Method and apparatus for detecting interference with satellite navigation signals of high-dynamic projectile carriers

Abstract

This application discloses a method and apparatus for detecting interference in satellite navigation signals of highly dynamic projectile-borne vehicles, addressing the technical problem of high false alarm probability in interference detection. Specifically, an interference detection scheme for highly dynamic projectile-borne vehicle satellite navigation signals filters interference signals by setting a detection threshold to reduce the false alarm probability. Furthermore, it reduces the impact of random thermal noise jitter on detection errors by averaging multiple received system noise segments. Additionally, it introduces power spectrum monitoring based on the Fourier transform calculation of the interference signal to obtain the frequency domain characteristics of the non-stationary interference signal, thereby providing support for positioning performance evaluation testing of onboard navigation terminals under interference conditions.

Description

Technical Field

[0001] This application relates to the field of satellite navigation technology, and in particular to a method and apparatus for detecting interference with satellite navigation signals of highly dynamic projectile carriers. Background Technology

[0002] With the rapid development of electronic technology, satellite navigation systems are widely used in the national economy and military fields. However, deceptive jamming has not only gradually become a significant threat to satellite navigation systems, but it also possesses the capability to cause catastrophic consequences.

[0003] In the process of developing the existing technology, the inventors discovered that:

[0004] The ubiquitous electromagnetic radiation in battlefield combat environments makes the satellite navigation environment extremely complex. In this environment, satellite navigation signals are susceptible to various intentional electromagnetic interferences, and the high false alarm probability caused by deceptive interference will lead to the saturation of the navigation and guidance system during operation, thus preventing it from functioning properly and seriously affecting the accuracy of the output results.

[0005] Therefore, there is a need to provide an interference detection scheme for satellite navigation signals of highly dynamic projectile carriers to solve the technical problem of high false alarm probability in interference detection. Summary of the Invention

[0006] This application provides a new interference detection scheme for satellite navigation signals of highly dynamic projectile carriers, in order to solve the technical problem of high false alarm probability in interference detection.

[0007] Specifically, a method for detecting interference with satellite navigation signals of highly dynamic projectile carriers includes the following steps:

[0008] Based on the system noise, determine the detection threshold under the allowable false alarm probability;

[0009] Receive signal;

[0010] Perform power spectrum transformation on the signal;

[0011] Identify spectral lines with amplitudes higher than the detection threshold from the power spectrum;

[0012] The interference parameters are determined based on the position and height of the spectral lines that are above the detection threshold.

[0013] Output interference parameters.

[0014] Furthermore, determining the detection threshold within the allowable false alarm probability based on system noise specifically includes:

[0015] The relationship between the detection threshold and the false alarm probability is as follows:

[0016] ;

[0017] Furthermore, the detection probability of the interference signal is:

[0018]

[0019]

[0020] ;

[0021] in, Indicates the detection threshold. This represents the probability of a false alarm. This indicates the probability of detecting the interference signal. It is a generalized Markum Q function.

[0022] Furthermore, the method also includes:

[0023] The received system noise segments are accumulated and averaged to reduce the noise variance and improve the correlation between the detection threshold and the false alarm probability.

[0024] Furthermore, based on the position and height of spectral lines above the detection threshold, interference parameters are determined, specifically including:

[0025] Based on the position and height of the spectral line above the detection threshold, determine:

[0026] The bandwidth of the interference is:

[0027] ;

[0028] Then the first The power of each interference is:

[0029] ;

[0030] in For amplitudes above the threshold spectral line, For the average amplitude of the spectrum without interference lines, then the first... The interference-to-noise ratio can be expressed as:

[0031] ;

[0032] in The number of sampling points. Sampling frequency, For the first The interference exceeds the maximum value of the corresponding data point. For the first The interference exceeds the minimum value of the corresponding data point that corresponds to the threshold;

[0033] No. The center frequency of an interference can be expressed as:

[0034] ;

[0035] in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

[0036] Furthermore, the method also includes:

[0037] Number of sampling points Without changing, make near to make Smaller dimensions increase frequency resolution and reduce measurement error;

[0038] in Sampling frequency, This is the highest frequency of the signal.

[0039] Furthermore, the method also includes:

[0040] exist Increase the number of sampling points while keeping the numbers unchanged. , The smaller size increases the spectral resolution, thereby reducing measurement errors.

[0041] This application also provides an interference detection device for satellite navigation signals of highly dynamic projectile carriers.

[0042] Specifically, an interference detection device for satellite navigation signals of highly dynamic projectile carriers includes:

[0043] The detection module is used to determine the detection threshold within which the false alarm probability is acceptable based on system noise; it is also used to receive signals.

[0044] The conversion module is used to perform power spectrum transformation on the signal;

[0045] The identification module is used to identify spectral lines with amplitudes higher than the detection threshold from the power spectrum; it is also used to determine interference parameters based on the position and height of the spectral lines that are higher than the detection threshold; and it is also used to output the interference parameters.

