Frequency hopping signal parameter estimation method, device, equipment and storage medium

Abstract

The present invention relates to the field of signal processing technology, and provides a method, device, equipment and storage medium for estimating parameters of a frequency hopping signal. The method comprises: performing short-time Fourier transform on a received frequency hopping signal to obtain a time-frequency diagram; performing cumulative averaging and noise estimation on each segment of the power spectrum in the time-frequency diagram to obtain a noise estimation value; performing correction and smoothing processing on the time-frequency diagram based on the noise estimation value to obtain a smoothed power spectrum; calculating a decision threshold value based on the smoothed power spectrum, and performing threshold judgment on the smoothed power spectrum point by point to obtain a boundary index value of the frequency hopping signal that is not less than the decision threshold value; the boundary index value is a data point where the magnitude relationship between the smoothed power spectrum and the decision threshold value changes; and estimating the parameters of the frequency hopping signal based on the boundary index value. By performing threshold judgment on the smoothed power spectrum point by point to obtain the upper and lower boundaries of the bandwidth, the time resolution and frequency resolution when estimating the parameters of the frequency hopping signal are improved, thereby improving the parameter estimation accuracy of the frequency hopping signal.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, device, equipment and storage medium for estimating frequency hopping signal parameters. Background Art

[0002] With the development of wireless communication technology, frequency hopping spread spectrum (FHSS) is a modulation technology with strong anti-interference ability and high security, which is widely used in mobile communications and wireless sensor networks. Frequency hopping technology transmits signals by quickly switching between different frequencies, thereby reducing the risk of signal interference and eavesdropping. However, the complexity of frequency hopping signals also increases the difficulty of signal recognition and extraction.

[0003] The identification and extraction of frequency hopping signals mainly depends on the estimation of parameters such as bandwidth and center frequency of frequency hopping signals. At present, the existing parameter estimation methods of frequency hopping signals include spectrum analysis and time-frequency analysis. In the case of rapid frequency changes, spectrum analysis has insufficient time resolution and is difficult to accurately capture the instantaneous frequency changes of frequency hopping signals; time-frequency analysis is a compromise between time and frequency resolution, and it is difficult to achieve high time resolution and high frequency resolution at the same time, thereby improving the accuracy of frequency hopping signal parameter estimation. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for estimating parameters of a frequency hopping signal, which are used to solve the defect that the time resolution of the existing frequency hopping signal parameter estimation method is insufficient, thus affecting the estimation accuracy of the parameters of the frequency hopping signal.

[0005] The present invention provides a method for estimating frequency hopping signal parameters, comprising:

[0006] Performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal;

[0007] Performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal;

[0008] Correcting and smoothing the time-frequency graph based on the background noise estimation value to obtain a smoothed power spectrum;

[0009] Calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where a magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0010] Parameters of the frequency hopping signal are estimated based on the boundary index value.

[0011] According to the frequency hopping signal parameter estimation method provided by the present invention, the cumulative averaging and noise floor estimation of each power spectrum segment in the time-frequency diagram to obtain the noise floor estimation value of the frequency hopping signal includes:

[0012] Accumulate and average the data points of the same frequency domain in different time domains of each power spectrum in the time-frequency diagram to obtain an average power spectrum;

[0013] Based on a preset first window size and a first sliding step size, sliding window processing is performed on the average power spectrum. During the sliding window processing, morphological operations are performed on the data points in the window to obtain a background noise estimation value of the frequency hopping signal; the morphological operations include at least one of corrosion, expansion, opening operation and closing operation.

[0014] According to the frequency hopping signal parameter estimation method provided by the present invention, the smoothed power spectrum includes a first smoothed power spectrum and a second smoothed power spectrum; the time-frequency diagram is corrected and smoothed based on the background noise estimation value to obtain a smoothed power spectrum, including:

[0015] Subtracting the background noise estimation value from the average power spectrum to correct the time-frequency diagram to obtain a corrected power spectrum;

[0016] Based on the first window size and the first sliding step, as well as the preset second window size and the second sliding step, sliding window processing is performed on the modified power spectrum respectively. During the sliding window processing, the average value of the data points in the window is calculated to smooth the modified power spectrum to obtain a first smoothed power spectrum corresponding to the first window size and a second smoothed power spectrum corresponding to the second window size; the second window size is smaller than the first window size.

[0017] According to the frequency hopping signal parameter estimation method provided by the present invention, the boundary index value includes a left index value and a right index value; the decision threshold value is calculated according to the smoothed power spectrum, and threshold decision is performed point by point on the smoothed power spectrum based on the decision threshold value to obtain the boundary index value of the frequency hopping signal, including:

[0018] Adding the first smoothed power spectrum to a preset frequency value to obtain a decision threshold value;

[0019] Compare the second smoothed power spectrum with the decision threshold value point by point, and obtain the left index value where the second smoothed power spectrum jumps from not greater than the decision threshold value to not less than the decision threshold value, and the right index value where the second smoothed power spectrum jumps from not less than the decision threshold value to not greater than the decision threshold value;

[0020] If the left index value and / or the right index value does not exist in the second smooth power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy, and the step of comparing the second smooth power spectrum with the decision threshold value point by point is returned and executed until the left index value and the right index value are obtained.

