Signal bandwidth and pulse width detection method

Abstract

The invention discloses a signal bandwidth and pulse width detection method, and relates to the field of communication signal reconnaissance. The method comprises the following steps of: segmenting baseband data and calculating a power spectrum of each segment; smooth filtering is carried out on the power spectrum; thirdly, processing the smoothed data through a first-stage self-adaptive threshold, and preliminarily estimating the bandwidth and the pulse width of the signal; and finally, frequency domain filtering is carried out based on the roughly estimated bandwidth, fine detection is carried out by using a second-stage adaptive threshold, and the accurate bandwidth and pulse width are finally obtained. According to the method, the detection process is converted from the time domain to the frequency domain, and a multistage threshold estimation mechanism is adopted, so that the defects that a traditional time domain method is easily interfered by noise and multiple signals are difficult to detect at the same time are effectively overcome, and the accuracy and reliability of pulse signal parameter measurement are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication signal reconnaissance, and in particular to a method for simultaneously detecting bandwidth and pulse width of multiple pulse signals based on frequency domain analysis in wideband detection. BACKGROUND

[0002] Pulse signal detection in radio signal detection is mainly achieved by analyzing the time domain and frequency domain characteristics of electromagnetic wave signals. In the field of signal detection, wideband detection is required to detect the bandwidth and pulse width of signals at different frequency points.

[0003] In radio signal detection, pulse signal detection is one of the key tasks. Traditional methods mainly rely on analyzing the time domain characteristics of electromagnetic wave signals. However, time domain detection methods are susceptible to noise signals, leading to inaccurate measurement of pulse width. In addition, traditional time domain methods can usually only detect one specific frequency point, making it difficult to adapt to the simultaneous detection requirements of multiple different frequency point signals in modern wideband communication environments.

[0004] Therefore, there is an urgent need in the art for a new technical solution that can overcome noise interference and simultaneously detect the bandwidth and pulse width of multiple signals. SUMMARY

[0005] The purpose of the present application is to provide a signal bandwidth and pulse width detection method to solve the problem of large noise influence and inability to simultaneously detect multiple signals in the prior art.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: A signal bandwidth and pulse width detection method, comprising the following steps: (1) Calculate the baseband signal power spectrum Perform symbol rate and other conventional parameter estimation on the input baseband signal, with sampling rate denoted as , data length denoted as N, divide the data into K segments, each segment length M, and adjacent two end data overlap length M / 2, then the minimum unit for estimating pulse width is the total data length divided by K, then K is the down rounding of , calculate the power spectrum {P}_{k}\left [ {n} \right ] of each segment data according to formula (1) {P}_{k}\left [ {n} \right ]=\left | {\sum ^{M-1}_{i=0} {x\left ( {i+kM / 2} \right ){e}^{-j2\pi ni / M}}} \right |^{2} (1) where 0≤k line interval According to formula (2): (2).

[0007] (2) data smoothing The K segments of data are respectively subjected to smoothing filtering, and the influence of interference signals is eliminated.

[0008] (3) threshold estimation The average value is obtained by accumulating and summing the K segments of data subjected to smoothing filtering and dividing the accumulated number, and the average value is multiplied by the first coefficient 1.5 to serve as the first threshold for signal detection. According to the comparison between the threshold value and the K segments of data subjected to smoothing, the position value corresponding to the points exceeding the threshold is 1.

[0009] (4) coarse estimation of signal bandwidth and pulse width The number of points being 1 is counted for the K segments of data, and if all the point values in a certain segment of data are 0 or the point values corresponding to 0, it is indicated that there is no signal, and the number of times of the current data segment signal appearing can be calculated, and the number of signal appearing signals, the number of segments existing signals, the total data time length and the coarse estimated pulse width are counted. A threshold value is obtained by accumulating and averaging a plurality of segments of data and multiplying by a coefficient 0.8, the number of continuous points being 1 exceeding the threshold value is counted, and the number of points is multiplied by the line interval The bandwidth of the signal can be obtained, and the number of times of appearing in each segment obtained by coarse estimation is easily affected by noise and multiple signals, causing the pulse width to be inaccurate, and secondary estimation is required on this basis.

[0010] (5) fine estimation of signal bandwidth and pulse width The approximate signal bandwidth and signal number can be obtained through coarse estimation, the values of the data points corresponding to the frequencies outside the K segments of data subjected to smoothing filtering are set to 0 according to the signal bandwidth, and then the average value is calculated by accumulating and summing the data of each segment, and the second detection threshold is obtained by multiplying the average value by another coefficient 0.5. The position value corresponding to the points exceeding the threshold is 1 by comparing the threshold with each segment of data subjected to filtering, and whether the signal appears on the corresponding frequency point is detected through the threshold to obtain more accurate signal appearing signal segment number, total segment number and total data time length, so as to fine estimate the pulse width. At the same time, fine analysis based on the slope change is carried out on the frequency spectrum subjected to filtering, accumulation and averaging, the maximum value is found in the average frequency spectrum, and the left and right sides are searched respectively, the bandwidth boundary is judged based on the slope change of the frequency spectrum curve, and the fine estimated signal bandwidth is determined until the slope change is less than the preset threshold.

[0011] The beneficial effects of the present application are as follows: 1. Strong anti-interference: through the analysis from time domain to frequency domain, and using multi-level adaptive threshold, the influence of noise is effectively suppressed; 2. Multi-signal detection capability: multiple different frequency signals can be detected in a wide range of bandwidth and pulse width at the same time; 3. High precision: through the iterative process of "rough estimation-precise estimation", the measurement precision of bandwidth and pulse width is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 The detection flowchart of the present application.

[0013] Figure 2 The spectrum diagram of the baseband signal in the embodiment of the present application.

[0014] Figure 3 The spectrum diagram of the signal after thresholding in the preliminary detection in the embodiment of the present application.

[0015] Figure 4 The signal spectrum result diagram obtained after precise estimation in the embodiment of the present application.

[0016] Figure 5 The pulse width detection result diagram obtained after precise estimation in the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0018] The parameters of a preferred embodiment of the present application are as follows: the sampling rate is 100MHz, the FFT point number M is 65536, and the total data time length is 200ms. The pulse width of the signal to be detected is 100ms, the bandwidth is 7.5KHz, and the actual signal spectrum diagram is as shown in Figure 2 .

[0019] (1) Calculate the baseband signal power spectrum The total data is divided into K segments. According to the downward rounding calculation of K= floor (N / M), wherein N is the total data point number, and M is the FFT point number 65536, K=609 is calculated. The length of each segment is M=65536, and the adjacent segments overlap M / 2=32768 points. After windowing each segment of data, FFT is performed, and the power spectrum {P}k[n] is calculated according to formula (1). The spectral line interval is 1525Hz calculated by formula (2).

[0020] (2) Data smoothing ​The 609 segment power spectrum data is respectively subjected to smoothing filtering processing to eliminate random interference and peak noise.

[0021] (3) First threshold estimation and detection The 609 segment smoothed power spectrum data is added at all corresponding frequency points, and then divided by 609 to obtain an average power spectrum. The mean value of the average power spectrum is calculated, and multiplied by a coefficient 1.5 (first coefficient) as a first detection threshold. Each frequency point value in each segment of the smoothed power spectrum is compared with the threshold, and the frequency points exceeding the threshold are marked as 1, otherwise as 0. The pulse width and the number of signals of the signal can be counted.

[0022] (4) Coarse estimation of signal bandwidth and pulse width The 609 segment smoothed data is accumulated to obtain an average value, and the number of continuous frequency points marked as 1 is counted after the average value is multiplied by a coefficient 0.8 (third coefficient). The coarse estimated bandwidth is obtained by multiplying the spectral line interval . In this embodiment, the signal bandwidth estimated by this step is about 38 kHz, which is greatly different from the actual value of 7.5 kHz, as shown in Figure 3 . At the same time, the number of data segments in which the frequency points marked as 1 exist in all 609 segments is counted, and the pulse width is coarsely estimated according to the number.

[0023] (5) Fine estimation of signal bandwidth and pulse width Frequency domain filtering: according to the coarse estimated signal bandwidth, the center frequency is located, and the data points outside the bandwidth range of each segment of the smoothed power spectrum are set to zero; Second estimation: the 609 segment data after zero setting is accumulated to obtain an average value, and the average value is multiplied by another coefficient 0.5 (second coefficient) to obtain a more accurate second detection threshold; Fine estimation of signal bandwidth: according to the preliminary estimated signal bandwidth, the center spectrum is found, the data is filtered by frequency domain filtering, the signal in the corresponding frequency range is found, and then the average value of the accumulated data is obtained to obtain a fine spectrum as shown in Figure 4 . In the effective frequency band of the spectrum, the maximum value point is found, and then the left and right sides are searched, and when the slope of the spectrum curve changes less than 0.2, the bandwidth edge is determined. The frequency difference between the left and right edges is the fine estimated bandwidth signal. The measured result of this embodiment is 7.6 kHz; Fine estimation of pulse width: each segment of the data after zero setting is compared with the second detection threshold to determine whether the segment has a signal. The number of segments N in which the signal exists is counted. The fine estimated pulse width is (N / 609)*200 ms. The measured result of this embodiment is 100.8 ms, as shown in Figure 5 .

[0024] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, and shall be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope of claims.

Claims

1. A signal bandwidth and pulse width detection method, characterized by, The method comprises the following steps: obtaining time domain data of a baseband signal; dividing the time domain data into K segments, each segment having a length of M and having aliasing between adjacent segments, performing Fourier transform on each segment of data and calculating power spectrum density to obtain K power spectrum estimation segments; performing smoothing filtering on the K power spectrum estimation segments respectively; based on the plurality of power spectrum estimation segments, performing multi-stage adaptive threshold detection to estimate bandwidth and pulse width of one or more signals.

2. The method of claim 1, wherein, The divided time domain data satisfies K = floor(N / M), wherein N is a total length of the data, M is a length of each piece of data, and M / 2 is an aliasing length of adjacent two pieces of data. the divided time domain data satisfies K = floor(N / M), wherein N is a total length of the data, M is a length of each piece of data, and M / 2 is an aliasing length of adjacent two pieces of data.

3. The method of claim 1, wherein, The multi-stage adaptive threshold detection comprises: first stage threshold detection: obtaining an average value by accumulating and averaging the K segments of data after smoothing filtering, multiplying the average value by a first coefficient to obtain a first detection threshold, comparing each point in each segment of data with the first detection threshold to obtain a first decision result, and performing coarse estimation of signal bandwidth and pulse width based on the first decision result; second stage threshold detection: performing frequency domain filtering on the K segments of data after smoothing filtering based on the signal bandwidth obtained by coarse estimation, retaining the frequency point data corresponding to the bandwidth, accumulating and averaging the K segments of filtered data, multiplying the average value by a second coefficient to obtain a second detection threshold, and performing detection on each segment of filtered data using the second detection threshold to obtain a second decision result, and performing fine estimation of signal bandwidth and pulse width based on the second decision result.

4. The method of claim 3, wherein, The coarse estimation of signal bandwidth and pulse width comprises: The number of continuous points with the first decision result of "1" in each segment of data is counted. If the number exceeds a threshold obtained by multiplying the average value of the statistical results of all segments of data by a third coefficient, it is determined that the segment has a signal. The number of continuous serial numbers of 1 exceeding the threshold is counted, and the number is multiplied by the spectral line interval to obtain a rough estimated signal bandwidth. , calculating the coarse estimated pulse width according to the number of segments in which signals are determined to exist, the total number of segments, and the total data time length.

5. The method of claim 3, wherein, The fine estimation of signal bandwidth comprises: obtaining an average spectrum by accumulating and averaging the K segments of filtered data; finding a maximum value in the average spectrum and searching to the left and right respectively, determining bandwidth boundaries based on the slope change of the spectrum curve until the slope change is less than a preset threshold, and determining the fine estimated signal bandwidth accordingly.

6. The method of claim 3, wherein, The fine estimation of pulse width comprises: counting the number of segments in which the second decision result is "1" according to the second decision result; calculating the fine estimated pulse width based on the number of segments, the total number of segments K, and the total data time length.

7. The method of claim 3, wherein The first coefficient is 1.5, and the second coefficient is 0.

5.

8. The method of claim 4, wherein The third coefficient is 0.8.

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