[0046] Furthermore, the detection module is used to determine a detection threshold within the allowable false alarm probability based on system noise, specifically including:

[0047] The relationship between the detection threshold and the false alarm probability is as follows:

[0048] ;

[0049] Furthermore, the detection probability of the interference signal is:

[0050]

[0051]

[0052] ;

[0053] in, Indicates the detection threshold. This represents the probability of a false alarm. This indicates the probability of detecting the interference signal. It is a generalized Markum Q function.

[0054] Furthermore, the detection module is also used for:

[0055] The received system noise segments are accumulated and averaged to reduce the noise variance and improve the correlation between the detection threshold and the false alarm probability.

[0056] Furthermore, the identification module is used to determine interference parameters based on the position and height of spectral lines above the detection threshold, specifically including:

[0057] Based on the position and height of the spectral line above the detection threshold, determine:

[0058] The bandwidth of the interference is:

[0059] ;

[0060] Then the first The power of each interference is:

[0061] ;

[0062] in For amplitudes above the threshold spectral line, For the average amplitude of the spectrum without interference lines, then the first... The interference-to-noise ratio can be expressed as:

[0063] ;

[0064] in The number of sampling points. Sampling frequency, For the first The interference exceeds the maximum value of the corresponding data point. For the first The interference exceeds the minimum value of the corresponding data point that corresponds to the threshold;

[0065] No. The center frequency of an interference can be expressed as:

[0066] ;

[0067] in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

[0068] The technical solution provided in this application has at least the following beneficial effects:

[0069] Interference signals are filtered by setting a detection threshold to reduce the probability of false alarms. Furthermore, the impact of random thermal noise jitter on detection errors is reduced by averaging the received system noise from multiple segments. Additionally, power spectrum monitoring is introduced based on the Fourier transform of the interference signal to obtain the frequency domain characteristics of non-stationary interference signals, thus providing support for the positioning performance evaluation test of the onboard navigation terminal under interference conditions. Attached Figure Description

[0070] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0071] Figure 1 This is a flowchart illustrating an interference detection method for satellite navigation signals of a highly dynamic projectile carrier, provided in an embodiment of this application.

[0072] Figure 2 A comparison diagram of the power spectrum before and after multiple time-domain averaging provided in this application embodiment.

[0073] Figure 3 This is a graph showing the relationship between the number of sampling points and the detection probability in the interference detection method provided in the embodiments of this application.

[0074] Figure 4 This is a graph showing the relationship between different bandwidth signals and detection probabilities provided in the embodiments of this application.

[0075] Figure 5 This is a schematic diagram of the structure of an interference detection device for satellite navigation signals of a high-dynamic projectile carrier provided in an embodiment of this application.

[0076] Interference detection device for 100 pairs of high-dynamic projectile-carrier satellite navigation signals

[0077] 11 Detection Modules

[0078] 12 conversion modules

[0079] 13 Recognition Modules Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0081] Please refer to Figure 1 To address the technical problem of high false alarm probability in interference detection, this application provides a method for detecting interference in satellite navigation signals of high-dynamic projectile carriers, comprising the following steps:

[0082] S110: Determine the detection threshold under the allowable false alarm probability based on system noise;

[0083] S120: Receive signal;

[0084] S130: Perform power spectrum transformation on the signal;

[0085] S140: Identify spectral lines with amplitudes higher than the detection threshold from the power spectrum;

[0086] S150: Determine the interference parameters based on the position and height of the spectral lines that are above the detection threshold;

[0087] S160: Output interference parameters.

[0088] Its core principle is to set a detection threshold for interference, identify power spectral lines above the detection threshold as interference, and identify spectral lines below the detection threshold as receiver noise, thereby detecting whether interference exists in the frequency band; then, based on the position and height of the spectral lines above the detection threshold, the carrier frequency, power, bandwidth and other parameters of the interference are determined as interference parameters, and the interference parameters are output.

[0089] It is evident that the false alarm probability is related to the setting of the detection threshold.

[0090] Furthermore, in one specific embodiment provided in this application, step S110, based on system noise, determines the detection threshold under the allowable false alarm probability, specifically including:

[0091] The relationship between the detection threshold and the false alarm probability is as follows:

[0092] ;

[0093] Furthermore, the detection probability of the interference signal is:

[0094]

[0095]

[0096] ;

[0097] in, Indicates the detection threshold. This represents the probability of a false alarm. This indicates the probability of detecting the interference signal. This is the generalized Marcum's Q function.

[0098] It should be noted that this application further reveals the relationship between the detection threshold and the false alarm probability, as well as the detection probability of interference signals, thereby avoiding missed detections and improving the reliability of interference detection.

[0099] The derivation process of the above relation is explained below:

[0100] The signal received by the satellite navigation interference detection system is The received navigation signal is The system's Gaussian noise is The interference signal is Then we have:

[0101]

[0102] In high-dynamic projectile-carrier satellite navigation systems, the navigation signal is relatively weak, usually ten to twenty decibels lower than the system noise. Therefore, the received signal can be considered to contain interference or noise.

