the variance value as an expected value of the corresponding frequency slice; Calculate the square of the deviation between the sample in each frequency slice and the corresponding expected value, weight it by the inverse of the corresponding expected value, add the calculation results of all samples, and obtain the test statistic; A P value is obtained according to the test statistic, and whether there is suppression interference in the GNSS radio frequency signal is determined through the relationship between the P value and a predefined significance level.

3. The GNSS interference detection method based on time-frequency domain statistical analysis according to claim 2 is characterized in that: In the statistical hypothesis testing problem, the absence of suppression interference in the GNSS radio frequency signal is taken as the null hypothesis, and the presence of suppression interference in the GNSS radio frequency signal is taken as the alternative hypothesis; When the P value is greater than or equal to the predefined significance level, the null hypothesis is accepted; When the P value is less than the predefined significance level, the alternative hypothesis is accepted.

4. The GNSS interference detection method based on time-frequency domain statistical analysis according to claim 1, characterized in that: When determining whether there is deception interference in the GNSS radio frequency signal: According to the time width difference of the relevant output signal after short-time Fourier transform, it is judged whether there is deception interference with a delay greater than 0; According to the time variance of the time-frequency spectrum energy of the relevant output signal after short-time Fourier transform, it is judged whether there is deceptive interference with a time delay equal to 0 or approximately 0.

5. The GNSS interference detection method based on time-frequency domain statistical analysis according to claim 4 is characterized in that: When determining whether there is deception interference in the GNSS radio frequency signal: A judgment is made based on the time width difference of the relevant output signal after the short-time Fourier transform and a preset time width difference threshold, and if the time width difference is greater than the preset time width difference threshold, then there is deception interference with a delay greater than 0 in the GNSS radio frequency signal; If the time width difference is less than or equal to the preset time width difference threshold, the variance of the time-frequency spectrum energy over time is calculated, and the variance is compared with the preset judgment threshold. If the variance of the time-frequency spectrum energy over time is greater than or equal to the judgment threshold, there is deception interference with a delay equal to 0 or approximately 0 in the GNSS radio frequency signal; If the variance of the time-frequency spectrum energy over time is less than the judgment threshold, there is no deception interference in the GNSS radio frequency signal.

6. The GNSS interference detection method based on time-frequency domain statistical analysis according to claims 1-5, characterized in that: According to the peak value of the correlation output signal and a preset decision threshold, it is determined whether there is a useful signal in the GNSS radio frequency signal.

7. A GNSS interference detection device based on time-frequency domain statistical analysis, characterized in that: The device comprises: A radio frequency signal processing module is used to obtain a GNSS radio frequency signal, convert the GNSS radio frequency signal into a baseband signal, and then perform sampling and digital processing to obtain a digital component signal; A spectrum obtaining module, used for processing the digitized component signal by short-time Fourier transform to obtain a spectrum represented by time and frequency; A suppression interference signal detection module is used to determine whether there is suppression interference in the GNSS radio frequency signal based on the spectrum, using a goodness of fit detection method and a chi-square statistic, and if there is suppression interference, suppress the suppression interference on the digitized component signal to obtain a suppressed digitized component signal; A correlation output signal obtaining module, used for performing correlation integration between the digitized component signal or the suppressed digitized component signal and a local code to obtain a correlation output signal; The deception interference detection module is used to perform short-time Fourier transform on the relevant output signal, and then determine whether there is deception interference in the GNSS radio frequency signal through the time width difference and the variance of the time-frequency spectrum energy over time, so as to realize the detection of suppression interference and deception interference.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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