Time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal

Abstract

The invention relates to the technical field of acoustic signal processing, and particularly discloses a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal, which comprises the following steps: acquiring a signal; carrying out downsampling and band-pass filter processing on the collected signals; the processed data are converted into a one-dimensional array, and the position of a local peak value is found through sliding window detection and local extreme value retrieval; carrying out data interception and elimination according to the local peak value position; setting a threshold value and clearing data smaller than the threshold value; adding a judgment condition, and clearing data except the condition to obtain a reference fragment; calculating the similarity between the reference segments, and determining a time offset; and determining the actual rising edge position of the pulse according to the time offset, and obtaining a time delay value. By adopting the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal, the interference influence of an underwater acoustic channel on a received signal is solved, the phenomenon that a multi-path interference signal positioning peak value is inaccurate is solved, and the accuracy of time delay estimation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of acoustic signal processing, and in particular to a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal. BACKGROUND

[0002] Common time delay estimation methods include matched filtering, cross-spectral method, and adaptive filtering. Matched filtering method obtains correlation peak value by cross-correlation operation of received signal and transmitted signal, and then estimates time delay. This method is simple to implement, fast in calculation, suitable for real-time processing, and widely used in practical engineering.

[0003] Underwater acoustic signal time delay detection method is a very important technology in underwater acoustic communication, navigation, positioning and other fields. Common underwater acoustic signal time delay detection methods include methods based on signal correlation, cross-correlation, cross-correlation, time-frequency analysis and other means. However, these methods also have some shortcomings and deficiencies, including: Influence of multipath effect: Underwater environment is complex, and underwater acoustic signal is often affected by multiple propagation paths, resulting in multipath effect. This effect makes signal time delay detection more difficult, because reflection and refraction of signal may cause time delay estimation error.

[0004] Noise interference: Underwater environment usually has strong background noise (such as ship noise, marine biological activity, etc.). These noises interfere with the accuracy of time delay detection, especially in low signal-to-noise ratio conditions, the accuracy of time delay estimation may be greatly reduced.

[0005] Signal attenuation and distortion: Underwater acoustic signal will be attenuated and distorted due to changes in water depth, temperature, salinity and other factors during transmission. This will change the frequency components of the signal, affecting the detection accuracy of time delay.

[0006] High computational complexity: Some methods, such as time-frequency analysis-based methods (such as wavelet transform, short-time Fourier transform, etc.), can effectively improve the accuracy of time delay detection, but these methods have high computational complexity, especially in real-time detection, which may not meet the needs of practical applications.

[0007] Multiple signal source interference: In underwater environment, multiple sound sources exist at the same time, which may cause mutual interference between signals, making time delay estimation more difficult. Traditional time delay detection methods are difficult to effectively distinguish signals from different sound sources.

[0008] Not adaptive to dynamic environmental changes: Underwater environment is usually dynamic, and factors such as changes in water flow and temperature layers may fluctuate over time, which will affect the propagation characteristics of underwater acoustic signals. Traditional time delay detection methods often assume a stable environment, but in dynamic environment, the accuracy of time delay detection will be greatly affected.

[0009] Because the double pulse peak position obtained by frequency domain matching filter is generally in the center position, and due to the influence of the underwater acoustic channel and the environment, the signal contains noise such as known signal and reverberation, and the peak position is not necessarily in the center, and there is a certain error. In order to accurately obtain the rising edge position of the time measurement pulse, so as to obtain the true time delay value and reduce the time measurement error, it is necessary to adjust based on the peak position. SUMMARY

[0010] The purpose of the application is to solve the problem of interference of the underwater acoustic channel on the received signal, and the influence of inaccurate positioning of the multi-path interference signal on the time delay detection accuracy.