[0021] According to the frequency hopping signal parameter estimation method provided by the present invention, the parameters of the frequency hopping signal include bandwidth and center frequency; the estimating the parameters of the frequency hopping signal based on the boundary index value includes:

[0022] Calculate the difference between the left index value and the right index value in the boundary index value, and determine the number of data points corresponding to the bandwidth of the frequency hopping signal according to the difference;

[0023] estimating a bandwidth of the frequency hopping signal based on the number of data points;

[0024] Calculating an average value of the left index value and the right index value, and determining a target index of the center frequency of the frequency hopping signal according to the average value;

[0025] A center frequency of the frequency hopping signal is estimated according to the target index.

[0026] According to the frequency hopping signal parameter estimation method provided by the present invention, the target index includes an integer part and a decimal part; and estimating the center frequency of the frequency hopping signal according to the target index includes:

[0027] Determine the frequency value corresponding to the integer part of the target index according to the corresponding relationship between the target index and the frequency axis of the time-frequency graph;

[0028] The sum of the frequency value and the decimal part of the target index is multiplied by the frequency resolution to obtain the center frequency of the frequency hopping signal; the frequency resolution is the ratio of the sampling rate of the frequency hopping signal to the length of the frequency axis.

[0029] According to the frequency hopping signal parameter estimation method provided by the present invention, the short-time Fourier transform is performed on the received frequency hopping signal to obtain the time-frequency diagram of the frequency hopping signal, including:

[0030] Obtaining N data points from the time-domain IQ data of the received frequency-hopping signal;

[0031] Performing short-time Fourier transform on the N data points to obtain N frequency domain data points of the frequency hopping signal;

[0032] The time domain signal length of the frequency hopping signal is used as the time axis length, and N is used as the frequency domain axis length, and the N frequency domain data points are mapped to obtain a time-frequency diagram of the frequency hopping signal.

[0033] The present invention also provides a frequency hopping signal parameter estimation device, comprising the following modules:

[0034] A time-frequency conversion module, used for performing a short-time Fourier transform on the received frequency-hopping signal to obtain a time-frequency diagram of the frequency-hopping signal;

[0035] A noise floor estimation module, used for performing cumulative averaging and noise floor estimation on each power spectrum segment in the time-frequency diagram to obtain a noise floor estimation value of the frequency hopping signal;

[0036] A correction and smoothing module, used for correcting and smoothing the time-frequency diagram based on the background noise estimation value to obtain a smoothed power spectrum;

[0037] A threshold decision module is used to calculate a decision threshold value according to the smooth power spectrum, and perform threshold decision on the smooth power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where the magnitude relationship between the smooth power spectrum and the decision threshold value changes;

[0038] A parameter estimation module is used to estimate the parameters of the frequency hopping signal based on the boundary index value.

[0039] The present invention also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-described frequency hopping signal parameter estimation methods when executing the computer program.

[0040] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned frequency hopping signal parameter estimation methods are implemented.

[0041] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned frequency hopping signal parameter estimation methods are implemented.

[0042] The frequency hopping signal parameter estimation method, device, equipment and storage medium provided by the present invention, based on the correction and smoothing of the time-frequency diagram of the frequency hopping signal, improves the processing accuracy when estimating the parameters of the frequency hopping signal by performing threshold judgment on the smoothed power spectrum point by point, improves the time resolution and frequency resolution of the frequency hopping signal parameter estimation, and thus improves the parameter estimation accuracy of the frequency hopping signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of a method for estimating frequency hopping signal parameters provided by an embodiment of the present invention.

[0045] Figure 2 It is a schematic diagram of a frequency hopping signal parameter estimation process provided by an embodiment of the present invention.

[0046] Figure 3 It is a schematic diagram of time domain data of a frequency hopping signal provided by an embodiment of the present invention.

[0047] Figure 4 It is a frequency domain data schematic diagram of a frequency hopping signal provided by an embodiment of the present invention.

[0048] Figure 5 It is a schematic diagram of a spectrum diagram of a frequency hopping signal provided by an embodiment of the present invention.

[0049] Figure 6 It is a schematic diagram of a modified power spectrum provided by an embodiment of the present invention.

[0050] Figure 7 It is a schematic diagram of threshold decision for right index value provided by an embodiment of the present invention.

[0051] Figure 8 It is a structural schematic diagram of a frequency hopping signal parameter estimation device provided by an embodiment of the present invention.

[0052] Fig. 9 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Figure 1 FIG. 1 is a flow chart of a method for estimating frequency hopping signal parameters provided by the present invention. Figure 1 As shown, the method comprises the following steps:

[0055] Step 100, performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal;

[0056] Step 200, performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal;

[0057] Step 300, correcting and smoothing the time-frequency diagram based on the background noise estimation value to obtain a smoothed power spectrum;

[0058] Step 400, calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where the magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0059] Step 500: Estimate parameters of the frequency hopping signal based on the boundary index value.

[0060] Perform short-time Fourier transform on the received frequency hopping signal to obtain the video image of the frequency hopping signal. The short-time Fourier transform is STFT (Short-Time Fourier Transform), and the transformation formula is as follows:

[0061] ; (1)

[0062] In Formula 1, x[n] is the time domain signal to be transformed, m is time, k is frequency, j is an imaginary unit, w[n] is a window function used to divide the signal to be transformed into shorter time periods, and N is the number of data points in each divided time period, that is, the signal length of a single short-time Fourier transform.