[0103] After power spectrum monitoring of the signal, the detection statistics can be expressed as follows:

[0104] In the case of only noise:

[0105] :

[0106] In the presence of interference:

[0107] :

[0108] The power spectrum of the received signal is:

[0109]

[0110] In the formula ,here It is a window function Fourier transform.

[0111] Ignoring the influence of the window function, the envelope magnitude of the above equation satisfies the following equation:

[0112]

[0113] Because the sum of squares of statistically independent and identically distributed (IID) Gaussian random variables with non-zero means follows a non-central Chi-square distribution, the ambiguity function, when a signal is present, follows a non-central Chi-square distribution with 2 degrees of freedom, which can be expressed as:

[0114]

[0115] In the formula It is a zero-order modified Bessel function of the first kind.

[0116] In the absence of a signal, the ambiguity function follows a central Chi-squared distribution with 2 degrees of freedom, which can be expressed as:

[0117]

[0118] Assuming the detection threshold is set to Then the false alarm probability of the detection can be expressed as:

[0119]

[0120] The relationship between the detection threshold and the false alarm probability can be expressed as:

[0121]

[0122] The detection probability of interference signals can be described as follows:

[0123]

[0124] make Then the above formula can be described as:

[0125]

[0126]

[0127]

[0128] in, This refers to the generalized Marcum's Q function.

[0129]

[0130] Furthermore, in one specific embodiment provided in this application, in order to reduce the false alarm probability and reduce the impact of random thermal noise jitter on detection error, the method further includes:

[0131] The received system noise segments are accumulated and averaged to reduce the noise variance and improve the correlation between the detection threshold and the false alarm probability.

[0132] Please refer to the details. Figure 2 In practical applications, the inventors reduced the impact of noise variance by averaging multiple received data segments before processing. It gets smaller. Figure 2 The solid line represents the power spectrum of 100 averages in the time domain, while the dashed line represents the direct power spectrum of a data segment. From... Figure 2 As can be seen, multiple averaging can effectively reduce the variance of noise and effectively improve the detection probability of the signal while keeping the false alarm probability unchanged, without affecting the power of interference signals and noise. It can effectively reduce the false alarm probability and improve the detection probability.

[0133] Furthermore, in one specific embodiment provided in this application, the power spectrum transformation of the signal in step S130 is taken into consideration that:

[0134] In actual interference spectrum analysis, some interference signals are non-stationary signals. Non-stationary interference signals do not meet the absolute integrability condition of Fourier transform, making it difficult to obtain the frequency domain characteristics of such interference signals, and thus difficult to analyze them.

[0135] To address the technical problem of obtaining the frequency domain characteristics of non-stationary interference signals, this application introduces power spectrum monitoring into spectrum analysis to detect frequency domain energy, thereby obtaining the frequency domain characteristics of non-stationary interference signals. In other words, the interference detection method provided in this application combines spectrum analysis with power spectrum monitoring.

[0136] Among these methods, spectral analysis of high-dynamic interference signals is an effective way to accurately sense interference parameters. Spectral analysis of interference signals involves calculating the Fast Fourier Transform (FFT) of the interference signal to obtain its spectral parameters, and then studying its frequency domain characteristics. Power spectrum monitoring, on the other hand, describes the frequency domain characteristics of random signals from a statistical perspective, thereby analyzing the signal's frequency domain characteristics and obtaining interference parameters.

[0137] The following describes the implementation process of using Fourier transform to perform spectral analysis on a signal:

[0138] The definition of the Discrete Fourier Transform is:

[0139]

[0140] It is a sequence Spectral function, sequence The necessary and sufficient condition for the existence of the discrete Fourier transform is that the sequence... Absolutely summable, i.e., satisfying

[0141]

[0142] If continuous time signal If the time domain is infinitely long, then the discretized sequence It is also an infinitely long sequence, while the Discrete Fourier Transform is only suitable for calculating finite-length sequences, therefore it is necessary to... Time-domain windowing is used to truncate an infinitely long sequence, reducing it to a finite-length sequence. However, when an infinitely long sequence is truncated using a rectangular window function, the spectrum expands significantly and oscillates to both sides, a phenomenon commonly referred to as spectral leakage or power leakage. This introduces errors into spectral analysis and reduces frequency resolution. The spectral broadening and oscillation caused by truncation can also lead to spectral aliasing.

[0143] To reduce spectral leakage and inter-spectral interference, the truncation length can be increased. And select a window function with smaller sidelobes. Increase data length This can narrow the main lobe and improve frequency resolution. At the same time, a non-rectangular window function with smaller side lobes should be selected to reduce the main-to-side lobe ratio, which is more conducive to signal spectrum analysis.

[0144] Commonly used window functions include rectangular window, triangular window, Hanning window, Hamming window, Blackman window, Chebyshev window, Bartlett window, and Kaiser window. These window functions have their own characteristics. Generally speaking, the wider the main lobe, the greater the ratio of main lobe to secondary lobe; the narrower the main lobe, the smaller the ratio of main lobe to secondary lobe, but the spectral resolution will be improved.