[0011] In order to achieve the above purpose, the application provides a time delay detection method based on double pulse linear frequency modulation underwater acoustic signal, comprising the following steps: S1, collecting the double pulse linear frequency modulation signal emitted by the underwater sound source, the double pulse linear frequency modulation signal being composed of a double pulse linear frequency modulation signal composed of a ranging pulse and a depth measurement pulse; S2, performing down-sampling and band-pass filter processing on the collected original double pulse linear frequency modulation signal; S3, converting the data processed by S2 into a one-dimensional array, finding the positions of all local maximum values by means of sliding window detection and local extreme value retrieval, sequentially adding all local maximum values to find the position of the local peak value; S4, intercepting the signal near the local peak value according to the local peak value position, removing the remaining data, and defining the intercepted signal as the final signal; S5, after normalizing the final signal data, setting a threshold to remove data less than the threshold; S6, adding a decision condition to remove data outside the double pulse time interval to obtain a reference segment; S7, calculating the similarity score between the reference segments to determine the time offset between the pulses; S8, determining the actual rising edge positions of the ranging pulse and the depth measurement pulse according to the determined time offset, and obtaining the time delay value.

[0012] Preferably, in S1, the ranging pulse and the depth measurement pulse are a pair of linear frequency modulation signals with the same pulse width, the same frequency band and the same frequency modulation slope, the ranging pulse and the depth measurement pulse are positive frequency linear frequency modulation signals, and the frequency range is 14kHz-24kHz. The pulse width and the bandwidth are adjusted according to the scene.

[0013] Preferably, in S2, the original double pulse linear frequency modulation signal collected is subjected to down-sampling processing, and the expression is: ; Wherein, is the original signal, is the down-sampling factor, is the down-sampled signal sequence; The down-sampled signal data is processed through a band-pass filter, and the frequency domain processing expression of the band-pass filter is:

[0014] wherein, is the Fourier transform of the down-sampled signal is the frequency response function of the band-pass filter, and is defined as:

[0015] wherein, that is, the passband of the filter is 14 kHz to 24 kHz.

[0016] Preferably, S3 is specifically: The data processed through S2 down-sampling and band-pass filtering is converted into a one-dimensional array, and all local maximum positions are found by means of sliding window detection and local extreme value retrieval. All local maximum values are sequentially added to find the maximum, and the position of the peak value is found, and the expression is: ; wherein, represents an input discrete-time signal sequence, represents the position index of the maximum value of the signal.

[0017] Preferably, in S4, after finding two local peak values, the signal near the local peak values is intercepted according to the positions of the two local peak values, and the remaining data is removed. Suppose the positions of the two peak values are: ; wherein, are the two maximum local peak values; Suppose the window length is , and the length of the signal reserved before and after each peak value is , and the interception range is: ; The final signal is defined as: .

[0018] Preferably, in S5, after the final signal data is normalized, a threshold is set, and the data smaller than the threshold is removed. The setting of the threshold depends on the system background noise and the noise in the underwater acoustic environment, and the expression is: ; wherein,​ is the normalized signal data, is the maximum value in the signal data; ; in, T The energy threshold is set to filter out low-amplitude noise interference and retain only the effective pulse signal part. It is the signal after threshold processing, which only retains the original signal greater than T part.

[0019] Preferably, in S6, a judgment condition is added to set the double-pulse time interval to be within 100ms-400ms, and the data with a double-pulse time interval outside 100ms-400ms is cleared. The expression is: ; in, is the sampling rate, Indicates the detected The location of the local peak, Indicates the detected The location of the peak.

[0020] Preferably, S7 is specifically: Intercept the ranging pulse segment data, overlap the two peak positions, and then perform point product summation and similarity test in sequence; : Signal segment near the first peak; : Signal segment near the second peak; in, and Indicates that the original signal is 、 The sampling value of N Indicates the length of the new segment to be intercepted. is the signal segment near the ranging pulse, It is the signal segment near the sounding pulse; Calculate the normalized cross-correlation to get the similarity score: ; in, S represents the normalized cross-correlation coefficient, Indicates the first signal segment The value of the sampling point, Indicates the second signal segment The value of the sampling point, N Indicates the length of the new number segment; like threshold, two pulse heights are similar, wherein threshold is a similarity decision threshold; The time offset between the pulses is the abscissa from the highest to the lowest similarity, and the expression is: ; Wherein, And Respectively represent the signal segment intercepted with the peak value of the two pulses as the center, The sliding offset Indicates the L2 norm of the corresponding signal segment, and the maximum value of the calculation The abscissa corresponding to the maximum value is the optimal time offset between the two pulses.