[0063] Optionally, the short-time Fourier transform of the frequency hopping signal can be real-time, performing the short-time Fourier transform on the frequency hopping signal while receiving it, or the frequency hopping signal can be received within a period of time and the received frequency hopping signal can be uniformly short-time Fourier transformed.

[0064] The power spectrum of each segment in the time-frequency diagram is cumulatively averaged and the noise floor is estimated to obtain the noise floor estimation value of the frequency hopping signal. The time-frequency diagram is constructed with time and frequency as coordinate axes. The cumulative average of the time-frequency diagram is the cumulative average of the power spectra of the same frequency point in different time periods, or the cumulative average of the power spectra of different frequency points at the same time point.

[0065] Furthermore, based on the time-frequency diagram after cumulative averaging, the noise floor of the frequency hopping signal is estimated to determine the noise floor estimation value of the frequency hopping signal, and the time-frequency diagram is corrected and smoothed based on the noise floor estimation value to obtain a smoothed power spectrum, i.e., a smoothed power spectrum.

[0066] The decision threshold is calculated according to the smoothed power spectrum, and the smoothed power spectrum is threshold-determined point by point based on the calculated decision threshold to obtain the boundary index value of the frequency hopping signal. The boundary index value is a data point in the frequency hopping signal that is not less than the decision threshold value, and the boundary index value is a data point where the magnitude relationship between the smoothed power spectrum and the decision threshold value changes.

[0067] Optionally, the decision threshold value can be calculated by the smoothed power spectrum according to a preset rule, and the calculated decision threshold value includes the threshold value corresponding to each data point in the smoothed power spectrum. The size relationship between the smoothed power spectrum and the decision threshold value changes, including the smoothed power spectrum jumping from greater than the decision threshold value to less than the decision threshold value, and the smoothed power spectrum jumping from less than the decision threshold value to greater than the decision threshold value. The boundary index value is the boundary where the size relationship between the smoothed power spectrum and the decision threshold value changes.

[0068] The parameters of the frequency hopping signal are estimated based on the boundary index value. Optionally, the data points corresponding to the boundary index value can represent the bandwidth start and end points of the frequency hopping signal. The bandwidth of the frequency hopping signal is estimated based on the bandwidth start and end points, and the center frequency of the frequency hopping signal is estimated to obtain the parameters of the frequency hopping signal.

[0069] In this embodiment, based on the correction and smoothing of the time-frequency diagram of the frequency hopping signal, threshold judgment is performed point by point on the smoothed power spectrum, thereby improving the processing accuracy of parameter estimation of the frequency hopping signal, improving the time resolution and frequency resolution of the frequency hopping signal parameter estimation, and thus improving the parameter estimation accuracy of the frequency hopping signal.

[0070] In one embodiment, the short-time Fourier transform of the frequency hopping signal is processed in segments. In step 100, the received frequency hopping signal is subjected to a short-time Fourier transform to obtain a time-frequency diagram of the frequency hopping signal, which specifically includes:

[0071] Step 101, obtaining N data points from time-domain IQ data of a received frequency hopping signal;

[0072] Step 102, performing short-time Fourier transform on the N data points to obtain N frequency domain data points of the frequency hopping signal;

[0073] Step 103: Using the time domain signal length of the frequency hopping signal as the time axis length and N as the frequency domain axis length, the N frequency domain data points are mapped to obtain a time-frequency diagram of the frequency hopping signal.

[0074] First, N data points are obtained from the time domain IQ data of the received frequency hopping signal. The data points in the time domain IQ data of the frequency hopping signal are obtained by sampling the frequency hopping signal based on a specific sampling rate, and N is the processing length of the configured short-time Fourier transform.

[0075] Optionally, if the received frequency hopping signal includes data points, and N data points are taken from the received frequency hopping signal in time sequence for short-time Fourier transform, and finally frequency domain data points to form a time-frequency diagram. In the time-frequency diagram, the length of the time axis is L, and the length of the frequency axis is N. N can be determined and configured according to the sampling rate of the time domain IQ data.

[0076] Based on this, when constructing the time-frequency diagram, the signal length of the frequency hopping signal is used as the time axis length, N is used as the frequency domain axis length, and N frequency domain data points are mapped to obtain the time-frequency diagram of the frequency hopping signal.

[0077] In some embodiments, the cumulative average of the time-frequency graph is to perform the cumulative average of the data points in the same frequency domain at different time domains, and perform the noise floor estimation based on the averaged power spectrum to obtain the noise floor estimation value of the frequency hopping signal. Specifically, step 200 also includes:

[0078] Step 201, accumulating and averaging the data points of the same frequency domain in different time domains of each power spectrum segment in the time-frequency diagram to obtain an average power spectrum;

[0079] Step 202, based on a preset first window size and a first sliding step size, sliding window processing is performed on the average power spectrum. During the sliding window processing, morphological operations are performed on the data points in the window to obtain a background noise estimation value of the frequency hopping signal; the morphological operations include at least one of corrosion, expansion, opening operation and closing operation.

[0080] The data points of the same frequency domain in different time domains in each segment of the power spectrum in the time-frequency diagram are accumulated and averaged to obtain the average power spectrum. For example, take N data points in P segment for accumulation, and the accumulation dimension is the data points of the same frequency domain in different time domains to obtain the average power spectrum. , the specific implementation method is shown in the following formula 2:

[0081] ; (2)

[0082] In the average power spectrum, each data point is the average value of P data points of the same frequency at different times corresponding to P segments of the power spectrum.

