An improved Doppler signal processing method and system

Abstract

The present invention discloses an improved Doppler signal processing method and system, belonging to the field of Doppler signal processing technology, comprising the following steps: Step S1, signal acquisition and preprocessing: acquiring the original Doppler signal and performing a sliding average filter; Step S2, time-frequency analysis: using a Hamming window function, an improved wavelet threshold denoising method, and combining a short-time Fourier transform to perform time-domain analysis on the filtered Doppler signal to obtain a motion spectrum; Step S3, feature extraction: using a Hilbert transform to calculate the true envelope and combining a peak-finding function based on envelope analysis to traverse the data in the motion spectrum to extract peak points; Step S4, judgment output: screening the peak points that meet the peak height threshold condition and outputting the peak points and their position coordinates. The present invention achieves better signal extraction effect and visual speed measurement, effectively realizes signal separation and extraction, and significantly improves the accuracy and reliability of signal processing.

Description

Technical Field

[0001] The present invention relates to the technical field of Doppler signal processing, and in particular to an improved Doppler signal processing method and system. Background Art

[0002] Traditional Doppler signal processing methods mainly include spectrum analysis, frequency tracking, counting technology, photon correlation technology and Fourier transform method. In complex measurement environments, such as multipath propagation and noise interference, a single signal processing method is often unable to effectively cope with various signal characteristics and noise types. As a result, the characteristics of the Doppler signal may vary depending on the measurement object, environmental conditions and sensor characteristics. Therefore, a single method is difficult to adapt to all situations.

[0003] Among them, Fourier transform, as a basic method in the field of signal processing, has been widely used in Doppler signal processing. From the initial discrete Fourier transform (DFT) to the later fast Fourier transform (FFT), since Fourier transform is a global transform, it cannot provide local information of time and frequency at the same time and is difficult to process non-stationary signals. When processing Doppler signals, when the target motion state changes, Fourier transform cannot accurately reflect the real-time changes in signal frequency. Summary of the Invention

[0004] The purpose of the present invention is to provide an improved Doppler signal processing method and system, aiming to effectively extract key features in the signal and suppress noise interference.

[0005] To achieve the above object, the present invention provides an improved Doppler signal processing method, comprising the following steps:

[0006] Step S1, signal acquisition and preprocessing: acquiring the original Doppler signal and performing sliding average filtering;

[0007] Step S2, time-frequency analysis: using Hamming window function, improved wavelet threshold denoising and short-time Fourier transform to perform time domain analysis on the filtered Doppler signal to obtain a motion spectrum diagram;

[0008] Step S3, feature extraction: using Hilbert transform to calculate the true envelope and combining it with a peak-finding function based on envelope analysis to traverse the data in the motion spectrum to extract the peak points;

[0009] Step S4: Determine and output: Filter the peak points that meet the peak height threshold condition, and output the peak points and their position coordinates.

[0010] Preferably, step S1 specifically includes:

[0011] Step S11, setting the sampling rate to 200 MHz, collecting the original Doppler signal and inputting it;

[0012] Step S12: Set the window length to 9 and perform sliding average filtering on the input original Doppler signal. This process suppresses random noise while retaining the signal trend characteristics. The output of the sliding average filter is The expression is:

[0013] (1);

[0014] in, Indicates the current moment in the time series, and its value range is , is the signal length, is the length of the moving average filter, the moving average filter calculation time The current point and the front The average value of the points is taken as the signal value after filtering. is the average coefficient, is the input signal, is the index variable in the sum operation and , by changing Value, select the input signal value in turn The values at different times are accumulated.

[0015] Preferably, step S2 specifically includes:

[0016] Step S21: Process the filtered Doppler signal using a Hamming window function to suppress the edge effect of the Doppler signal. The definition of the Hamming window function is as follows:

[0017] (2);

[0018] in, is the serial number of the current sampling point, and its value range is , is the length of the Hamming window function;

[0019] Step S22: Dynamically denoise the Doppler signal obtained after processing in step S21 using improved wavelet threshold denoising. Different frequency components in the Doppler signal obtained after processing in step S21 are separated by the multi-scale decomposition capability of wavelet. The formula of the improved wavelet threshold denoising function is as follows:

[0020] (3);

[0021] in, is the output signal, is the input signal value, is the absolute value of the input signal, is the symbolic function defined, according to the input signal The positive or negative value of the output signal is determined The symbol, is the threshold, α and β are adjustment parameters, α controls the contraction strength and affects the compression amplitude of the output signal, β controls the smoothness and affects the adjustment of the signal in the transition area;

[0022] Step S23, the Doppler signal obtained after the processing in step S22 is subjected to segmented windowing and short-time Fourier transform, the time-frequency characteristics of the Doppler signal are analyzed, the frequency characteristics of the Doppler signal varying with time are captured, and the original spectrum is obtained. The formula for windowing using the window function is:

[0023] (4);

[0024] in, is the signal sequence, Represents the input signal At the current moment The value of is the window function in the signal sequence The starting position mark on Indicates the input signal Perform segmented windowing. Window expression for the signal sequence, is the length of each signal sequence;

[0025] Short-time Fourier transform is used to capture non-stationary signals and mutation signals, and the expression is:

[0026] (5);

[0027] in, represents the result of short-time Fourier transform, For the current moment, Is the signal sequence The index parameter for even-odd segment calculation, is the window function in the signal sequence The starting position on is the frequency index, used to determine the frequency position, is the signal length, For about Odd sequence Point transformation, For about even sequence of Point transformation;

[0028] Step S24: Processing the original spectrum using a spectrum optimization method based on background spectrum correction;

[0029] Step S241: Collect a static spectrum without information about moving objects as a base spectrum. ;

[0030] Step S242: Dynamic spectrum with motion information Perform difference calculation to obtain the true motion spectrum The formula is as follows:

[0031] (6).

[0032] Preferably, step S3 specifically includes:

[0033] Step S31: After short-time Fourier transform, the real motion spectrum The first frequency point in The corresponding value is 0, which means , to remove the DC component of the Doppler signal;

[0034] Step S32: Calculate the Doppler signal after removing the DC component using Hilbert transform. The coefficient of Hilbert transform is , is a frequency variable used to determine The value of When , the negative frequencies in the frequency domain signal move forward Phase, when When , the positive frequencies in the frequency domain signal move backward Phase to obtain a complex signal, and then modulus the obtained complex signal to obtain the true envelope of the frequency domain signal. The formula for the value of is as follows:

[0035] (7);

[0036] Where, is an imaginary unit;

[0037] Step S33: Set a peak height threshold. Use the peak-finding function to traverse the entire real motion spectrum data and determine whether each data satisfies the peak height threshold condition. The data that satisfies the peak height threshold condition is the peak point. The method for setting the peak height threshold is as follows: take the absolute value of the calculated real envelope. Among the real envelope data after taking the absolute value, the data that is greater than 0.5 times the envelope value of all other data is set as the peak height threshold. The formula for screening peak points using the peak height threshold is:

[0038] (8);

[0039] in, represents the peak height threshold; represents the true envelope signal, is the absolute value function The abbreviation of Indicates taking the absolute value of the true envelope signal;

[0040] The output value of the peak finding function is , the formula is as follows:

[0041] (9);

[0042] is the index of the real motion spectrum data. It points to each data point in turn during the traversal of the entire real motion spectrum data. For spectrum data The value at point and yes When the adjacent data points When the output is 1, The point is the peak point. When the output is 0, The point is not a peak point.

[0043] Preferably, after step S4, the method further includes building a single-point laser Doppler velocimetry system to conduct a disk rotation velocimetry experiment to verify the performance of the improved Doppler signal processing method.