[0021] Preferably, S8 is specifically: The peak position of the ranging pulse is subtracted by the time offset to obtain the accurate pulse rising edge position, that is, the time delay value; The peak position of the ranging pulse is defined as , The optimal offset measured in the sliding matching is The pulse rising edge position is ; According to the sampling rate , the time delay Is calculated: .

[0022] The above-mentioned time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal has the following beneficial effects: (1) The present application reduces the amount of calculation by finding the double-pulse peak value through downsampling, bandpass filtering, normalization processing, matching filtering, and adaptive linear enhancement ALE, and accelerating the calculation, so that the signal detection is completed within the transmission period.

[0023] (2) The present application is simple to implement in engineering, uses similarity detection, adjusts the rising edge position, solves the phenomenon that the water acoustic channel interferes with the received signal and the multi-path interference signal positioning peak value is not accurate, accurately positions the rising edge position of the direct sound, improves the accuracy of time delay estimation, enhances the robustness and reliability of linear frequency modulation signal detection, and then improves the reliability and precision of time delay estimation in underwater environmental noise and multi-path interference channel. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 Is the overall flowchart of an embodiment of the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal of the present application; Figure 2 ​is a data one-dimensional array search local maximum value schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 3 is a two-peak spliced schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; schematic view; Figure 4 is a threshold processing schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 5 is a similarity comparison schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 6 is a similarity curve view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 7 is a time domain view of signal preprocessing of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application, wherein (a) is a time domain view of a double pulse LFM signal, (b) is a time domain view of a double pulse LFM signal after adding noise, and (c) is a time domain view of a signal after preprocessing; Figure 8 is a track 1 schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 9 is a track 2 schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 10 is a track 3 schematic view of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application; Figure 11 is a test detection result schematic view of an embodiment of the present application under a 1% power condition, wherein (a) is an original signal time domain view, (b) is a signal time domain view after matched filtering, and (c) is a time domain view after ALE (signal enhancement based on an adaptive filter principle) of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application.

[0025] Figure 12 is a test detection result schematic view of an embodiment of the present application under a 5% power condition, wherein (a) is an original signal time domain view, (b) is a signal time domain view after matched filtering, and (c) is a time domain view after ALE (signal enhancement based on an adaptive filter principle) of an embodiment of a time delay detection method based on a double pulse linear frequency modulation underwater acoustic signal of the present application. DETAILED DESCRIPTION

[0026] The technical solutions of the present application are described in further detail below with reference to the accompanying drawings and examples. Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the usual meanings understood by those with ordinary skills in the art to which the present application belongs. The terms "first", "second", and similar words used in the present application do not represent any order, quantity, or importance, but are only used to distinguish different components.

[0027] The present application proposes a complete detection scheme, specifically including down-sampling, band-pass filtering, matching detection, similarity verification, and pulse rising edge determination, which can effectively suppress reverberation and noise interference, and expand the applicability of the detection algorithm under different ranges and transmission power conditions.

[0028] As shown in Figure 1 A time delay detection method based on double-pulse linear frequency modulation underwater acoustic signals, comprising the following steps: S1, collecting double-pulse linear frequency modulation signals emitted by an underwater sound source.

[0029] The double-pulse linear frequency modulation signal designed by the present application is composed of a double-pulse linear frequency modulation signal composed of a ranging pulse and a depth pulse, and the ranging pulse and the depth pulse are a pair of linear frequency modulation signals with the same pulse width, the same frequency band, and the same frequency modulation slope. The ranging pulse and the depth pulse are positive frequency linear frequency modulation signals, the frequency range is 14kHz-24kHz, and the pulse width and the bandwidth are adjusted according to the scene.