[0145] In practical applications, power spectrum monitoring can be manifested as: periodogram method, segmented average periodogram method, windowed average periodogram method, Welch method, maximum entropy method, multi-window method, power spectrum estimation based on MUSIC algorithm, power spectrum estimation based on ESPRIT algorithm, and fine spectrum estimation using time-frequency analysis techniques, etc.

[0146] In the specific implementation of the interference detection method provided in this application, the inventors, through research and simulation of the above algorithm, preferably adopted a two-stage Fourier transform processing combined with two-stage power spectrum estimation. This is because for non-stationary interference signals, their Fourier transform does not exist, so it is necessary to analyze their power spectrum to obtain the frequency domain characteristics of the non-stationary interference signal. Adopting a two-stage processing scheme, that is, first determining the initial frequency band range of the interference signal, and when it is necessary to accurately determine the boundary, windowing the signal, downsampling, and performing Fourier transform processing again, can meet the requirements of fine resolution of the spectrum.

[0147] The received continuous signal is converted into a digital signal through A / D sampling, and then transformed into a baseband digital complex signal through quadrature downconversion, low-pass filtering, and downsampling. The corresponding window function and data length are then selected according to preset parameters. When a signal is detected, a first-level spectrum analysis is performed; a second-level spectrum analysis is then performed based on the results of the first-level analysis.

[0148] To achieve better spectral resolution accuracy within a limited length, spectral correction techniques can be added, such as: spectral accuracy correction based on the continuous refinement Fourier transform analysis method (FFT+FT method), full-phase discrete spectral correction technique, and triangular method spectral correction technique.

[0149] It should also be noted that the second-order Fourier transform spectral analysis scheme cannot analyze multiple frequency points within the same frequency band simultaneously. Therefore, when multiple interferences exist within the monitored frequency band, the analysis efficiency is low. To achieve fine-grained spectral analysis of 10Hz across the entire analysis band, a large amount of data must be used for Fourier transform operations. In this case, real-time monitoring mechanisms are almost impossible to operate, and only post-analysis mechanisms can be employed. Online analysis mechanisms are only used to observe the presence or absence of interference spectra and the approximate spectrum across the entire frequency band. In this scenario, online monitoring can achieve spectral resolution on the order of kHz, while post-event static playback is used to achieve fine-grained spectral resolution of 10Hz.

[0150] Furthermore, in one specific embodiment provided in this application, step S150 determines the interference parameters based on the spectral line position and spectral line height above the detection threshold, specifically including:

[0151] Based on the detection threshold, the position and height of spectral lines above the detection threshold, a search is conducted in the power spectral domain to separate the aliased signals in the time domain and determine parameters such as the number of interferences, bandwidth, and center frequency as interference parameters.

[0152] Interference bandwidth detection involves recording the frequency points and energy values ​​of a continuous segment of the power spectrum that are significantly above a threshold. This allows us to determine the interference bandwidth.

[0153] ;

[0154] Then the first The power of each interference is:

[0155] ;

[0156] in For amplitudes above the threshold spectral line, For the average amplitude of the spectrum without interference lines, then the first... The interference-to-noise ratio can be expressed as:

[0157] ;

[0158] in The number of sampling points. Sampling frequency, For the first The interference exceeds the maximum value of the corresponding data point. For the first The interference exceeds the minimum value of the corresponding data point that corresponds to the threshold;

[0159] No. The center frequency of an interference can be expressed as:

[0160] ;

[0161] in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

[0162] As can be seen, while completing interference detection, the interference detection method provided in this application can also estimate parameters such as the center frequency and bandwidth of the interference signal.

[0163] It should be noted that the spectral resolution and measurement accuracy are affected by the number of data points processed and the sampling frequency. If things remain unchanged, try to make near (in (the highest frequency of the signal), which allows This decreases the size, thereby increasing the frequency resolution and reducing measurement error. Furthermore, in... Increase the number of sampling points while keeping the numbers unchanged. , A smaller sample size increases spectral resolution, which in turn reduces measurement error. The spectral resolution is determined by the sampling rate and the number of Fourier transform processing points.

[0164] Please refer to Figure 3-4 The inventors derived the relationship between the number of sampling points and the detection probability, as well as the relationship between signals of different bandwidths and the detection probability, through simulation.

[0165] Specifically, the center frequency of the signal carrier is set to 1MHz, the sampling frequency to 10MHz, and the number of sampling points to 10000. Assuming 100% signal detection, the estimation performance of the single-frequency signal parameters in step S150 is shown in the table below:

[0166]

[0167] As can be seen from the table, even at a low interference-to-noise ratio (IRR), step S150 has good estimation performance for single-carrier signals, and the estimation error of interference power is less than 0.1 dB when the IRR is -15 dB.

[0168] The center frequency of the signal carrier is 1.5MHz, the sampling frequency is 10MHz, and the number of sampling points is 10000. Assuming 100% signal detection, the estimation performance of the linear frequency modulated signal parameters in step S150 for a bandwidth of 1MHz is shown in the table below:

[0169]

[0170] As can be seen from the table, for a linear frequency modulated signal with a certain bandwidth, when the interference-to-noise ratio is greater than 0dB, step S150 has good estimation performance for the bandwidth, center frequency and power of the interference signal.

