A method for estimating signal characteristic parameters based on wavelet decomposition

Abstract

The present invention provides a method for estimating signal characteristic parameters based on wavelet decomposition, belonging to the field of signal time-frequency analysis. This method obtains the high-frequency components of the signal in the time domain through wavelet decomposition, then calculates the average noise amplitude of the high-frequency components using a histogram statistical sorting method. The start and end positions of the burst signal are determined using a time-domain amplitude threshold method. Finally, parameters such as the hold time, time interval, and frequency domain bandwidth of the burst signal are calculated based on the start and end positions, providing more information for signal classification. This method is applicable to signals whose center frequency does not hop, or frequency-hopping signals whose center frequency is centered and whose hopping range is no more than one-quarter of the monitoring bandwidth. It consumes few resources and is simple to implement.

Description

Technical Field

[0001] The present invention belongs to the field of signal time-frequency analysis, and in particular relates to a signal characteristic parameter estimation method based on wavelet decomposition. Background Art

[0002] With the development of electronic countermeasures and anti-interference communications, communication signal detection, recognition, and blind detection and recognition under non-cooperative conditions are becoming increasingly important. Traditional recognition methods relying solely on the time domain or frequency domain can no longer meet the needs of signal detection. Therefore, multi-domain joint recognition methods in the time domain, frequency domain, and space domain have emerged.

[0003] Currently, most existing technologies use methods such as wavelet transform, energy detection, EMD, and short-time Fourier transform (STFT). Energy detection is weak for detecting short-duration burst signals; EMD is complex to implement, consumes large amounts of resources, and cannot achieve real-time calculations; and the STFT method, once the time window is determined, its time and frequency resolutions are already determined, making it incapable of multi-resolution analysis. Furthermore, wavelet transforms are computationally intensive, consume a lot of resources, and take a long time to complete a single signal detection, making them unsuitable for real-time signal detection scenarios. Summary of the Invention

[0004] To address these shortcomings, the present invention provides a method for estimating signal characteristic parameters based on wavelet decomposition. This method is applicable to signals whose center frequency does not hop, or to frequency-hopping signals whose center frequency hops within a range no greater than one-quarter of the monitoring bandwidth. It consumes few resources and is simple to implement, providing more robust evidence for signal classification.

[0005] The object of the present invention is achieved through the following solutions:

[0006] A method for estimating signal characteristic parameters based on wavelet decomposition comprises the following steps:

[0007] Step 1, receiving tracking signals frame by frame;

[0008] Step 2: Process each frame of signal, find the positions of the first and second occurrences of the signal, and obtain the time length, signal bandwidth, and time interval of the signal.

[0009] Furthermore, the specific method of step 2 is:

[0010] Step 201: Convert the tracking signal from analog to digital. The sampling frequency of the digital signal is ;

[0011] Step 202: Perform digital down-conversion on the digital signal to obtain a digital baseband time domain signal y=[ 、 、 … ], where 0, 1, …, 2n are the number of signal points, with a total of N frequency points, N = 2n + 1;

[0012] Step 203: Select the wavelet function db7 and obtain the decomposition high-pass filter Hi_D_filter according to the Mallat algorithm;

[0013] Step 204: Convolve the digital baseband time domain signal with Hi_D_filter to obtain a high frequency component cD1=[ 、 … ], cD1 contains a total of Q elements, Q=n+1;

[0014] Step 205: Calculate the time domain signal amplitude of each point i in the high frequency component cD1 , i=0,2,…,2n;

[0015] Step 206: Find the maximum value of the time domain signal amplitude and minimum value , and calculate the range ;

[0016] Step 207: Divide the Q time domain signal amplitudes into 64 spaces. The space number is ,in, To divide the gradient;

[0017] Step 208: Statistical space The number of amplitudes in and the average amplitude ;

[0018] Step 209, according to the histogram statistics method, obtain the maximum value that meets the following conditions value:

[0019]

[0020] And calculate the average amplitude obtained under this condition:

[0021]

[0022] Where m is the number of cumulative amplitudes that meet the conditions;

[0023] Step 210: Set threshold , threshold factor The value of is 2.5~3, and the amplitudes of Q time domain signals are compared with the threshold one by one in order. The comparison is made, and the time domain signal amplitude greater than T is used as the sign of the start or end of the signal. The index corresponding to the time domain signal amplitude when the signal first starts and ends is recorded as and , the index corresponding to the time domain signal amplitude when the signal starts for the second time is recorded as The index refers to a unique serial number set for each frequency point in the digital baseband time domain signal starting from the first frame signal;