[0030] The underwater sound source emits the double-pulse signal every cycle, and the signal propagation time can be obtained by detecting the front edge of the ranging pulse, and the propagation distance can be calculated by the underwater sound velocity. The time interval from the trailing edge of the ranging pulse to the leading edge of the depth pulse is obtained to obtain the depth information of the underwater sound source. The initial interval is 100ms, i.e. the depth is 0m, and the interval increases by 1ms for every 1m of water depth, and the maximum interval is 400ms for a depth of 300m. The system can obtain high-precision depth information of the target by calculating the time interval between the ranging pulse and the depth pulse. The time interval between the double pulses can also effectively reduce the influence of reverberation on the rising edge detection of the pulse signal.

[0031] The signal time delay detection process is specifically: S2, down-sampling and band-pass filter processing are performed on the collected original double-pulse linear frequency modulation signal.

[0032] Firstly, the sampling frequency is appropriately reduced by down-sampling processing due to the reduction of the highest frequency of the signal. The collected original double-pulse linear frequency modulation signal is subjected to down-sampling processing, which can reduce the computational load of the subsequent processor, and the expression is: ; Among them, is the original signal, is the down-sampling factor, is the down-sampled signal sequence.

[0033] The down-sampled signal data is processed through a band-pass filter, and the frequency domain processing expression of the band-pass filter is: ; wherein, is the Fourier transform of the down-sampled signal , is the frequency response function of the band-pass filter, defined as:

[0034] wherein, , that is, the passband of the filter is 14 kHz to 24 kHz, which can effectively filter out low-frequency noise and high-frequency interference signals and retain the frequency band where the main energy of the double pulse signal is located.

[0035] S3, as shown in Figure 2 , the data processed through S2 down-sampling and band-pass filtering is converted into a one-dimensional array, and all local maximum positions are found by means of sliding window detection and local extreme value retrieval. All local maximum values are sequentially added to find the position of the local peak value, and the expression is: ; wherein, represents the input discrete-time signal sequence, represents the position index of the signal maximum value.

[0036] S4, as shown in Figure 3 , after finding two local peak values, the signal near the local peak value is intercepted according to the positions of the two local peak values, and the remaining data is removed. The intercepted signal is defined as the final signal.

[0037] Suppose the positions of the two peak values are: ; wherein, are the two maximum local peak values; Suppose the window length is , the length of each peak value is reserved before and after the peak value, and the interception range is: ; The final signal is defined as: .

[0038] S5, as shown in Figure 4As shown, after normalizing the final signal data, a threshold is set, and data less than the threshold is removed. The threshold is set according to the system background noise and noise in the underwater acoustic environment, and the expression is: ; wherein, is the normalized signal data, is the maximum value in the signal data.

[0039] ; wherein, T is the set energy threshold, the value of which is set according to the system background noise level, background interference intensity and other environmental factors, and is used to filter out low-amplitude noise interference and only keep the effective pulse signal part, is the signal after threshold processing, only the part of the original signal greater than T is kept.

[0040] S6, add a judgment condition to remove data outside the double-pulse time interval to obtain a reference segment. The double-pulse time interval is set to be within 100ms-400ms, and data outside the double-pulse time interval of 100ms-400ms is removed, and the expression is: ; wherein, is the sampling rate, represents the position of the detected local peak value, represents the position of the detected next, i.e., the th peak value.

[0041] S7, calculate the similarity score between the reference segments to determine the time offset between the pulses.

[0042] Since the emission time interval of the double-pulse signal is short, the passing underwater acoustic environment is basically consistent, and the signal arriving at the hydrophone from the sound source is basically consistent. As shown in Figure 5 , on the basis of the previous processing result, the ranging pulse segment data is intercepted, the two peak value positions are overlapped, and then point multiplication summation is performed in turn and similarity test is performed.