[0044] A system for executing an improved Doppler signal processing method comprises a preprocessing module, a time-frequency analysis module, and a feature extraction module connected in sequence. The preprocessing module comprises an original signal input module for acquiring Doppler signals using a set sampling frequency, and a sliding average filtering module for filtering the acquired Doppler signals using a set window length. The time-frequency analysis module comprises a Hamming window function processing module capable of suppressing signal edge effects, an improved wavelet threshold denoising module for dynamically denoising the Doppler signals, and a short-time Fourier transform module for capturing the frequency characteristics of the Doppler signals. The feature extraction module comprises a Doppler peak extraction module based on envelope analysis and then utilizing Hilbert transform, and a spectrum feature output module for outputting peak coordinates corresponding to effective frequency points. The original signal input module, the sliding average filtering module, the Hamming window function processing module, the improved wavelet threshold denoising module, the short-time Fourier transform module, the Doppler peak extraction module, and the spectrum feature output module are connected in sequence.

[0045] Therefore, the improved Doppler signal processing method and system adopted by the present invention have the following beneficial effects:

[0046] 1) To meet the stringent requirements for high precision in Doppler velocity measurement, an innovative improved wavelet threshold denoising method is proposed. This method dynamically adjusts the local characteristics or noise level of the Doppler signal, achieving effective signal separation and extraction, and significantly improving the accuracy and reliability of signal processing.

[0047] 2) With short-time Fourier transform as the core, combined with sliding average filtering, Hamming window function, improved wavelet threshold denoising, and peak extraction technology, a complete laser Doppler velocimetry signal processing method is formed. Compared with a single processing method, this method can achieve accurate extraction of Doppler peak signals and visualized velocimetry.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of a signal processing structure according to an embodiment of the present invention;

[0050] Figure 2 A comparison diagram of the improved threshold function curves according to an embodiment of the present invention;

[0051] Figure 3 Flowchart of a peak extraction algorithm according to an embodiment of the present invention;

[0052] Figure 4 A user interface diagram of an embodiment of the present invention, wherein (a) is the original diagram, and (b) is the fast Fourier transform and envelope diagram;

[0053] Figure 5 Spectrum comparison diagrams obtained by a single-point laser Doppler velocimeter (LDV) system according to an embodiment of the present invention, where (a) is a conventional fast Fourier transform spectrum diagram and (b) is a spectrum diagram using an improved Doppler signal processing method. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0055] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0056] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the present invention provides an improved Doppler signal processing method, comprising the following steps: step S1, signal acquisition and preprocessing: acquiring the original Doppler signal and performing a sliding average filter; step S2, time-frequency analysis: using a Hamming window function, an improved wavelet threshold denoising method and combining short-time Fourier transform to perform time domain analysis on the filtered Doppler signal to obtain a motion spectrum; step S3, feature extraction: using Hilbert transform to calculate the true envelope and combining a peak search function based on envelope analysis to traverse the data in the motion spectrum to extract peak points; step S4, judgment output: screening the peak points that meet the peak height threshold condition and outputting the peak points and their position coordinates.

[0058] Step S1 specifically includes:

[0059] Step S11, setting the sampling rate to 200 MHz, collecting the original Doppler signal and inputting it;

[0060] Step S12, set the window length to 9, and perform sliding average filtering on the input original Doppler signal. This process suppresses random noise while retaining the signal trend characteristics. Set the length of a moving average filter to , the output is , Expressed as:

[0061] (1);

[0062] in, Indicates the current moment in the time series, and its value range is , is the signal length, is the length of the moving average filter, the moving average filter calculation time The current point and the front The average value of the points is taken as the signal value after filtering. is the average coefficient, is the input signal, is the index variable in the sum operation and , by changing Value, select the input signal value in turn The values at different times are accumulated.

[0063] Step S2 specifically includes:

[0064] Step S21: Process the filtered Doppler signal using a Hamming window function to suppress the edge effect of the Doppler signal. The definition of the Hamming window function is as follows:

[0065] (2);

[0066] in, is the serial number of the current sampling point, and its value range is , is the length of the Hamming window function;

[0067] Step S22: Dynamically denoise the Doppler signal obtained after processing in step S21 using improved wavelet threshold denoising. Different frequency components in the Doppler signal obtained after processing in step S21 are separated by the multi-scale decomposition capability of wavelet. The formula of the improved wavelet threshold denoising function is as follows:

[0068] (3);

[0069] in, is the output signal, is the input signal value, is the absolute value of the input signal, is the symbolic function defined, according to the input signal The positive or negative value of the output signal is determined The symbol, is the threshold, α and β are adjustment parameters, α controls the contraction strength and affects the compression amplitude of the output signal, β controls the smoothness and affects the adjustment of the signal in the transition area.

[0070] Step S23, the Doppler signal obtained after the processing in step S22 is subjected to segmented windowing and short-time Fourier transform, the time-frequency characteristics of the Doppler signal are analyzed, the frequency characteristics of the Doppler signal varying with time are captured, and the original spectrum is obtained. The formula for windowing using the window function is:

[0071] (4);

[0072] in, is the signal sequence, Represents the input signal At the current moment The value of is the window function in the signal sequence The starting position mark on Indicates the input signal Perform segmented windowing. Window expression for the signal sequence, is the length of each signal sequence;

[0073] Short-time Fourier transform is used to capture non-stationary signals and mutation signals, and the expression is:

[0074] (5);

[0075] in, represents the result of short-time Fourier transform, For the current moment, Is the signal sequence The index parameter for even-odd segment calculation, is the window function in the signal sequence The starting position on is the frequency index, used to determine the frequency position, is the signal length, For about Odd sequence Point transformation, For about even sequence of Point transformation;

[0076] Step S24: Processing the original spectrum using a spectrum optimization method based on background spectrum correction;

[0077] Step S241: Collect a static spectrum without information about moving objects as a base spectrum. ;

[0078] Step S242: Dynamic spectrum with motion information Perform difference calculation to obtain the true motion spectrum The formula is as follows:

[0079] (6);

[0080] Step S3 specifically includes:

[0081] Step S31: After short-time Fourier transform, the real motion spectrum The first frequency point in The corresponding value is 0, which means , to remove the DC component of the Doppler signal;

[0082] Step S32: Calculate the Doppler signal after removing the DC component using Hilbert transform. The coefficient of Hilbert transform is , is a frequency variable used to determine The value of When , the negative frequencies in the frequency domain signal move forward Phase, when When , the positive frequencies in the frequency domain signal move backward Phase to obtain a complex signal, and then modulus the obtained complex signal to obtain the true envelope of the frequency domain signal. The formula for the value of is as follows:

[0083] (7);

[0084] Where, is an imaginary unit;

[0085] like Figure 3 As shown, in step S33, 0.5 times the maximum envelope amplitude is set as the minimum peak height threshold to avoid noise interference. Then, the entire real motion spectrum data is traversed by the peak search function to determine whether each data meets the peak height threshold condition. The data that meets the peak height threshold condition is the peak point. The method for setting the peak height threshold is: taking the absolute value of the calculated real envelope, and taking 0.5 times the maximum envelope value in the real envelope data after taking the absolute value. The formula for screening the peak point using the peak height threshold is:

[0086] (8);

[0087] in, represents the peak height threshold; represents the true envelope signal, is the absolute value function The abbreviation of Indicates taking the absolute value of the true envelope signal.

[0088] The output value of the peak finding function is , the formula is as follows:

[0089] (9);

[0090] is the index of the real motion spectrum data. It points to each data point in turn during the traversal of the entire real motion spectrum data. For spectrum data The value at point and yes When the adjacent data points When the output is 1, The point is the peak point. When the output is 0, The point is not a peak point.

[0091] As the core module of the time-frequency analysis module, the Short-Time Fourier Transform (STFT) performs localized analysis on the Doppler signal in the time and frequency domains, providing the instantaneous frequency characteristics of the signal. This processing step lays the foundation for subsequent denoising and feature extraction. Figure 2 As shown in the comparison diagram of the improved threshold function curve, the horizontal axis is the input signal , the vertical axis is the output signal In the legend in the upper left corner, the three curves are the hard threshold function curve, the soft threshold function curve, and the improved threshold function curve from top to bottom. The hard threshold algorithm is simple and direct, but it is easy to introduce large reconstruction errors; the soft threshold algorithm can better suppress noise, but it will cause excessive smoothing of the signal and easily lose details; the improved wavelet denoising threshold algorithm proposed in this invention dynamically adjusts the processing method of the input signal in different signal areas (dramatic changes or flat), and performs better in terms of smoothness and adaptability. It can dynamically adjust the processing method of the input signal according to the local characteristics of the signal or the noise level.

[0092] like Figure 4 As shown, Figure 4 (a) is the original image, Figure 4 (b) is the fast Fourier transform and envelope diagram, comparing these two images, Figure 4 (b) is smoother, the peak is more obvious, and the output panel shows detailed data, which is more intuitive and convenient for users to view. The single-point laser Doppler velocimeter (LDV) is a device used to measure the speed information of an object at a specific point. In the present invention, the laser Doppler effect is used. When the laser is irradiated on a moving object, the frequency of the scattered light will change. The Doppler signal related to the speed is obtained by detecting this frequency change; the disk rotation velocity measurement experiment uses the disk as the moving object, and by rotating it, the speed information of the disk during rotation is measured using a single-point laser Doppler velocimeter system. In the present invention, the disk is in a rotating state, and the single-point laser Doppler velocimeter system continuously monitors and collects data on the relevant information during the disk rotation process, and obtains Doppler signals related to the disk motion at different rotation frequencies. The improved Doppler velocity measurement method is verified by building a single-point velocity measurement system and conducting a disk rotation velocity measurement experiment. Figure 5 As shown, Figure 5 (a) is a traditional Fast Fourier Transform (FFT) spectrum diagram, where the horizontal axis is frequency in Hertz (Hz) and the vertical axis is amplitude in decibel milliwatt (dbm). Figure 5Figure (b) shows a spectrum diagram of an improved Doppler signal processing method and system proposed by the present invention. The horizontal axis represents frequency in Hertz (Hz), and the vertical axis represents normalized amplitude. Comparing the two figures, we can see that the improved Doppler signal processing method does not deviate from the characteristics of the original spectrum diagram, and the spectrum diagram is clear, with low signal-to-noise ratio and prominent peaks. Therefore, the improved Doppler signal processing method and system proposed by the present invention have significant effects.

[0093] 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 the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An improved Doppler signal processing method, characterized in that: The following steps are involved: Step S1, signal acquisition and preprocessing: acquiring the original Doppler signal and performing sliding average filtering; Step S1 specifically includes: Step S11, setting the sampling rate to 200 MHz, collecting the original Doppler signal and inputting it; Step S12: Set the window length to 9 and perform sliding average filtering on the input original Doppler signal. This process suppresses random noise while retaining the signal trend characteristics. The output of the sliding average filter is The expression is: （1）； in, Indicates the current moment in the time series, and its value range is , is the signal length, is the length of the moving average filter, the moving average filter calculation time The current point and the front The average value of the points is taken as the signal value after filtering. is the average coefficient, is the input signal, is the index variable in the sum operation and , by changing Value, select the input signal value in turn Accumulate the values at different moments in the Step S2, time-frequency analysis: using Hamming window function, improved wavelet threshold denoising and short-time Fourier transform to perform time domain analysis on the filtered Doppler signal to obtain a motion spectrum diagram; Step S2 specifically includes: Step S21: Process the filtered Doppler signal using a Hamming window function to suppress the edge effect of the Doppler signal. The definition of the Hamming window function is as follows: （2）； in, is the serial number of the current sampling point, and its value range is , is the length of the Hamming window function; Step S22: Dynamically denoise the Doppler signal obtained after processing in step S21 using improved wavelet threshold denoising. Different frequency components in the Doppler signal obtained after processing in step S21 are separated by the multi-scale decomposition capability of wavelet. The formula of the improved wavelet threshold denoising function is as follows: （3）； in, is the output signal, is the input signal, is the absolute value of the input signal, is the symbolic function defined, according to the input signal The positive or negative value of the output signal is determined The symbol, is the threshold, and is the adjustment parameter, Controls the contraction strength and affects the compression amplitude of the output signal. Control smoothness, affecting the adjustment of the signal in the transition area; Step S23, the Doppler signal obtained after the processing in step S22 is subjected to segmented windowing and short-time Fourier transform, the time-frequency characteristics of the Doppler signal are analyzed, the frequency characteristics of the Doppler signal varying with time are captured, and the original spectrum is obtained. The formula for windowing using the window function is: （4）； in, is the signal sequence, Represents the input signal At the current moment The value of is the window function in the signal sequence The starting position mark on Indicates the input signal Perform segmented windowing. Window expression for the signal sequence, is the length of each signal sequence; Short-time Fourier transform is used to capture non-stationary signals and mutation signals, and the expression is: （5）； in, represents the result of short-time Fourier transform, For the current moment, Is the signal sequence The index parameter for even-odd segment calculation, is the window function in the signal sequence The starting position on is the frequency index, used to determine the frequency position, is the signal length, For about Odd sequence Point transformation, For about even sequence of Point transformation; Step S24: Processing the original spectrum using a spectrum optimization method based on background spectrum correction; Step S241: Collect a static spectrum without information about moving objects as a base spectrum. ; Step S242: Dynamic spectrum with motion information Perform difference calculation to obtain the true motion spectrum The formula is as follows: （6）； Step S3, feature extraction: using Hilbert transform to calculate the true envelope and combining it with a peak-finding function based on envelope analysis to traverse the data in the motion spectrum to extract the peak points; Step S3 specifically includes: Step S31: After short-time Fourier transform, the real motion spectrum The first frequency point in The corresponding value is 0, which means , to remove the DC component of the Doppler signal; Step S32: Calculate the Doppler signal after removing the DC component using Hilbert transform. The coefficient of Hilbert transform is , is a frequency variable used to determine The value of When , the negative frequencies in the frequency domain signal move forward Phase, when When , the positive frequencies in the frequency domain signal move backward Phase to obtain a complex signal, and then modulus the obtained complex signal to obtain the true envelope of the frequency domain signal. The formula for the value of is as follows: （7）； Where, is an imaginary unit; Step S33: Set a peak height threshold. Use the peak-finding function to traverse the entire real motion spectrum data and determine whether each data satisfies the peak height threshold condition. The data that satisfies the peak height threshold condition is the peak point. The method for setting the peak height threshold is as follows: take the absolute value of the calculated real envelope. Among the real envelope data after taking the absolute value, the data that is greater than 0.5 times the envelope value of all other data is set as the peak height threshold. The formula for screening peak points using the peak height threshold is: （8）； in, represents the peak height threshold; represents the true envelope signal, is the absolute value function The abbreviation of Indicates taking the absolute value of the true envelope signal; The output value of the peak finding function is , the formula is as follows: （9）； is the index of the real motion spectrum data. It points to each data point in turn during the traversal of the entire real motion spectrum data. For spectrum data The value at point and yes When the adjacent data points When the output is 1, The point is the peak point. When the output is 0, The point is not the peak point; Step S4: Determine and output: Filter the peak points that meet the peak height threshold condition, and output the peak points and their position coordinates.

2. The improved Doppler signal processing method according to claim 1, wherein: After step S4, the process also includes conducting a disk rotation speed measurement experiment by building a single-point laser Doppler speed measurement system to test the performance of the improved Doppler signal processing method.

3. A system for executing the improved Doppler signal processing method according to claim 1, characterized in that: The invention comprises a pre-processing module, a time-frequency analysis module and a feature extraction module which are connected in sequence, wherein the pre-processing module comprises an original signal input module which acquires Doppler signals with a set sampling frequency, and a sliding average filtering module which filters the acquired Doppler signals with a set window length; the time-frequency analysis module comprises a Hamming window function processing module which can suppress signal edge effects, an improved wavelet threshold denoising module which dynamically denoises Doppler signals, and a short-time Fourier transform module which captures the frequency characteristics of Doppler signals; the feature extraction module comprises a Doppler peak extraction module which is based on envelope analysis and then utilizes Hilbert transform, and a spectrum feature output module which outputs the peak coordinates corresponding to the effective frequency points; the original signal input module, the sliding average filtering module, the Hamming window function processing module, the improved wavelet threshold denoising module, the short-time Fourier transform module, the Doppler peak extraction module and the spectrum feature output module are connected in sequence.

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