[0024] Step 211, calculate the duration of the signal appearance:

[0025]

[0026] Among them, U represents the index The frame number and index of the signal frame The difference in the frame number of the signal frame;

[0027] Step 212, intercept the index To index If the digital baseband time domain signal data between If it is not an integer power of 2, add 0 to the end of the intercepted data until the data length is , k is an integer; perform FFT operation on the obtained data, and obtain the power spectrum of each point according to the FFT operation result ,t=0,1,…, , the statistical power spectrum is greater than The frequency point number Q, finally get the signal bandwidth ;

[0028] Step 213, calculate the time interval between signal appearances:

[0029]

[0030] Among them, M is the index The signal frame to index The total number of frames in the signal frame;

[0031] At this point, the signal characteristic parameters are obtained 、 and .

[0032] Compared with the background technology, the present invention has the following advantages:

[0033] 1. This invention adopts the histogram statistical sorting method, and the calculation time complexity is , high computational efficiency and good real-time performance.

[0034] 2. The present invention adopts a wavelet decomposition algorithm to obtain the holding time and time interval parameters of the burst signal. This method consumes few resources and is simple to implement.

[0035] 3. The present invention combines signal time positioning and uses the FFT method to obtain the frequency domain bandwidth parameters of the burst signal, which is simple and easy.

[0036] 4. The present invention is applicable to signals whose center frequency does not jump, or frequency hopping signals whose center frequency jumps within a range not exceeding one-quarter of the monitoring bandwidth, and can provide more basis for signal classification. DETAILED DESCRIPTION

[0037] The present invention is described in further detail below.

[0038] A signal characteristic parameter estimation method based on wavelet decomposition. This method obtains the high-frequency components of the signal in the time domain through wavelet decomposition, then calculates the average amplitude of the high-frequency component noise using a histogram statistical sorting method. The start and end positions of the burst signal are determined using a time domain amplitude threshold method. Finally, the burst signal parameters such as the hold time, time interval, and frequency domain bandwidth are calculated based on the start and end positions. The specific steps are as follows:

[0039] Step 1, receiving tracking signals frame by frame;

[0040] Step 2: Process each frame of signal, find the positions of the first and second occurrences of the signal, and obtain the signal duration, signal bandwidth, and time interval. The specific method is as follows:

[0041] Step 201: Convert the tracking signal from analog to digital. The sampling frequency of the digital signal is ;

[0042] Step 202: Perform digital down-conversion on the digital signal in the FPGA to obtain a digital baseband time domain signal y = [ 、 、 … ], where 0, 1, …, 2n are the number of signal points, with a total of N frequency points, N = 2n + 1;

[0043] Step 203: Select the wavelet function "db7" and obtain the wavelet filter according to the Mallat algorithm: decompose the high-pass filter Hi_D_filter;

[0044] Step 204: Convolve the digital baseband time domain signal with Hi_D_filter to obtain a high frequency component cD1=[ 、 … ], cD1 contains a total of Q elements, Q=n+1;

[0045] Step 205: Calculate the time domain signal amplitude of each point i in the high frequency component cD1 , i=0,2,…,2n;

[0046] Step 206: Find the maximum value of the time domain signal amplitude and minimum value , and calculate the range ;

[0047] Step 207: Divide the Q time domain signal amplitudes into 64 spaces. The space number is ,in, To divide the gradient, when hour, is 1dB, when hour, ;

[0048] Step 208: Statistical space The number of amplitudes in and the average amplitude ;

[0049] Step 209, according to the histogram statistics method, obtain the maximum value that meets the following conditions value:

[0050]

[0051] And calculate the average amplitude obtained under this condition :

[0052]

[0053] Where m is the number of cumulative amplitudes that meet the conditions;

[0054] It can be updated once when processing each frame of signal, or other update frequencies can be set according to project requirements;

[0055] Step 210: Set threshold , threshold factor The value of is 2.5~3, and the amplitudes of Q time domain signals are compared with the threshold one by one in order. For comparison, the time domain signal amplitude greater than T is used as the sign of the start or end of the signal, that is, when the time domain signal amplitude is greater than T for the first time, it indicates the start of the signal; when the time domain signal amplitude is greater than T for the second time, it indicates the end of the signal; when the time domain signal amplitude is greater than T for the third time, it indicates the start of the next signal, and so on;