[0171] Furthermore, the interference detection method provided in this application can estimate parameters such as the center frequency and bandwidth of the interference signal while performing interference detection. In a specific embodiment provided in this application, the interference detection method is used to estimate the center frequency of the interference as follows:

[0172] Assuming the energy of the interference signal is concentrated near its center frequency, and the energy value point corresponding to the center frequency is theoretically the point of maximum energy, the power spectrum of the interference signal will definitely have a spike near its center frequency, significantly higher than the energy threshold. Therefore, the frequencies and energy values ​​exceeding the threshold are recorded, along with the maximum energy value. and corresponding frequency points and the frequency values ​​of the two points adjacent to it. and and energy value and Using the data from these three points, the center frequency of the interference can be estimated using the following formula. :

[0173]

[0174] In the formula This represents the number of sample points in the Fourier transform.

[0175] Interference power estimation using interference detection methods is as follows:

[0176] The detected interference power is used Definition (the ratio of the expected detected signal power to the unexpected signal power). The expected signal power value at each Fourier transform sampling point. Assuming the interference originates from the received signal, since the power spectrum of the desired signal is typically symmetrical, most interference exists on one side of the desired signal. An approximation of the desired signal power is determined by the following formula.

[0177]

[0178] The interference signal power using the interference detection method is:

[0179]

[0180] In the formula is It is the number of Fourier transform sample points. It is the ideal signal energy. This is the number of interference points detected. It is the power of the signal after interference is added.

[0181]

[0182] The interference bandwidth estimation is as follows:

[0183] Threshold interference bandwidth for:

[0184]

[0185] Interference bandwidth Defined as the bandwidth at the peak energy of an ideal signal, then for:

[0186]

[0187] In the formula It is the energy peak of the interference signal (i.e., the one mentioned above) ), It is the energy peak of the ideal signal. It is the difference between the peak value of the interference signal energy and the threshold value.

[0188] By selecting the size of the reference window and the data blocks and sampling points within the reference window, the threshold for radio frequency interference detection can be controlled, thereby detecting low-power interference.

[0189] P is a metric for measuring the presence of radio frequency interference. The larger the P value, the greater the probability that radio frequency interference is present. The expression is as follows:

[0190]

[0191] In the above formula and These are the mean and variance of the reference window. It is the mean of the estimated window.

[0192] In summary, the interference detection method for satellite navigation signals of highly dynamic projectile carriers provided in this application reduces the false alarm probability by filtering interference signals through setting a detection threshold. Furthermore, it reduces the impact of random thermal noise jitter on detection errors by averaging the received multi-segment system noise. Additionally, it introduces power spectrum monitoring based on the Fourier transform calculation of the interference signal to obtain the frequency domain characteristics of the non-stationary interference signal, thereby providing support for positioning performance evaluation testing of onboard navigation terminals under interference conditions.

[0193] Please refer to Figure 5 To support methods for detecting interference with satellite navigation signals from highly dynamic projectile carriers, this application also provides an interference detection device 100 for satellite navigation signals from highly dynamic projectile carriers, comprising:

[0194] Detection module 11 is used to determine the detection threshold under the allowable false alarm probability based on system noise; it is also used to receive signals.

[0195] Conversion module 12 is used to perform power spectrum transformation on the signal;

[0196] The identification module 13 is used to identify spectral lines with amplitudes higher than the detection threshold from the power spectrum; it is also used to determine interference parameters based on the position and height of the spectral lines higher than the detection threshold; and it is also used to output the interference parameters.

[0197] Its core principle is to set a detection threshold for interference, identify power spectral lines above the detection threshold as interference, and identify spectral lines below the detection threshold as receiver noise, thereby detecting whether interference exists in the frequency band; then, based on the position and height of the spectral lines above the detection threshold, the carrier frequency, power, bandwidth and other parameters of the interference are determined as interference parameters, and the interference parameters are output.

[0198] It is evident that the false alarm probability is related to the setting of the detection threshold.

[0199] Furthermore, in one specific embodiment provided in this application, the detection module 11 determines a detection threshold within the allowable false alarm probability based on system noise, specifically including:

[0200] The relationship between the detection threshold and the false alarm probability is as follows:

[0201] ;

[0202] Furthermore, the detection probability of the interference signal is:

[0203]

[0204]

[0205] ;

[0206] in, Indicates the detection threshold. This represents the probability of a false alarm. This indicates the probability of detecting the interference signal. This is the generalized Marcum's Q function.

[0207] It should be noted that this application further reveals the relationship between the detection threshold and the false alarm probability, as well as the detection probability of interference signals, thereby avoiding missed detections and improving the reliability of interference detection.

[0208] The derivation process of the above relation is explained below:

[0209] The signal received by the satellite navigation interference detection system is The received navigation signal is The system's Gaussian noise is The interference signal is Then we have:

[0210]

[0211] In high-dynamic projectile-carrier satellite navigation systems, the navigation signal is relatively weak, usually ten to twenty decibels lower than the system noise. Therefore, the received signal can be considered to contain interference or noise.

[0212] After power spectrum monitoring of the signal, the detection statistics can be expressed as follows:

[0213] In the case of only noise:

[0214] :

[0215] In the presence of interference:

[0216] :

[0217] The power spectrum of the received signal is:

[0218]

[0219] In the formula ,here It is a window function Fourier transform.

[0220] Ignoring the influence of the window function, the envelope magnitude of the above equation satisfies the following equation:

[0221]

[0222] Because the sum of squares of statistically independent and identically distributed (IID) Gaussian random variables with non-zero means follows a non-central Chi-square distribution, the ambiguity function, when a signal is present, follows a non-central Chi-square distribution with 2 degrees of freedom, which can be expressed as:

[0223]

[0224] In the formula It is a zero-order modified Bessel function of the first kind.

[0225] In the absence of a signal, the ambiguity function follows a central Chi-squared distribution with 2 degrees of freedom, which can be expressed as:

[0226]

[0227] Assuming the detection threshold is set to Then the false alarm probability of the detection can be expressed as:

[0228]

[0229] The relationship between the detection threshold and the false alarm probability can be expressed as:

[0230]

[0231] The detection probability of interference signals can be described as follows:

[0232]

[0233] make Then the above formula can be described as:

[0234]

[0235]

[0236]

[0237] in, This refers to the generalized Marcum's Q function.

[0238]

[0239] Furthermore, in one specific embodiment provided in this application, in order to reduce the false alarm probability and reduce the impact of random thermal noise jitter on detection error, the detection module 11 is further configured to:

[0240] The received system noise segments are accumulated and averaged to reduce the noise variance and improve the correlation between the detection threshold and the false alarm probability.

[0241] In practical applications, the detection module 11 reduces the impact of noise variance by averaging the received data segments before processing. The variance of noise is reduced through multiple averaging processes. This effectively increases the detection probability of the signal while maintaining the false alarm probability, without affecting the power of the interference signal and noise.

[0242] Furthermore, in one specific embodiment provided in this application, the conversion module 12 performs power spectrum transformation on the signal considering that:

[0243] In actual interference spectrum analysis, some interference signals are non-stationary signals. Non-stationary interference signals do not meet the absolute integrability condition of Fourier transform, making it difficult to obtain the frequency domain characteristics of such interference signals, and thus difficult to analyze them.

[0244] To address the technical problem of difficulty in obtaining the frequency domain characteristics of non-stationary interference signals, this application introduces power spectrum monitoring into spectrum analysis to detect frequency domain energy, thereby obtaining the frequency domain characteristics of non-stationary interference signals.

[0245] Among these methods, spectral analysis of high-dynamic interference signals is an effective way to accurately sense interference parameters. Spectral analysis of interference signals involves calculating the Fast Fourier Transform (FFT) of the interference signal to obtain its spectral parameters, and then studying its frequency domain characteristics. Power spectrum monitoring, on the other hand, describes the frequency domain characteristics of random signals from a statistical perspective, thereby analyzing the signal's frequency domain characteristics and obtaining interference parameters.

[0246] The following describes the implementation process of the interference detection device 100 using Fourier transform to perform spectrum analysis on the signal:

[0247] The definition of the Discrete Fourier Transform is:

[0248]

[0249] It is a sequence Spectral function, sequence The necessary and sufficient condition for the existence of the discrete Fourier transform is that the sequence... Absolutely summable, i.e., satisfying

[0250]

[0251] If continuous time signal If the time domain is infinitely long, then the discretized sequence It is also an infinitely long sequence, while the Discrete Fourier Transform is only suitable for calculating finite-length sequences, therefore it is necessary to... Time-domain windowing is used to truncate an infinitely long sequence, reducing it to a finite-length sequence. However, when an infinitely long sequence is truncated using a rectangular window function, the spectrum expands significantly and oscillates to both sides, a phenomenon commonly referred to as spectral leakage or power leakage. This introduces errors into spectral analysis and reduces frequency resolution. The spectral broadening and oscillation caused by truncation can also lead to spectral aliasing.

[0252] To reduce spectral leakage and inter-spectral interference, the truncation length can be increased. And select a window function with smaller sidelobes. Increase data length This can narrow the main lobe and improve frequency resolution. At the same time, a non-rectangular window function with smaller side lobes should be selected to reduce the main-to-side lobe ratio, which is more conducive to signal spectrum analysis.

[0253] Commonly used window functions include rectangular window, triangular window, Hanning window, Hamming window, Blackman window, Chebyshev window, Bartlett window, and Kaiser window. These window functions have their own characteristics. Generally speaking, the wider the main lobe, the greater the ratio of main lobe to secondary lobe; the narrower the main lobe, the smaller the ratio of main lobe to secondary lobe, but the spectral resolution will be improved.

[0254] The interference detection device 100 monitors the power spectrum of the interference signal. In practical applications, this can be manifested as: periodogram method, segmented average periodogram method, windowed average periodogram method, Welch method, maximum entropy method, multi-window method, power spectrum estimation based on MUSIC algorithm, power spectrum estimation based on ESPRIT algorithm, and fine spectrum estimation using time-frequency analysis technology.

[0255] In the specific implementation of the interference detection device 100 provided in this application, the inventors, through research and simulation of the above-mentioned algorithm, preferably adopted a two-stage Fourier transform processing combined with two-stage power spectrum estimation. This is because for non-stationary interference signals, their Fourier transform does not exist, so it is necessary to analyze their power spectrum to obtain the frequency domain characteristics of the non-stationary interference signal. Adopting a two-stage processing scheme, that is, first determining the initial frequency band range of the interference signal, and when it is necessary to accurately determine the boundary, windowing the signal, downsampling, and performing Fourier transform processing again, can meet the requirements of fine resolution of the spectrum.

[0256] The received continuous signal is converted into a digital signal through A / D sampling, and then transformed into a baseband digital complex signal through quadrature downconversion, low-pass filtering, and downsampling. The corresponding window function and data length are then selected according to preset parameters. When a signal is detected, a first-level spectrum analysis is performed; a second-level spectrum analysis is then performed based on the results of the first-level analysis.

[0257] To achieve better spectral resolution accuracy within a limited length, spectral correction techniques can be added, such as: spectral accuracy correction based on the continuous refinement Fourier transform analysis method (FFT+FT method), full-phase discrete spectral correction technique, and triangular method spectral correction technique.

[0258] It should also be noted that the second-order Fourier transform spectral analysis scheme cannot analyze multiple frequency points within the same frequency band simultaneously. Therefore, when multiple interferences exist within the monitored frequency band, the analysis efficiency is low. To achieve fine-grained spectral analysis of 10Hz across the entire analysis band, a large amount of data must be used for Fourier transform operations. In this case, real-time monitoring mechanisms are almost impossible to operate, and only post-analysis mechanisms can be employed. Online analysis mechanisms are only used to observe the presence or absence of interference spectra and the approximate spectrum across the entire frequency band. In this scenario, online monitoring can achieve spectral resolution on the order of kHz, while post-event static playback is used to achieve fine-grained spectral resolution of 10Hz.

[0259] Furthermore, in one specific embodiment provided in this application, the identification module 13 determines interference parameters based on the position and height of spectral lines above the detection threshold, specifically including:

[0260] Based on the detection threshold, the position and height of spectral lines above the detection threshold, a search is conducted in the power spectral domain to separate the aliased signals in the time domain and determine parameters such as the number of interferences, bandwidth, and center frequency as interference parameters.

[0261] Interference bandwidth detection involves recording the frequency points and energy values ​​of a continuous segment of the power spectrum that are significantly above a threshold. This allows us to determine the interference bandwidth.

[0262] ;

[0263] Then the first The power of each interference is:

[0264] ;

[0265] in For amplitudes above the threshold spectral line, For the average amplitude of the spectrum without interference lines, then the first... The interference-to-noise ratio can be expressed as:

[0266] ;

[0267] in The number of sampling points. Sampling frequency, For the first The interference exceeds the maximum value of the corresponding data point. For the first The interference exceeds the minimum value of the corresponding data point that corresponds to the threshold;

[0268] No. The center frequency of an interference can be expressed as:

[0269] ;

[0270] in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

[0271] As can be seen, while completing interference detection, the interference detection method provided in this application can also estimate parameters such as the center frequency and bandwidth of the interference signal.

[0272] It should be noted that the spectral resolution and measurement accuracy are affected by the number of data points processed and the sampling frequency. If things remain unchanged, try to make near (in (the highest frequency of the signal), which allows This decreases the size, thereby increasing the frequency resolution and reducing measurement error. Furthermore, in... Increase the number of sampling points while keeping the numbers unchanged. , A smaller sample size increases spectral resolution, which in turn reduces measurement error. The spectral resolution is determined by the sampling rate and the number of Fourier transform processing points.

[0273] Furthermore, the interference detection device 100 provided in this application can estimate parameters such as the center frequency and bandwidth of the interference signal while performing interference detection. In a specific embodiment provided in this application, the interference detection device 100 estimates the interference center frequency as follows:

[0274] Assuming the energy of the interference signal is concentrated near its center frequency, and the energy value point corresponding to the center frequency is theoretically the point of maximum energy, the power spectrum of the interference signal will definitely have a spike near its center frequency, significantly higher than the energy threshold. Therefore, the frequencies and energy values ​​exceeding the threshold are recorded, along with the maximum energy value. and corresponding frequency points and the frequency values ​​of the two points adjacent to it. and and energy value and Using the data from these three points, the center frequency of the interference can be estimated using the following formula. :

[0275]

[0276] In the formula This represents the number of sample points in the Fourier transform.

[0277] The interference power estimation using interference detection device 100 is as follows:

[0278] The detected interference power is used Definition (the ratio of the expected detected signal power to the unexpected signal power). The expected signal power value at each Fourier transform sampling point. Assuming the interference originates from the received signal, since the power spectrum of the desired signal is typically symmetrical, most interference exists on one side of the desired signal. An approximation of the desired signal power is determined by the following formula.