[0043] : signal segment near the first peak value; : signal segment near the second peak value; wherein, and represent the sampling values of the original signal at , N ​​denotes the length of the new segment, for the signal segment near the ranging pulse, for the signal segment near the depth pulse, both of which are used for the subsequent sliding matching process to calculate the normalized cross-correlation value to extract the accurate time offset.

[0044] The normalized cross-correlation is calculated to obtain the similarity score: ; wherein, S denotes the normalized cross-correlation coefficient (similarity score) measuring the similarity between the two signal segments, denotes the value of the i-th sampling point in the first signal segment, denotes the value of the i-th sampling point in the second signal segment, denotes the length of the new segment, i.e., the number of sampling points contained in each segment; the similarity score result ranges from 0 to 1. N If threshold, the two pulses are highly similar, wherein threshold is the similarity decision threshold.

[0045] As shown in FIG. 8, the obtained similarity curve gradually decreases, and the time offset between the pulses is the abscissa from the highest to the lowest similarity, and the expression is:

[0046] Figure 6 ; ; wherein, and denote the signal segments intercepted with the two pulse peaks as the centers, is the sliding offset denotes the L2 norm of the corresponding signal segment, and the maximum value of is calculated by traversing , and the abscissa corresponding to the maximum value is the optimal time offset between the two pulses.

[0047] S8, as shown in FIG. 9, according to the determined time offset, the actual rising edge positions of the ranging pulse and the depth pulse are determined to obtain the time delay value. Figure 7 The peak position of the ranging pulse is subtracted by the time offset to obtain the accurate pulse rising edge position, i.e., the time delay value;

[0048] the peak position of the ranging pulse is defined as , is the optimal offset measured in the sliding matching, is the pulse rising edge position, and then: ​​​​ ; According to the sampling rate , the time delay is calculated: ; The unit is second (s).

[0049] Example one

[0050] Step 001: Four positioning buoys are laid in the offshore area, labeled No. 1, No. 2, No. 3, and No. 4, with an interval of about 4 km, to construct a long baseline reference coordinate system. At the same time, the shore-based platform carries 3D segments and transceivers into the calibration area, configures standard hydrophones and transmitters, and establishes a synchronous acquisition and control interface.

[0051] Step 002: Set the transmitter to double-pulse linear frequency modulation signal, parameters: frequency range: 14kHz-24kHz, pulse width and bandwidth are the same; Pulse time interval: 100ms-400ms adjustable. During the orbit sailing process, according to the order of orbit 1 (10:55-11:10), orbit 2 (11:22-12:00), and orbit 3 (12:35-13:04), transmit double-pulse signals of different power (1% and 5%).

[0052] Step 003: As shown in Figures 8-10 , the shore-based platform drifts with the current, continuously collecting response data of the four buoys, detecting whether the double-pulse signal can be stably received within 4 km under different transmission power and orbit conditions. Whether there is signal loss between No. 3 and No. 4 buoys. Detect the deviation between the orbit solution trajectory and the reference orbit during the orbit start and end time period.

[0053] Step 004: The collected original double-pulse linear frequency modulation signal is detected and time delay is extracted. The collected original double-pulse linear frequency modulation signal is first subjected to downsampling processing to reduce the signal sampling rate, thereby reducing the subsequent operation amount. At the same time, a pre-designed bandpass filter is applied to the downsampled signal to limit the frequency between 14kHz-24kHz, suppress low and high frequency noise, and improve the detection signal quality. In the preprocessed signal, the reference positions of the ranging pulse and the depth pulse are determined through sliding window detection and local extreme value retrieval, marked as local peak A and local peak B.

[0054] For the detected two local peak segments, several sampling points before and after the peak are extracted as reference segments, the similarity between the reference segments is calculated, and the offset between the pulses is determined. According to the offset determined by the similarity detection, the actual rising edge positions of the ranging pulse and the depth pulse are further determined, so as to accurately obtain the pulse time delay value and avoid the detection error caused by the main peak offset. The similarity test adopts the normalized cross-correlation method, the offset between the two pulses is determined by calculating the similarity coefficient, and the influence of reverberation and interference on the detection accuracy is excluded.

[0055] