Sweep frequency interference detection method and system based on frequency domain spectral line accumulation

Abstract

The invention discloses a frequency domain spectral line accumulation-based frequency sweeping interference detection method and system, and the method comprises the following steps: obtaining sampling data, carrying out the multi-channel digital channelization of the sampling data, and obtaining a frequency domain signal corresponding to each channel; spectral line accumulation is carried out on the frequency domain signal of each channel, then a noise area and a non-noise area are divided, the position of a bottom noise spectral line is estimated according to the noise area of each channel, and finally a detection threshold is calculated according to the spectral line of the position of the bottom noise spectral line. Taking a frequency range exceeding a detection threshold in the frequency domain signal of each channel as a to-be-detected frequency region; and carrying out sweep frequency interference detection based on the gravity center frequency on the to-be-detected frequency region of each channel, and combining the detection results of the channels to obtain a final sweep frequency interference detection result. According to the method, the calculated amount and complexity of the system can be greatly relieved, and the method has very high practicability in engineering implementation.

Description

Technical Field

[0001] The present invention relates to interference signal detection technology, and in particular to a frequency-domain spectrum line accumulation-based sweep frequency interference detection method and system. Background Art

[0002] There are all kinds of signals in the electromagnetic space. For the receiver, except for the communication signals that are useful to them, the rest are useless signals and even interference signals that affect their normal reception. These interference signals are both man-made and non-man-made. Among them, non-man-made interference signals usually have limitations, and their impact can be eliminated through certain signal processing methods; while man-made malicious interference has many types and good interference effects, which brings new challenges to both civil and military communication scenarios. Common communication interference includes single-tone interference, multi-tone interference, noise interference, comb spectrum interference and swept frequency interference. Among them, swept frequency interference adopts an interference strategy of rapidly scanning the frequency band. By covering the entire frequency band through the interference signal carrier frequency, it can have the interference effects of both suppression interference and targeting interference, and is widely used in the field of wireless communication interference.

[0003] The general interference signal detection process is: the received signal is sent to the DSP for signal sorting, interference feature extraction and interference type identification after completing analog-to-digital conversion through the ADC. However, for non-stationary interference signals such as swept frequency interference, the DSP processing speed cannot match the frequency time-varying speed. Summary of the invention

[0004] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems in the prior art, a frequency sweep interference detection method and system based on frequency domain spectrum line accumulation are provided, which can greatly alleviate the calculation amount and complexity of the system and has strong practicality in engineering implementation.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A frequency-domain spectrum line accumulation-based sweep frequency interference detection method comprises the following steps:

[0007] Acquire sampling data, perform multi-channel digital channelization on the sampling data, and obtain a frequency domain signal corresponding to each channel;

[0008] The frequency domain signal of each channel is accumulated, and then the noise area and the non-noise area are divided. The location of the noise floor spectrum line is estimated according to the noise area of ​​each channel. Finally, the detection threshold is calculated according to the spectrum line at the location of the noise floor spectrum line, and the frequency range exceeding the detection threshold in the frequency domain signal of each channel is taken as the frequency area to be detected.

[0009] A sweep frequency interference detection based on the center frequency is performed on the frequency region to be detected of each channel, and the detection results of each channel are combined to obtain the final sweep frequency interference detection result.

[0010] Furthermore, when the sampled data is converted into a multi-channel digital channel, it specifically includes:

[0011] The input signal is delayed in multiple stages to obtain the signal after each stage of delay;

[0012] The other channel of the input signal and the signal after each stage of delay are down-converted and phase-rotated, and then filtered using the corresponding filter;

[0013] Each filtered signal is multiplied by the corresponding phase adjustment factor and then Fourier transformed with the frequency offset factor corresponding to the current channel to obtain the frequency domain signal corresponding to the current channel.

[0014] Furthermore, the frequency domain signal expression corresponding to the current channel is as follows:

[0015]

[0016] Where k is the channel number, s [ (m) represents the result of the input signal after the pth delay, p = 0, 1, ..., M-1, M represents the number of multi-channel digital channelization, j m represents the phase rotation factor, h p (m) represents the filter corresponding to the p-th delay, e -jπp / 2M represents the phase adjustment factor of the p-th level delay, and e-j2πkp / M represents the frequency offset factor of the p-th level delay corresponding to the k-th channel.

[0017] Furthermore, when spectral lines are accumulated for the frequency domain signal of each channel, all spectral lines in each frame of the spectrum within a time window greater than or equal to the frequency sweep period are incoherently accumulated, and new statistics are calculated, and the expression is as follows:

[0018]

[0019] Among them, Z n represents the average value of the accumulated amplitude of all spectral lines in the spectrum of the nth frame, x n,k It represents the amplitude of the kth spectral line in the nth frame, and N represents the number of incoherent accumulations of the amplitude spectral line.

[0020] Furthermore, when dividing the noise area and the non-noise area and estimating the position of the noise floor spectrum line according to the noise area of ​​each channel, it includes:

[0021] If the statistical characteristics of the amplitude spectrum after incoherent accumulation obey the Gaussian distribution, and there are continuous noise spectrum lines in it, the area composed of the noise spectrum lines is regarded as the noise area, and the spectrum lines outside the noise area are regarded as the non-noise area.

[0022] If there are more than the specified number of continuous noise spectral lines in the noise area, the corresponding rectangular window is used to perform convolution with the spectral line of the noise area, and the position of the minimum value of the convolution output is used as the estimated value of the position of the background noise spectral line of the corresponding channel in the noise area;

[0023] The minimum value among the estimated values ​​of the positions of the noise floor spectrum lines of all channels is selected as the final estimated value of the positions of the noise floor spectrum lines.

[0024] Further, when calculating the detection threshold according to the spectrum line at the location of the background noise spectrum line, specifically calculating the constant false alarm rate threshold according to the expected value of the spectrum line at the location of the background noise spectrum line, the following steps are included:

[0025] Calculate the threshold value th1 under the standard normal distribution. The expression is as follows:

[0026] th1=x

[0027] The expected estimate of the background noise after spectral line accumulation For reference statistics, the threshold value th1 under the standard normal distribution is converted to the threshold value th2 actually used. The expression is as follows:

[0028]

[0029] Normalize the threshold value th2 to adapt to the expected value of the background noise under the white noise assumption The expression is as follows:

[0030]

[0031] Where x represents the value that satisfies the specified false alarm rate obtained by table lookup or calculation. Indicates the expected value of the spectrum line at the location of the background noise spectrum line, Z n Indicates the average value of the cumulative amplitude of all spectral lines in the spectrum corresponding to the spectral line at the location of the background noise spectral line, σ n represents the standard deviation of the noise, μ n represents the expected value of the noise, and N represents the number of incoherent accumulations of the amplitude spectrum lines.

[0032] Further, when performing frequency sweep interference detection based on the center of gravity frequency for the frequency region to be detected of each channel, it specifically includes:

[0033] Traverse each frame of spectrum in the frequency region to be detected in the current channel, and calculate the centroid normalized frequency of the current frame of spectrum;

[0034] If the centroid normalized frequency of the current frame spectrum is within the specified range, the current frame spectrum has no frequency offset;

[0035] If the centroid normalized frequency of the current frame spectrum is outside the specified range, the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is calculated;

[0036] If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is less than the specified threshold value, the current frame spectrum has no frequency offset;

[0037] If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is greater than or equal to the specified threshold value, the current frame spectrum undergoes frequency shift and the current frame spectrum is added to the frequency sweep interference detection result. At the same time, the frequency sweep count value is increased by one, and the centroid normalized frequency recorded in the current channel is replaced with the centroid normalized frequency of the current frame spectrum.

[0038] If the frequency sweep count value is greater than the specified value, the frequency sweep judgment of the current channel ends.

[0039] Furthermore, when calculating the centroid normalized frequency of the current frame spectrum, it includes:

[0040] Calculate the center of gravity frequency using the following formula:

[0041]

[0042] Normalize the center of gravity frequency, the formula is as follows:

[0043]

[0044] Among them, F1 is the lower limit of the frequency area to be detected, F2 is the upper limit of the frequency area to be detected, and P i is the power spectral density of the spectrum of the i-th frame, f i is the frequency value of the spectrum of the i-th frame.

[0045] Furthermore, when the detection results of each channel are combined, it includes:

[0046] Opening up a storage space for the frequency scanning results, and dividing the storage space into a storage area corresponding to each channel;

[0047] Acquire the sweep frequency interference detection result of each channel in real time, and assign a value to the address corresponding to the serial number of the spectrum line in the spectrum of the sweep frequency interference detection result in the corresponding storage area;

[0048] The addresses of the storage space are read in sequence according to the specified order, and the assigned address information is packaged and sent to the subsequent processing unit for real frequency conversion and result display.

[0049] The present invention also proposes a swept-frequency interference detection system based on frequency domain spectral line accumulation, comprising an ADC and an FPGA, wherein the FPGA is programmed or configured to execute any one of the swept-frequency interference detection methods based on frequency domain spectral line accumulation.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] The present invention is applied to FPGA, which can execute multiple operations in parallel and significantly improve the data processing speed.

[0052] The present invention adopts a multi-channel digital channelization method to reduce the speed of sampled data to match the FPGA processing speed, thereby achieving effective reduction in the speed of high-speed sampled data and improving processing efficiency.

[0053] The present invention accumulates spectral lines for each frame of spectrum, making the output more stable and close to the expected value of the amplitude of each spectral line. At the same time, by accumulating multiple frames of spectrum data, the influence of noise is reduced and the signal-to-noise ratio is improved, which helps to reduce the false alarm rate. In addition, the signal-to-noise ratio is improved through spectrum accumulation technology, making it possible to detect weak signals, thereby improving the detection capability of the system.

[0054] After estimating the location of the full channel noise floor, the present invention calculates the constant false alarm rate threshold according to the expected value of the spectrum line at the location of the noise floor spectrum line. The expected value of the spectrum line at this location provides an accurate noise reference level, which helps to set the detection threshold more accurately, thereby improving the accuracy of detection.

[0055] The present invention regards the frequency range of each channel that exceeds the threshold as the frequency area to be detected, performs frequency sweep interference detection based on the center of gravity frequency on the frequency area to be detected of each channel, and then merges and outputs the frequency sweep results, which can detect cross-channel frequency sweep interference and provide more comprehensive interference detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention.

[0057] Figure 2 This is a schematic diagram of the general implementation structure of digital channelized reception.

[0058] Figure 3 It is a schematic diagram of a structure for efficiently realizing digital channelization of real signals without blind spots in an embodiment of the present invention.

[0059] Figure 4 This is the curve of the relative error of the accumulated spectrum amplitude mean estimation changing with the number of accumulation times.

[0060] Figure 5 This is the relationship curve between the constant false alarm rate threshold value and the accumulation times.

[0061] Figure 6 Schematic diagram of defining thresholds for different false alarm rates on the 512-point FFT amplitude spectrum.

[0062] Figure 7 This is a schematic diagram of the spectrum accumulation characteristics of swept frequency interference.

[0063] Figure 8 This is a schematic diagram of the frequency change of the center of gravity of the swept frequency interference signal.

[0064] Fig. 9 Schematic diagram of the normalized center of gravity frequency change of broadband interference.

[0065] Fig.10 This is a schematic diagram of the change in the normalized center of gravity frequency of swept frequency interference.

[0066] Fig.11 The figure is a schematic diagram of the scanning interference detection process of a single channel in an embodiment of the present invention.

[0067] Fig.12 The figure is a schematic diagram of the cross-channel merging process of frequency sweep interference in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0069] Embodiment 1

[0070] The present invention proposes a frequency-domain spectrum accumulation-based sweep interference detection method, which is applied to a detection system composed of an ADC and an FPGA. The received signal is converted to digital by the ADC and then sent to the FPGA for interference detection. Since the method is implemented by the FPGA, the problem that the DSP processing speed in the existing solution cannot match its frequency time-varying speed can be overcome. Figure 1 As shown, the method of this embodiment includes the following steps:

[0071] S1) acquiring sampled data, performing multi-channel digital channelization on the sampled data, and obtaining a frequency domain signal corresponding to each channel;

[0072] S2) accumulating the spectrum lines of the frequency domain signal of each channel, and then dividing the noise area and the non-noise area, and estimating the position of the noise floor spectrum line according to the noise area of ​​each channel, and finally calculating the detection threshold according to the spectrum line at the position of the noise floor spectrum line, and taking the frequency range exceeding the detection threshold in the frequency domain signal of each channel as the frequency area to be detected;

[0073] S3) performing a sweep frequency interference detection based on the centroid frequency for the to-be-detected frequency region of each channel, and combining the detection results of each channel to obtain a final sweep frequency interference detection result.

[0074] Each step is described in detail below.

[0075] Since the ADC sampling frequency used in the system is 3840MHz, and the FPGA cannot process data at a GHz clock, a multi-channel digital channelization method is needed to reduce the sampling data speed to match the FPGA processing speed. In step S1 of this embodiment, M=16 levels of aliasing-free digital channelization are used, with a channel processing bandwidth of 240MHz and a passband bandwidth of 120MHz. The general implementation structure of digital channelization reception is as follows: Figure 2 As shown in the figure, the input signal s(n) is decomposed into M sub-signals. Each sub-signal is mixed with the corresponding local oscillator signal, and then low-pass filtered and down-converted to obtain the frequency domain signals of M channels. The output expression of each channel is:

[0076]

[0077] Among them, ω k =(k+1 / 4)2π / M, k=0,1,…,M-1. Let i=rM+p, then:

[0078]

[0079] For p = 0, 1, ..., M-1, let s p (m) = s (mM-p), h p (r) = h(rM+p), we have:

[0080]

[0081] Substituting the rotation factors into the equation gives:

[0082]

[0083] Substituting the above formula into the output expression, we have:

[0084]

[0085] Where k is the channel number, s p (m) represents the result of the input signal after the pth delay, p = 0, 1, ..., M-1, M represents the number of multi-channel digital channelization, j m represents the phase rotation factor, h p (m) represents the filter corresponding to the p-th delay, e-jπp / 2M represents the phase adjustment factor of the p-th delay, and e -j2πkp / M represents the frequency offset factor of the p-th level delay corresponding to the k-th channel. Based on this expression, the efficient implementation structure of the real signal without blind zone digital channelization can be obtained as follows: Figure 3As shown in FIG. 1 , when the sampled data is converted into a multi-channel digital channel, the following steps are specifically included:

[0086] The input signal s(n) is delayed by M-1 stages to obtain the signals s(n-1), ..., s(n-M+1) after each stage of delay;

[0087] The input signal s(n) and the signals after each stage of delay s(n-1), ..., s(n-M+1) are down-converted and phase-rotated, and then the corresponding filters h0(m), ..., h M-1 (m) performing filtering;

[0088] Multiply each filtered signal by the corresponding phase adjustment factor e -jπ0 / 2M ,……,e -jπ(M-1) / 2M Then the frequency offset factor e corresponding to the current channel -j2πkO / M ,……,e -j2πk(M-1) / M Perform M-point Fourier transform to obtain the frequency domain signal y corresponding to the current channel k (m).

[0089] Since the spectrum of a real signal is symmetrical, Figure 3 The purpose of the conjugate operation is to map the received spectrum in the negative frequency domain to the positive frequency domain to complete the coverage of the entire positive frequency domain.

[0090] The signal detection method based on FPGA should reduce the point-by-point detection in the time dimension or frequency dimension as much as possible to reduce the complexity. It is known that after the signal is transformed from the time domain to the frequency domain through Fourier transform, the frequency distribution of various signals can be observed, and the spectrum of the full frequency band can be obtained by splicing multiple channels after solving the spectrum separately. In step S2 of this embodiment, the noise area and the non-noise area in the frequency domain are determined in multiple channels, and a detection threshold is obtained based on the background noise by the CFAR (constant false alarm detection) method, and the frequency range of the threshold point is determined by the energy comparison method to be regarded as the frequency area to be detected, and the subsequent sweep interference judgment is also only performed on the spectrum lines in the frequency area to be detected.

[0091] When the input signal is noise, the real and imaginary parts of the spectrum line obey Gaussian distribution, and its modulus, i.e., the amplitude spectrum line, obeys Rayleigh distribution. Assume that the real and imaginary parts of the spectrum line k obey independent normal distribution Then the probability density function of the amplitude spectrum Rayleigh distribution of the kth spectral line is:

[0092]

[0093] Its mean is The variance is And under the condition of Gaussian white noise, it can be considered that the mean and variance of each spectral line are equal, that is:

[0094]

[0095] Where N is the number of incoherent accumulations of the amplitude spectrum line, is the variance of the noise.

[0096] Since the amplitude of any spectral line in each frame of spectrum can be regarded as independent and identically distributed, the statistical characteristics of the incoherent accumulation of multiple frames of spectrum lines can be approximated by a Gaussian random process, that is, there is a statistic:

[0097]

[0098] Among them, μ n represents the expected value of the noise, x n,k represents the amplitude of the kth spectral line in the nth frame, and the statistic X n Obeying the standard normal distribution N(0,1), let the statistic of the incoherent accumulation of multi-frame spectral lines be Z n :

[0099]

[0100] Among them, Z n represents the average value of the accumulated amplitude of all spectral lines in the spectrum of the nth frame, x n,k It represents the amplitude of the kth spectral line in the nth frame, and N represents the number of incoherent accumulations of the amplitude spectral line.

[0101] Therefore, the method of multi-frame accumulation of amplitude spectrum is used to reduce Z n The variance of the output is adjusted to make the output amplitude close to the expected value of the amplitude of each spectral line.

[0102] From the above content, it can be seen that in step S2, when the spectrum line accumulation of the frequency domain signal of each channel is performed, specifically, all the spectrum lines in the multi-frame spectrum are incoherently accumulated by the mean calculation method, and a new statistic Z is calculated. n .

[0103] Furthermore, the estimation of noise statistical characteristics and noise power first requires the correct extraction of spectral lines containing only noise components from the accumulated spectrum. In actual engineering, CME or FCME algorithms are usually used for iteration to achieve approximate purposes, but FPGA has advantages in high-speed parallel data processing, so the above-mentioned iterative algorithm is not suitable for implementation in FPGA. Therefore, the assumption for selecting noise spectral lines in this scheme is: there is a continuous noise spectral line of not less than 10% in the monitored spectrum, and a rectangular window corresponding to the length is used to convolve with the spectral line, and the position of the minimum value of the convolution output is searched, which is the estimated value of the position of the background noise spectral line. In addition, because FPGA processes multiple channel data in parallel, the smallest convolution value in multiple channels should be selected as the estimated value of the background noise of the entire channel. Therefore, when dividing the noise area and the non-noise area in step S2 and estimating the position of the background noise spectral line according to the noise area of ​​each channel, it includes:

[0104] The statistical characteristics of the amplitude spectrum after incoherent accumulation obey the Gaussian distribution, and it is assumed that there are a certain number of continuous noise spectrum lines. The area composed of noise spectrum lines is regarded as the noise area, and the spectrum lines outside the noise area are regarded as the non-noise area.

[0105] If there are more than the specified number (10% of all the spectral lines in the spectrum) of continuous noise lines in the noise area, a rectangular window corresponding to the length of the noise area is used to convolve with the spectral lines in the noise area, and the position of the minimum value of the convolution output is used as the estimated value of the position of the bottom noise spectral line of the channel corresponding to the noise area;

[0106] The minimum value among the estimated values ​​of the positions of the noise floor spectrum lines of all channels is selected as the final estimated value of the positions of the noise floor spectrum lines.

[0107] After obtaining the position of the background noise spectrum line, the spectrum line set at this position can be extracted as the sample of the background noise estimation, and then the amplitude average of all samples is calculated to estimate the expected value of the background noise amplitude spectrum.

[0108] As recorded above It is known that the expected value of the noise amplitude spectrum after multi-frame spectrum mean detection is E(x k ), under white noise conditions:

[0109]

[0110] Since the power value of the noise is Therefore, the expected estimate of the noise amplitude spectrum can be used The noise power estimate is:

[0111]

[0112] The relative error of power estimation is:

[0113]

[0114] From the above formula, we can know that the noise power estimation error is proportional to the relative error of the mean estimation. Since the amplitude spectrum obeys the normal distribution after multi-frame accumulation, the maximum relative error can be calculated according to the '3σ' principle as follows:

[0115]

[0116] The relative error changes with the accumulation times of amplitude spectrum lines, that is, the total number of spectrum lines in the spectrum. Figure 4 shown.

[0117] In step S2 of this embodiment, when calculating the detection threshold according to the spectrum line at the location of the background noise spectrum line, specifically calculating the constant false alarm rate threshold according to the expected value of the spectrum line at the location of the background noise spectrum line, the following steps are included:

[0118] Calculate the threshold value th1 under the standard normal distribution. Specifically, assume that there is no interference signal and only noise is the H0 hypothesis, otherwise it is the H1 hypothesis. If the false alarm rate is p fa CFAR detection, then the cumulative distribution function under the H0 assumption needs to satisfy:

[0119]

[0120] At this time, the threshold value th1 under the standard normal distribution is expressed as follows:

[0121] th1=x (15)

[0122] Where x represents a value that satisfies a specified false alarm rate obtained by table lookup or calculation.

[0123] The expected estimate of the background noise after spectral line accumulation As a reference statistic, the threshold value th1 under the standard normal distribution is converted into the actual threshold value th2. If the expected estimate of the background noise after accumulating multiple frames of spectrum is is the reference statistic, then the expression of the threshold value th2 of the statistic is as follows:

[0124]

[0125] Normalize the threshold value th2 to adapt to the expected value of the background noise under the white noise assumption The expression is as follows:

[0126]

[0127] in, Indicates the expected value of the spectrum line at the location of the background noise spectrum line, Z nIndicates the average value of the cumulative amplitude of all spectral lines in the spectrum corresponding to the spectral line at the location of the background noise spectral line, σ n represents the standard deviation of the noise, μ n represents the expected value of noise, and N represents the total number of spectral lines. According to the above formula, the relationship between the constant false alarm threshold value and the accumulation times can be obtained as follows: Figure 5 As shown. Figure 5 The lower limit of the constant false alarm threshold in the 512-point FFT amplitude spectrum is used to define the thresholds of different false alarm rates. Figure 6 As shown, it is basically consistent with the theoretical analysis.

[0128] For any spectral line, the frequency sweep interference can be regarded as a signal that appears intermittently at a certain frequency. When the FFT processing time is much smaller than the frequency sweep period, it can be approximately considered that the frequency sweep interference is a transient stable deterministic signal within each Fourier transform time; if the frequency sweep interference changes within 1 to 2 frequency resolution units in each FFT, it can also be considered as a special "pulse interference signal", and the "pulse repetition period" is the frequency sweep period T sw . Figure 7 The amplitude characteristics of the swept-frequency interference accumulation spectrum are simulated when the interference / noise power ratio within the interference bandwidth is 5dB and the interference bandwidth is 0.75 of the observation bandwidth. It can be seen that the more times the spectrum is accumulated, the higher the interference-to-noise ratio of the spectrum line is improved, and the smoother the spectrum amplitude envelope of the swept-frequency interference is.

[0129] Therefore, the spectrum accumulation time of the swept frequency interference must be at least greater than or equal to T sw In order to ensure the coverage of the swept frequency interference bandwidth, in step S2, all spectral lines in the multi-frame spectrum are incoherently accumulated, specifically, all spectral lines in each frame spectrum in a time window greater than or equal to the swept frequency period are incoherently accumulated.

[0130] In the actual digital processing process, the amplitude spectrum of the swept frequency interference is not completely flat. Therefore, in step S3 of this embodiment, it is necessary to use the centroid frequency offset of the amplitude spectrum in the detection area for judgment, and the centroid frequency calculation formula is:

[0131]

[0132] Among them, F1 is the lower limit of the frequency area to be detected, F2 is the upper limit of the frequency area to be detected, and P i is the power spectral density of the spectrum of the i-th frame, f i is the frequency value of the spectrum of the i-th frame. From the perspective of reducing the range, the center of gravity frequency can also be normalized before processing, and the normalized frequency is also very consistent with the decimal mode operation of the FPGA divider. The normalization operation of the center of gravity frequency is:

[0133]

[0134] The center of gravity frequency shift process is as follows Figure 8 As shown, where F X 、F y are the center frequency positions at two moments respectively, then the decision process of frequency sweep interference is as follows: first, convert the center frequency into normalized frequency:

[0135]

[0136] After normalizing the center of gravity frequency, Q x and Q y The value range is [0,1], and the midpoint frequency of the area to be detected (i.e. the center frequency value F of the area to be detected) mid =(F2+F1) / 2) corresponds to 0.5 after normalization. For this embodiment, when the signal is a non-frequency-converting signal, the center-of-gravity frequency generally jumps around the midpoint frequency, such as Fig. 9 and Fig.10 As shown, this can be used to distinguish non-frequency swept signals that occupy a certain bandwidth.

[0137] Therefore, if Fig.11 As shown, in step S3, when performing frequency sweep interference detection based on the center of gravity frequency for the frequency region to be detected of each channel, it specifically includes:

[0138] Traverse each frame spectrum in the frequency region to be detected of the current channel, and calculate the centroid normalized frequency of the current frame spectrum according to formulas (18) and (19);

[0139] If the centroid normalized frequency of the current frame spectrum is within the specified range, the current frame spectrum has no frequency offset;

[0140] If the centroid normalized frequency of the current frame spectrum is outside the specified range, the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is calculated;

[0141] If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is less than the specified threshold value, the current frame spectrum has no frequency offset;

[0142] If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is greater than or equal to the specified threshold value, the current frame spectrum undergoes frequency shift and the current frame spectrum is added to the frequency sweep interference detection result. At the same time, the frequency sweep count value is increased by one, and the centroid normalized frequency recorded in the current channel is replaced with the centroid normalized frequency of the current frame spectrum.

[0143] If the frequency sweep count value is greater than the specified value, the frequency sweep judgment of the current channel ends.

[0144] In this embodiment, the specified range is set as a numerical interval centered on 0.5, the upper limit of the interval is 0.5+Δx, and the lower limit of the interval is 0.5-Δx. The Δx value can be adjusted according to the actual situation, but it should be ensured that the centroid normalized frequency is close to 0.5 when there is no frequency offset. The specified threshold value of this embodiment is 0.1. When the difference in the centroid normalized frequency is greater than or equal to the threshold value, it is considered that a frequency offset has occurred at this time, and the frequency sweep count value is increased by one. Since the frequency sweep rate is fast or slow, the normalized centroid frequency in the two frames of each 512-point FFT (interval of about 2.133us) may not change, so if the normalized centroid frequency of the current and subsequent frames remains unchanged, the counter value should also remain unchanged at this time. In addition, it is also possible that the normalized centroid frequency exceeds the threshold in some frames but then falls below the threshold. For this situation, the frequency sweep count value needs to be deducted accordingly to ensure that no misjudgment occurs. The sweep frequency counting threshold is set according to the actual sweep frequency interference rate range. When the sweep frequency counter value exceeds the threshold, the interference signal at this time is considered to be a sweep frequency signal, the sweep frequency judgment is ended, and the sweep frequency interference detection result is obtained.

[0145] Since the bandwidth occupied by the frequency sweep signal may span multiple channel frequency bands, a frequency sweep merger is required in step S3 of this embodiment. When the detection results of each channel are merged, the following steps are included:

[0146] Opening up a storage space for the frequency scanning results, and dividing the storage space into a storage area corresponding to each channel;

[0147] Acquire the sweep frequency interference detection result of each channel in real time, and assign a value to the address corresponding to the serial number of the spectrum line in the spectrum of the sweep frequency interference detection result in the corresponding storage area;

[0148] The addresses of the storage space are read in sequence according to the specified order, and the assigned address information is packaged and sent to the subsequent processing unit for real frequency conversion and result display.

[0149] For example, in this embodiment, the number of channels for which the sweep frequency interference detection result is obtained is 10, and the sweep frequency merging is as follows: Fig.12 As shown, the specific steps are as follows:

[0150] 1) A RAM with a width of 1 bit and a depth of 512*10=5120 is allocated to store the sweep frequency estimation results. However, due to the overlap between channels, the actual depth can be greater than 512*1+256*9=2816.

[0151] 2) According to the channel number output by the frequency sweep judgment result, the frequency sweep start frequency and the end frequency can be converted into relative frequency. For example, if the frequency sweep is located in channel 1, the start spectrum line is the 200th, the end spectrum line is the 250th, and the RAM start address of channel 1 is 0, then data 1 is written to the RAM addresses 0+200 and 0+250; similarly, if it is located in channel 2, since the adjacent channels overlap by 50%, the RAM start address of channel 2 is 256, then data 1 is written to the RAM addresses 256+200 and 256+250, and so on. For channels where no frequency sweep results are detected, data 0 is written to the corresponding RAM addresses.

[0152] 3) When all channels are processed, the RAM reads out the frequency sweep results in order from low to high addresses. For the part with output '1', it is considered that there is frequency sweep interference and the parameters are packaged into a PDW data packet and sent to the subsequent processing unit, which finally converts the real frequency and displays the results; for the part with output '0', it is considered that there is no frequency sweep interference and no subsequent processing is performed.

[0153] Embodiment 2

[0154] This embodiment proposes a swept frequency interference detection system based on frequency domain spectral line accumulation, including an ADC and an FPGA. The FPGA is programmed or configured to execute the swept frequency interference detection method based on frequency domain spectral line accumulation described in the first embodiment.

[0155] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A frequency sweep interference detection method based on frequency domain spectrum line accumulation, characterized in that: The following steps are involved: Acquire sampling data, perform multi-channel digital channelization on the sampling data, and obtain a frequency domain signal corresponding to each channel; The frequency domain signal of each channel is accumulated, and then the noise area and the non-noise area are divided. The location of the noise floor spectrum line is estimated according to the noise area of ​​each channel. Finally, the detection threshold is calculated according to the spectrum line at the location of the noise floor spectrum line, and the frequency range exceeding the detection threshold in the frequency domain signal of each channel is taken as the frequency area to be detected. A sweep frequency interference detection based on the center frequency is performed on the frequency region to be detected of each channel, and the detection results of each channel are combined to obtain the final sweep frequency interference detection result.

2. The method for detecting frequency-domain interference based on frequency-domain spectrum accumulation according to claim 1, characterized in that: When the sampled data is converted into multi-channel digital channels, it specifically includes: The input signal is delayed in multiple stages to obtain the signal after each stage of delay; The other channel of the input signal and the signal after each stage of delay are down-converted and phase-rotated, and then filtered using the corresponding filter; Each filtered signal is multiplied by the corresponding phase adjustment factor and then Fourier transformed with the frequency offset factor corresponding to the current channel to obtain the frequency domain signal corresponding to the current channel.

3. The method for detecting frequency-sweeping interference based on frequency-domain spectrum accumulation according to claim 2, characterized in that: The frequency domain signal expression corresponding to the current channel is as follows: Where k is the channel number, s p (m) represents the result of the input signal after the pth delay, p = 0, 1, ..., M-1, M represents the number of multi-channel digital channelization, j m represents the phase rotation factor, h p (m) represents the filter corresponding to the p-th delay, e -jπp / 2M represents the phase adjustment factor of the p-th delay, e -j2πkp / M Represents the frequency offset factor of the p-th level delay corresponding to the k-th channel.

4. The method for detecting frequency-domain interference based on frequency-domain spectrum accumulation according to claim 1, characterized in that: When spectral lines are accumulated for the frequency domain signal of each channel, all spectral lines in each frame of the spectrum within a time window greater than or equal to the sweep period are incoherently accumulated and a new statistic is calculated. The expression is as follows: Among them, Z n represents the average value of the accumulated amplitude of all spectral lines in the spectrum of the nth frame, x n,k It represents the amplitude of the kth spectral line in the nth frame, and N represents the number of incoherent accumulations of the amplitude spectral line.