[0056] The index corresponding to the amplitude of the time domain signal at the first start and end of the signal is recorded as and , the index corresponding to the time domain signal amplitude when the signal starts for the second time is recorded as The index refers to a unique serial number set for each frequency point in the digital baseband time domain signal starting from the first frame signal;

[0057] Step 211, calculate the duration of the signal appearance:

[0058]

[0059] Among them, U represents the index The frame number and index of the signal frame The difference in the frame number of the signal frame; for example, index Appears in the 3rd frame signal, index It appears in the 5th frame signal, so U=5-3=2.

[0060] Step 212, intercept the index To index If the digital baseband time domain signal data between If it is not an integer power of 2, the principle of padding with 0 values is adopted, and 0 is added to the end of the intercepted data until the data length is , k is an integer; perform FFT operation on the obtained data, and obtain the power spectrum of each point according to the FFT operation result ,t=0,1,…, , the statistical power spectrum is greater than The frequency point number Q, finally get the signal bandwidth ;

[0061] Step 213, calculate the time interval between signal appearances:

[0062]

[0063] Among them, M is the index The signal frame to index The total number of frames in the signal frame; for example, index Appears in the 5th frame signal, index It appears in the 8th frame signal, so M=8-5+1=4.

[0064] At this point, the signal characteristic parameters are obtained 、 and .

[0065] The present invention has the characteristics of low resource consumption, simple implementation, etc., and can provide more basis for signal classification.

Claims

1. A signal characteristic parameter estimation method based on wavelet decomposition, characterized in that: The following steps are involved: Step 1, receiving tracking signals frame by frame; Step 2: Process each frame of signal, find the positions of the first and second occurrences of the signal, and obtain the signal duration, signal bandwidth, and time interval. The specific method is as follows: Step 201: Convert the tracking signal from analog to digital. The sampling frequency of the digital signal is ; Step 202: Perform digital down-conversion on the digital signal to obtain a digital baseband time domain signal y=[ 、 、 … ], where 0, 1, …, 2n are the number of signal points, with a total of N frequency points, N = 2n + 1; Step 203: Select the wavelet function db7 and obtain the decomposition high-pass filter Hi_D_filter according to the Mallat algorithm; Step 204: Convolve the digital baseband time domain signal with Hi_D_filter to obtain a high frequency component cD1=[ 、 … ], cD1 contains a total of Q elements, Q=n+1; Step 205: Calculate the time domain signal amplitude of each point i in the high frequency component cD1 , i=0,2,…,2n; Step 206: Find the maximum value of the time domain signal amplitude and minimum value , and calculate the range ; Step 207: Divide the Q time domain signal amplitudes into 64 spaces. The space number is ,in, To divide the gradient; Step 208: Statistical space The number of amplitudes in and the average amplitude ; Step 209, according to the histogram statistics method, obtain the maximum value that meets the following conditions value: And calculate the average amplitude obtained under this condition: Where m is the number of cumulative amplitudes that meet the conditions; Step 210: Set threshold , threshold factor The value of is 2.5~3, and the amplitudes of Q time domain signals are compared with the threshold one by one in order. The comparison is made, and the time domain signal amplitude greater than T is used as the sign of the start or end of the signal. The index corresponding to the time domain signal amplitude when the signal first starts and ends is recorded as and , the index corresponding to the time domain signal amplitude when the signal starts for the second time is recorded as The index refers to a unique serial number set for each frequency point in the digital baseband time domain signal starting from the first frame signal; Step 211, calculate the duration of the signal appearance: Among them, U represents the index The frame number and index of the signal frame The difference in the frame number of the signal frame; Step 212, intercept the index To index If the digital baseband time domain signal data between If it is not an integer power of 2, add 0 to the end of the intercepted data until the data length is , k is an integer; perform FFT operation on the obtained data, and obtain the power spectrum of each point according to the FFT operation result ,t=0,1,…, , the statistical power spectrum is greater than Frequency points , and finally the signal bandwidth is obtained ; Step 213, calculate the time interval between signal appearances: Among them, M is the index The signal frame to index The total number of frames in the signal frame; At this point, the signal characteristic parameters are obtained 、 and .

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