[0279]

[0280] The interference signal power of the interference detection device 100 is:

[0281]

[0282] In the formula is It is the number of Fourier transform sample points. It is the ideal signal energy. This is the number of interference points detected. It is the power of the signal after interference is added.

[0283]

[0284] The interference bandwidth estimation is as follows:

[0285] Threshold interference bandwidth for:

[0286]

[0287] Interference bandwidth Defined as the bandwidth at the peak energy of an ideal signal, then for:

[0288]

[0289] In the formula It is the energy peak of the interference signal (i.e., the one mentioned above) ), It is the energy peak of the ideal signal. It is the difference between the peak value of the interference signal energy and the threshold value.

[0290] By selecting the size of the reference window and the data blocks and sampling points within the reference window, the threshold for radio frequency interference detection can be controlled, thereby detecting low-power interference.

[0291] P is a metric for measuring the presence of radio frequency interference. The larger the P value, the greater the probability that radio frequency interference is present. The expression is as follows:

[0292]

[0293] In the above formula and These are the mean and variance of the reference window. It is the mean of the estimated window.

[0294] In summary, the interference detection device 100 for high-dynamic projectile-borne satellite navigation signals provided in this application filters interference signals by setting a detection threshold to reduce the probability of false alarms. Furthermore, by averaging the received multi-segment system noise, the impact of random thermal noise jitter on detection errors is reduced. Additionally, by introducing power spectrum monitoring based on the Fourier transform calculation of the interference signal, the frequency domain characteristics of the non-stationary interference signal are obtained, thus providing support for positioning performance evaluation testing under interference conditions of the onboard navigation terminal.

[0295] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0296] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0297] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of interference detection for high dynamic projectile carrier satellite navigation signals, characterized in that, The method comprises the following steps: determining a detection threshold under a false alarm probability according to system noise; receiving a signal; performing power spectrum transformation on the signal; determining a spectral line with an amplitude higher than the detection threshold from the power spectrum; determining an interference parameter according to a spectral line position and a spectral line height higher than the detection threshold; outputting the interference parameter; The method further comprises: The relationship between the detection threshold and the false alarm probability is: ； and the detection probability of the interference signal is: ； wherein, denotes a detection threshold, denotes a false alarm probability, denotes a detection probability of an interfering signal, is a generalized Marcum Q function.

2. The method of claim 1, wherein, The method further comprises: accumulatively averaging the received multiple segments of system noise to reduce noise variance and improve the correlation between the detection threshold and the false alarm probability.

3. The method of claim 1, wherein, The method further comprises: The method further comprises: The method further comprises: ； The power of the first interfering signal is: ； wherein is the amplitude of the spectral line above the threshold, is the average amplitude of the spectral lines without interference, then the interference corresponds to a signal-to-noise ratio that can be expressed as: ； wherein is the number of samples, is the sampling frequency, is the maximum value of the data points for which the interference exceeds the threshold, is the minimum value of the data points for which the interference exceeds the threshold; The center frequency of the first interfering signal can be expressed as: ； in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

4. The method of claim 3, wherein, The detection module is configured to determine a detection threshold under a false alarm probability according to system noise; In the number of sampling points Without changing the case, make Approach So that Small, the frequency resolution increases, reduces the measurement error; wherein is the sampling frequency, is the highest frequency of the signal.

5. The method of claim 3, wherein, The method further comprises: In Without changing, increase the number of sampling points , Smaller, higher spectral resolution to reduce measurement error.

6. An apparatus for interference detection of high dynamic projectile carrier satellite navigation signals, characterized by The conversion module is configured to perform power spectrum transformation on the signal; The identification module is configured to determine a spectral line with an amplitude higher than the detection threshold from the power spectrum; and determine an interference parameter according to a spectral line position and a spectral line height higher than the detection threshold; The method further comprises: The relationship between the detection threshold and the false alarm probability is: and the detection probability of the interference signal is: The detection module is further configured to: accumulatively average the received multiple segments of system noise to reduce noise variance and improve the correlation between the detection threshold and the false alarm probability. ； The identification module is configured to determine an interference parameter according to a spectral line position and a spectral line height higher than the detection threshold, and specifically comprises: ； wherein denotes a detection threshold, denotes a false alarm probability, denotes a detection probability of an interfering signal, is a generalized Marcum Q function.

7. The apparatus of claim 6, wherein, determine: the bandwidth of the interference is:

8. The apparatus of claim 6, wherein, ​ ​ ​ ； The power of the first interfering signal is: ； wherein is the amplitude of the spectral line above the threshold, is the average amplitude of the spectral lines without interference, then the interference corresponds to a signal-to-noise ratio that can be expressed as: ； wherein is the number of samples, is the sampling frequency, is the maximum value of the data points for which the th interference exceeds the threshold, is the minimum value of the data points for which the th interference exceeds the threshold; The center frequency of the first interfering signal can be represented as: ； in, The center frequency of the entire detection bandwidth. , For the first The center frequency of the interference.

Images