5. The method for detecting frequency-sweeping interference based on frequency-domain spectrum line accumulation according to claim 1, characterized in that: Dividing the noise area and the non-noise area and estimating the position of the noise floor spectrum line according to the noise area of ​​each channel includes: If the statistical characteristics of the amplitude spectrum after incoherent accumulation obey the Gaussian distribution, and there are continuous noise spectrum lines in it, the area composed of the noise spectrum lines is regarded as the noise area, and the spectrum lines outside the noise area are regarded as the non-noise area; If there are more than the specified number of continuous noise spectral lines in the noise area, the corresponding rectangular window is used to perform convolution with the spectral line of the noise area, and the position of the minimum value of the convolution output is used as the estimated value of the position of the background noise spectral line of the corresponding channel in the noise area; The minimum value among the estimated values ​​of the positions of the noise floor spectrum lines of all channels is selected as the final estimated value of the positions of the noise floor spectrum lines.

6. The method for detecting frequency-sweeping interference based on frequency-domain spectrum accumulation according to claim 1, characterized in that: When calculating the detection threshold according to the spectrum line at the location of the background noise spectrum line, specifically calculating the constant false alarm rate threshold according to the expected value of the spectrum line at the location of the background noise spectrum line, the following steps are included: Calculate the threshold value th1 under the standard normal distribution. The expression is as follows: th1=x The expected estimate of the background noise after spectral line accumulation For reference statistics, the threshold value th1 under the standard normal distribution is converted to the threshold value th2 actually used. The expression is as follows: Normalize the threshold value th2 to adapt to the expected value of the background noise under the white noise assumption The expression is as follows: Where x represents the value that satisfies the specified false alarm rate obtained by table lookup or calculation. Indicates the expected value of the spectrum line at the location of the background noise spectrum line, Z n Indicates the average value of the cumulative amplitude of all spectral lines in the spectrum corresponding to the spectral line at the location of the background noise spectral line, σ n represents the standard deviation of the noise, μ n represents the expected value of the noise, and N represents the number of incoherent accumulations of the amplitude spectrum lines.

7. The method for detecting frequency-sweeping interference based on frequency-domain spectrum line accumulation according to claim 1, characterized in that: When performing frequency sweep interference detection based on the center of gravity frequency for the frequency region to be detected of each channel, it specifically includes: Traverse each frame of spectrum in the frequency region to be detected in the current channel, and calculate the centroid normalized frequency of the current frame of spectrum; If the centroid normalized frequency of the current frame spectrum is within the specified range, the current frame spectrum has no frequency offset; If the centroid normalized frequency of the current frame spectrum is outside the specified range, the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is calculated; If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is less than the specified threshold value, the current frame spectrum has no frequency offset; If the difference between the centroid normalized frequency of the current frame spectrum and the centroid normalized frequency recorded in the current channel is greater than or equal to the specified threshold value, the current frame spectrum undergoes frequency shift and the current frame spectrum is added to the frequency sweep interference detection result. At the same time, the frequency sweep count value is increased by one, and the centroid normalized frequency recorded in the current channel is replaced with the centroid normalized frequency of the current frame spectrum. If the frequency sweep count value is greater than the specified value, the frequency sweep judgment of the current channel ends.

8. The method for detecting frequency-sweeping interference based on frequency-domain spectrum line accumulation according to claim 7, characterized in that: When calculating the centroid normalized frequency of the current frame spectrum, it includes: Calculate the center of gravity frequency using the following formula: Normalize the center of gravity frequency, the formula is as follows: Among them, F1 is the lower limit of the frequency area to be detected, F2 is the upper limit of the frequency area to be detected, and P i is the power spectral density of the spectrum of the i-th frame, f i is the frequency value of the spectrum of the i-th frame.

9. The method for detecting frequency-sweeping interference based on frequency-domain spectrum line accumulation according to claim 7, characterized in that: When the detection results of each channel are merged, it includes: Opening up a storage space for the frequency scanning results, and dividing the storage space into a storage area corresponding to each channel; Acquire the sweep frequency interference detection result of each channel in real time, and assign a value to the address corresponding to the serial number of the spectrum line in the spectrum of the sweep frequency interference detection result in the corresponding storage area; The addresses of the storage space are read in sequence according to the specified order, and the assigned address information is packaged and sent to the subsequent processing unit for real frequency conversion and result display.

10. A frequency-sweeping interference detection system based on frequency-domain spectrum line accumulation, characterized in that: It comprises an ADC and an FPGA, wherein the FPGA is programmed or configured to execute the swept frequency interference detection method based on frequency domain spectrum line accumulation as claimed in any one of claims 1 to 9.

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