[0083] Furthermore, a sliding window processing method is adopted to perform background noise estimation based on the average power spectrum. Specifically, based on a preset first window size and a first sliding compensation, a sliding window processing is performed on the average power spectrum. During the sliding window processing, a morphological operation is performed on the data points in the window to obtain a background noise estimation value of the frequency hopping signal. The morphological operation includes at least one of corrosion, expansion, opening operation and closing operation. A one-dimensional morphological operation is adopted, and the amount of operation is much less than that of two-dimensional image processing.

[0084] In one embodiment, a window of a preset size is set, and one data point is slid each time as a first sliding step. During the sliding window processing, morphological operations are performed on the data points in the window.

[0085] For the erosion operation in morphological operation, the minimum value of the data in the window is taken, which can reduce the peak value and widen the valley. The operation method is shown in Formula 3:

[0086] ; (3)

[0087] For the dilation operation in morphological operation, the maximum value of the data points in the window is taken, which can reduce the valley value and widen the peak. The operation method is shown in Formula 4:

[0088] ; (4)

[0089] For the opening operation in morphological operation, the data points in the window are first corroded and then expanded, which can suppress the signal peak. The operation method is shown in Formula 5:

[0090] ; (5)

[0091] For the closing operation in morphological operation, the data points in the window are first expanded and then eroded, which can suppress the signal trough. The operation method is shown in Formula 6:

[0092] ; (6)

[0093] In formula 3-4, b is the data point in window B, and B represents the window size during morphological operation. Indicates the value of data point b. In one embodiment, before performing background noise estimation, it is also necessary to configure a possible maximum bandwidth within a frequency band for background noise estimation, and the maximum bandwidth is used to limit the upper limit of the bandwidth estimation value of the frequency hopping signal. Based on the maximum bandwidth, the average power spectrum obtained after cumulative averaging is used as the data before background noise estimation, and a morphological opening operation is performed by sliding window processing to obtain the background noise estimation value of the frequency hopping signal.

[0094] After obtaining the noise floor estimation value of the frequency hopping signal, the time-frequency diagram is corrected and smoothed according to the noise floor estimation value to obtain a smoothed power spectrum. The smoothing process also adopts a sliding window process, and different sizes of windows are used for smoothing respectively to obtain a first smoothed power spectrum and a second smoothed power spectrum with different smoothing degrees. Based on this, step 300 further includes:

[0095] Step 301, subtracting the background noise estimation value from the average power spectrum to correct the time-frequency diagram to obtain a corrected power spectrum;

[0096] Step 302, based on the first window size and the first sliding step, and the preset second window size and the second sliding step, respectively perform sliding window processing on the modified power spectrum. During the sliding window processing, calculate the average value of the data points in the window to smooth the modified power spectrum to obtain a first smoothed power spectrum corresponding to the first window size and a second smoothed power spectrum corresponding to the second window size; the second window size is smaller than the first window size.

[0097] The correction of the time-frequency diagram is performed on the basis of the average power spectrum. Specifically, the data before the noise floor estimation is subjected to morphological operations to obtain the noise floor estimation value, and then the noise floor estimation value is subtracted from the data before the noise floor estimation to obtain the corrected power spectrum data corresponding to the time-frequency diagram, that is, the corrected power spectrum. Among them, the data before the noise floor estimation is the average power spectrum. On the basis of the average power spectrum, the noise floor estimation value is subtracted to obtain the corrected power spectrum. In this way, no matter where the reference of the power spectrum in the time-frequency diagram is, the reference of the corrected power spectrum is at 0dB of the time-frequency diagram, which is convenient for subsequent threshold judgment.

[0098] Further, after obtaining the corrected power spectrum, the corrected power spectrum is smoothed. Specifically, based on the first window size and the first sliding step, the corrected power spectrum is subjected to sliding window processing, and during the sliding window processing, the average value of the data points in the window is calculated, so as to smooth the corrected power spectrum and obtain the first smoothed power spectrum under the first window size. Based on the preset second window size and the second sliding step, the corrected power spectrum is subjected to sliding window processing, and during the sliding window processing, the average value of the data points in the window is calculated, so as to smooth the corrected power spectrum and obtain the smoothed power spectrum under the second window size. The second window size is smaller than the first window size, and the second sliding step can be the same as or different from the first sliding step, and there is no specific limitation on this.

[0099] Optionally, a window of the same size as the morphological operation is used to smooth the modified power spectrum to obtain a first smoothed power spectrum, and a smaller window is added to smooth the modified power spectrum to obtain a spare second smoothed power spectrum. The smoothing process based on the first window size and the first sliding step and the smoothing process based on the second window size and the second sliding step can be performed in parallel or sequentially. When performed sequentially, the order is not strictly limited.

[0100] Among them, the sliding window processing method is also used. The difference between smoothing processing and morphological operation is that morphological operation is to calculate the extreme value of the data points in the window, while smoothing processing is to calculate the average value of the data points in the window. The smoothing processing method includes but is not limited to linear power spectrum and logarithmic power spectrum, and the specific method of smoothing processing is also not limited here.

[0101] In one embodiment, the boundary index value of the frequency hopping signal includes a left index value and a right index value, wherein the left index value is used to characterize the size relationship between the smoothed power spectrum and the decision threshold value from the smoothed power spectrum not greater than the decision threshold value to the mark when not less than the decision threshold value, and the right index value is used to characterize the size relationship between the smoothed power spectrum and the decision threshold value from the smoothed power spectrum not less than the decision threshold value to the mark when not greater than the decision threshold value. Based on this, step 400 specifically includes:

[0102] Step 401, adding the first smoothed power spectrum to a preset frequency value to obtain a decision threshold value;

[0103] Step 402, comparing the second smoothed power spectrum with the decision threshold value point by point, obtaining a left index value at which the second smoothed power spectrum jumps from not greater than the decision threshold value to not less than the decision threshold value, and a right index value at which the second smoothed power spectrum jumps from not less than the decision threshold value to not greater than the decision threshold value;

[0104] Step 403: If the left index value and / or the right index value does not exist in the second smooth power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy, and the step of comparing the second smooth power spectrum with the decision threshold value point by point is returned and executed until the left index value and the right index value are obtained.

[0105] The first smooth power spectrum is added to the preset frequency value to obtain a decision threshold value, and the second smooth power spectrum is compared with the decision threshold value point by point, so as to perform threshold decision on the second smooth power spectrum point by point, and obtain the left index value of the second smooth power spectrum whose size relationship with the decision threshold value changes from a bar of the second smooth power spectrum not greater than the decision threshold value to a bar of not less than the decision threshold value, and the right index value of the second smooth power spectrum changes from a bar of not less than the decision threshold value to a bar of not greater than the decision threshold value.

[0106] Furthermore, if there are no qualified left index values ​​and / or right index values ​​in the second smooth power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy, and the threshold decision is re-performed point by point on the second smooth power spectrum based on the adjusted decision threshold value until the left index value and the right index value are obtained.

[0107] In one embodiment, a preset frequency value, such as any value between 2 dB and 4 dB, is added to the first smoothed power spectrum after smoothing based on the larger window size, and based on the decision threshold value, a threshold decision is made point by point on the second smoothed power after smoothing based on the smaller window size to obtain left and right index values ​​that are not less than the decision threshold value. The left index value is a mark from not greater than to not less than the decision threshold value, and the right index value is a mark from not less than to not greater than the decision threshold value.

[0108] In another embodiment, based on the decision threshold value, threshold decision is performed point by point on the second smoothed power or the modified power spectrum to obtain the left and right index values ​​of the second smoothed power spectrum or the modified power spectrum. In some embodiments, based on the decision threshold value, threshold decision is first performed point by point on the second smoothed power. When there are no left and right index values ​​that meet the conditions in the second smoothed power spectrum, based on the decision threshold value, threshold decision is performed point by point on the modified power spectrum to obtain the left and right index values ​​of the modified power spectrum. If there are no left and right index values ​​that meet the conditions in the modified power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy. Based on the adjusted decision threshold value, threshold decision is continued to be performed point by point on the second smoothed power spectrum until a right index value that meets the conditions is obtained.

[0109] Optionally, when adjusting the decision threshold value, the preset adjustment strategy includes but is not limited to reducing the decision threshold value to 0.5 times of the original value, and using a threshold decision method of point-by-point comparison to find the upper and lower boundaries of the bandwidth of the possible frequency hopping signal. Optionally, through point-by-point threshold decision, the left and right index values ​​of one or more signal bandwidths can be obtained.

[0110] In one embodiment, the parameters to be estimated of the frequency hopping signal include bandwidth and center frequency, and step 500 may further include:

[0111] Step 501, calculating the difference between the left index value and the right index value in the boundary index value, and determining the number of data points corresponding to the bandwidth of the frequency hopping signal according to the difference;

[0112] Step 502, estimating the bandwidth of the frequency hopping signal based on the number of data points;

[0113] Step 503, calculating an average value of the left index value and the right index value, and determining a target index of the center frequency of the frequency hopping signal according to the average value;

[0114] Step 504: Estimate the center frequency of the frequency hopping signal according to the target index.

[0115] When estimating the parameters of the frequency hopping signal, first calculate the difference between the left index value and the right index value in the boundary index value, determine the number of data points corresponding to the bandwidth of the frequency hopping signal based on the difference, and estimate the bandwidth of the frequency hopping signal based on the number of data points.

[0116] Furthermore, an average value of the left index value and the right index value is calculated, a target index of the center frequency of the frequency hopping signal is determined according to the average value, and the center frequency of the frequency hopping signal is estimated according to the target index.

[0117] Optionally, the target index of the center frequency of the frequency hopping signal includes an integer part and a decimal part, and step 504 further includes:

[0118] Step 514, determining the frequency value corresponding to the integer part of the target index according to the corresponding relationship between the target index and the frequency axis of the time-frequency graph;

[0119] Step 524, multiply the sum of the frequency value and the decimal part of the target index by the frequency resolution to obtain the center frequency of the frequency hopping signal; the frequency resolution is the ratio of the sampling rate of the frequency hopping signal to the length of the frequency axis.

[0120] According to the correspondence between the target index and the frequency axis of the time-frequency diagram, the frequency value corresponding to the integer part of the target index is determined, and the sum of the frequency value and the decimal part of the target index is multiplied by the frequency resolution to obtain the center frequency of the frequency hopping signal. The frequency resolution of the frequency hopping signal is the ratio of the sampling rate of the frequency hopping signal to the length of the frequency axis.

[0121] In one embodiment, the number of data points included in the bandwidth can be obtained by subtracting the left index value plus one from the right index value, and multiplying it by the frequency resolution to estimate the bandwidth of the frequency hopping signal. , as shown in Formula 7:

[0122] ; (7)

[0123] in, is the right index value, is the left index value, is the frequency resolution of the frequency hopping signal, N is the length of the frequency axis, is the sampling rate of the frequency hopping signal.

[0124] Furthermore, the sum of the right index value plus the left index value divided by 2 can get the target index of the center frequency. , that is, the index of the center frequency is the average of the left index value and the right index value. Use the integer part of the target index to calculate the corresponding frequency value according to the corresponding relationship of the frequency axis, add the sum of the decimal parts, and multiply it by the product of the frequency resolution to get the estimated value of the center frequency. , as shown in Formula 8:

[0125] , ; (8)

[0126] In one embodiment, referring to Figure 2 The parameter estimation process of the frequency hopping signal shown in the figure performs short-time Fourier transform (STFT) on the received frequency hopping signal to generate a time-frequency diagram of the frequency hopping signal; performs cumulative averaging, noise floor estimation and correction on each power spectrum of the time-frequency diagram to obtain a corrected power spectrum; performs smoothing on each power spectrum of the time-frequency diagram based on the corrected power spectrum to obtain a smoothed power spectrum; performs threshold judgment and deduplication on each power spectrum of the time-frequency diagram based on the smoothed power spectrum to obtain left and right index values ​​of the time-frequency diagram; and estimates the parameters of the frequency hopping signal according to the left and right index values.

[0127] Optionally, the received frequency hopping signal is a frequency hopping signal of multiple network stations, such as Figure 3 and Figure 4 As shown, Figure 3 It is the time domain IQ data of the frequency hopping signal with 129 hops in 4 networks. Figure 4 yes Figure 3 The frequency domain data of the 129 hops of the 4 stations in the frequency hopping signal. For the time domain IQ data of the received frequency hopping signal, N data points are taken each time to perform short-time Fourier transform (STFT) to obtain the following Figure 5 The time-frequency diagram shown in FIG. 1 contains multiple power spectra. The data points in the same frequency domain at different time domains in the multiple power spectra are accumulated and averaged to obtain the average power spectrum. The average power spectrum is subjected to morphological operation by sliding window processing to obtain the noise floor estimation value of the frequency hopping signal.

[0128] Furthermore, based on the average power spectrum, the noise floor estimate is subtracted to obtain Figure 6The modified power spectrum shown in FIG. 1 uses a window of the same size as the morphological operation and a smaller window to smooth the modified power spectrum, respectively, to obtain a first smoothed power spectrum and a second smoothed power spectrum. Figure 7 In the threshold decision stage, the sum of the first smoothed power spectrum obtained by smoothing with a larger window and the preset frequency value is used as the decision threshold value, and the second smoothed power spectrum obtained by smoothing with a smaller window is subjected to threshold decision point by point, thereby obtaining the left index value in the second smoothed power spectrum that jumps from not greater than the decision threshold value to not less than the decision threshold value, and the right index value that jumps from not less than the decision threshold value to not greater than the decision threshold value.

[0129] Furthermore, if there are no left and right index values ​​that meet the conditions in the second smoothed power spectrum, the decision threshold is reduced to 0.5 times the original value, and then the second smoothed power spectrum is re-thresholded point by point, and the above steps are repeated to obtain the left and right index values ​​of one or more signal bandwidths, thereby finding the upper and lower boundaries of the possible channel bandwidth. Figure 7 In the example, a total of 5 left and right index values ​​of bandwidth signals are obtained.

[0130] In this embodiment, the noise floor of the frequency hopping signal is estimated and corrected by morphological operation, so that the corrected power spectrum reference is stabilized near 0dB, which improves the reliability of frequency hopping signal recognition. On this basis, the burr fluctuation is reduced by smoothing processing, and the spectrum stability and signal recognition accuracy are improved. Further, the threshold judgment is performed point by point on the smoothed power spectrum to ensure the detection accuracy of the frequency hopping signal, avoid repeated detection and misjudgment, and improve the time resolution and frequency resolution of the frequency hopping signal parameter estimation, thereby improving the accuracy of parameter estimation.

[0131] The frequency hopping signal parameter estimation device provided by the present invention is described below. The frequency hopping signal parameter estimation device described below and the frequency hopping signal parameter estimation method described above can be referred to each other.

[0132] Reference Figure 8 , a frequency hopping signal parameter estimation device provided by an embodiment of the present invention includes:

[0133] The time-frequency conversion module 10 is used to perform a short-time Fourier transform on the received frequency-hopping signal to obtain a time-frequency diagram of the frequency-hopping signal;

[0134] A noise floor estimation module 20, configured to perform cumulative averaging and noise floor estimation on each power spectrum segment in the time-frequency diagram to obtain a noise floor estimation value of the frequency hopping signal;

[0135] A correction and smoothing module 30, configured to correct and smooth the time-frequency diagram based on the background noise estimation value to obtain a smoothed power spectrum;

[0136] A threshold decision module 40 is used to calculate a decision threshold value according to the smoothed power spectrum, and perform threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where the magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0137] The parameter estimation module 50 is used to estimate the parameters of the frequency hopping signal based on the boundary index value.

[0138] In one embodiment, the background noise estimation module 20 is further used for:

[0139] Accumulate and average the data points of the same frequency domain in different time domains of each power spectrum in the time-frequency diagram to obtain an average power spectrum;

[0140] Based on a preset first window size and a first sliding step size, sliding window processing is performed on the average power spectrum. During the sliding window processing, morphological operations are performed on the data points in the window to obtain a background noise estimation value of the frequency hopping signal; the morphological operations include at least one of corrosion, expansion, opening operation and closing operation.

[0141] In one embodiment, the smoothed power spectrum includes a first smoothed power spectrum and a second smoothed power spectrum; the modified smoothing module 30 is further used for:

[0142] Subtracting the background noise estimation value from the average power spectrum to correct the time-frequency diagram to obtain a corrected power spectrum;

[0143] Based on the first window size and the first sliding step, as well as the preset second window size and the second sliding step, sliding window processing is performed on the modified power spectrum respectively. During the sliding window processing, the average value of the data points in the window is calculated to smooth the modified power spectrum to obtain a first smoothed power spectrum corresponding to the first window size and a second smoothed power spectrum corresponding to the second window size; the second window size is smaller than the first window size.

[0144] In one embodiment, the boundary index value includes a left index value and a right index value; the threshold decision module 40 is further used to:

[0145] Adding the first smoothed power spectrum to a preset frequency value to obtain a decision threshold value;

[0146] Compare the second smoothed power spectrum with the decision threshold value point by point, and obtain the left index value where the second smoothed power spectrum jumps from not greater than the decision threshold value to not less than the decision threshold value, and the right index value where the second smoothed power spectrum jumps from not less than the decision threshold value to not greater than the decision threshold value;

[0147] If the left index value and / or the right index value does not exist in the second smooth power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy, and the step of comparing the second smooth power spectrum with the decision threshold value point by point is returned and executed until the left index value and the right index value are obtained.

[0148] In one embodiment, the parameters of the frequency hopping signal include bandwidth and center frequency; the parameter estimation module 50 is further used for:

[0149] Calculate the difference between the left index value and the right index value in the boundary index value, and determine the number of data points corresponding to the bandwidth of the frequency hopping signal according to the difference;

[0150] estimating a bandwidth of the frequency hopping signal based on the number of data points;

[0151] Calculating an average value of the left index value and the right index value, and determining a target index of the center frequency of the frequency hopping signal according to the average value;

[0152] A center frequency of the frequency hopping signal is estimated according to the target index.

[0153] In one embodiment, the target index includes an integer part and a decimal part; the parameter estimation module 50 is further used to:

[0154] Determine the frequency value corresponding to the integer part of the target index according to the corresponding relationship between the target index and the frequency axis of the time-frequency graph;

[0155] The sum of the frequency value and the decimal part of the target index is multiplied by the frequency resolution to obtain the center frequency of the frequency hopping signal; the frequency resolution is the ratio of the sampling rate of the frequency hopping signal to the length of the frequency axis.

[0156] In one embodiment, the time-frequency conversion module 10 is further used for:

[0157] Obtaining N data points from the time-domain IQ data of the received frequency-hopping signal;

[0158] Performing short-time Fourier transform on the N data points to obtain N frequency domain data points of the frequency hopping signal;

[0159] The time domain signal length of the frequency hopping signal is used as the time axis length, and N is used as the frequency domain axis length, and the N frequency domain data points are mapped to obtain a time-frequency diagram of the frequency hopping signal.

[0160] Fig. 9 An example of a physical structure diagram of an electronic device is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930 and a communication bus 940, wherein the processor 910, the communication interface 920 and the memory 930 communicate with each other through the communication bus 940. The processor 910 may call the logic instructions in the memory 930 to execute the steps of the frequency hopping signal parameter estimation method, for example including:

[0161] Performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal;

[0162] Performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal;

[0163] Correcting and smoothing the time-frequency graph based on the background noise estimation value to obtain a smoothed power spectrum;

[0164] Calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where a magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0165] Parameters of the frequency hopping signal are estimated based on the boundary index value.

[0166] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0167] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can perform the steps of the frequency hopping signal parameter estimation method provided in the above embodiments, for example, including:

[0168] Performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal;

[0169] Performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal;

[0170] Correcting and smoothing the time-frequency graph based on the background noise estimation value to obtain a smoothed power spectrum;

[0171] Calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where a magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0172] Parameters of the frequency hopping signal are estimated based on the boundary index value.

[0173] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the steps of the frequency hopping signal parameter estimation method provided in the above embodiments, for example, including:

[0174] Performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal;

[0175] Performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal;

[0176] Correcting and smoothing the time-frequency graph based on the background noise estimation value to obtain a smoothed power spectrum;

[0177] Calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where a magnitude relationship between the smoothed power spectrum and the decision threshold value changes;

[0178] Parameters of the frequency hopping signal are estimated based on the boundary index value.

[0179] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0180] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating frequency hopping signal parameters, characterized in that: include: Performing short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal; Performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal; Correcting and smoothing the time-frequency graph based on the background noise estimation value to obtain a smoothed power spectrum; Calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where a magnitude relationship between the smoothed power spectrum and the decision threshold value changes; Parameters of the frequency hopping signal are estimated based on the boundary index value.

2. The method for estimating frequency hopping signal parameters according to claim 1, characterized in that: The step of performing cumulative averaging and noise estimation on each power spectrum segment in the time-frequency diagram to obtain a noise estimation value of the frequency hopping signal includes: Accumulate and average the data points of the same frequency domain in different time domains of each power spectrum in the time-frequency diagram to obtain an average power spectrum; Based on a preset first window size and a first sliding step size, sliding window processing is performed on the average power spectrum. During the sliding window processing, morphological operations are performed on the data points in the window to obtain a background noise estimation value of the frequency hopping signal; the morphological operations include at least one of corrosion, expansion, opening operation and closing operation.

3. The method for estimating frequency hopping signal parameters according to claim 2, characterized in that: The smoothed power spectrum includes a first smoothed power spectrum and a second smoothed power spectrum; the time-frequency diagram is corrected and smoothed based on the background noise estimation value to obtain a smoothed power spectrum, including: Subtracting the background noise estimation value from the average power spectrum to correct the time-frequency diagram to obtain a corrected power spectrum; Based on the first window size and the first sliding step, as well as the preset second window size and the second sliding step, sliding window processing is performed on the modified power spectrum respectively. During the sliding window processing, the average value of the data points in the window is calculated to smooth the modified power spectrum to obtain a first smoothed power spectrum corresponding to the first window size and a second smoothed power spectrum corresponding to the second window size; the second window size is smaller than the first window size.

4. The method for estimating frequency hopping signal parameters according to claim 3, characterized in that: The boundary index value includes a left index value and a right index value; the step of calculating a decision threshold value according to the smoothed power spectrum, and performing threshold decision on the smoothed power spectrum point by point based on the decision threshold value to obtain the boundary index value of the frequency hopping signal includes: Adding the first smoothed power spectrum to a preset frequency value to obtain a decision threshold value; Compare the second smoothed power spectrum with the decision threshold value point by point, and obtain the left index value where the second smoothed power spectrum jumps from not greater than the decision threshold value to not less than the decision threshold value, and the right index value where the second smoothed power spectrum jumps from not less than the decision threshold value to not greater than the decision threshold value; If the left index value and / or the right index value does not exist in the second smooth power spectrum, the decision threshold value is adjusted based on a preset adjustment strategy, and the step of comparing the second smooth power spectrum with the decision threshold value point by point is returned and executed until the left index value and the right index value are obtained.

5. The method for estimating frequency hopping signal parameters according to claim 1, characterized in that: The parameters of the frequency hopping signal include bandwidth and center frequency; and the estimating the parameters of the frequency hopping signal based on the boundary index value includes: Calculate the difference between the left index value and the right index value in the boundary index value, and determine the number of data points corresponding to the bandwidth of the frequency hopping signal according to the difference; estimating a bandwidth of the frequency hopping signal based on the number of data points; Calculating an average value of the left index value and the right index value, and determining a target index of the center frequency of the frequency hopping signal according to the average value; A center frequency of the frequency hopping signal is estimated according to the target index.

6. The method for estimating frequency hopping signal parameters according to claim 5, characterized in that: The target index includes an integer part and a decimal part; and estimating the center frequency of the frequency hopping signal according to the target index includes: Determine the frequency value corresponding to the integer part of the target index according to the corresponding relationship between the target index and the frequency axis of the time-frequency graph; The sum of the frequency value and the decimal part of the target index is multiplied by the frequency resolution to obtain the center frequency of the frequency hopping signal; the frequency resolution is the ratio of the sampling rate of the frequency hopping signal to the length of the frequency axis.

7. The method for estimating frequency hopping signal parameters according to claim 1, characterized in that: The step of performing a short-time Fourier transform on the received frequency hopping signal to obtain a time-frequency diagram of the frequency hopping signal comprises: Obtaining N data points from the time-domain IQ data of the received frequency-hopping signal; Performing short-time Fourier transform on the N data points to obtain N frequency domain data points of the frequency hopping signal; The time domain signal length of the frequency hopping signal is used as the time axis length, and N is used as the frequency domain axis length, and the N frequency domain data points are mapped to obtain a time-frequency diagram of the frequency hopping signal.

8. A frequency hopping signal parameter estimation device, characterized in that: include: A time-frequency conversion module, used for performing short-time Fourier transform on the received frequency-hopping signal to obtain a time-frequency diagram of the frequency-hopping signal; A noise floor estimation module, used for performing cumulative averaging and noise floor estimation on each power spectrum segment in the time-frequency diagram to obtain a noise floor estimation value of the frequency hopping signal; A correction and smoothing module, used for correcting and smoothing the time-frequency diagram based on the background noise estimation value to obtain a smoothed power spectrum; A threshold decision module is used to calculate a decision threshold value according to the smooth power spectrum, and perform threshold decision on the smooth power spectrum point by point based on the decision threshold value to obtain a boundary index value of the frequency hopping signal; the boundary index value is not less than the decision threshold value, and the boundary index value is a data point where the magnitude relationship between the smooth power spectrum and the decision threshold value changes; A parameter estimation module is used to estimate the parameters of the frequency hopping signal based on the boundary index value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the frequency hopping signal parameter estimation method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for estimating frequency hopping signal parameters as claimed in any one of claims 1 to 7 are implemented.

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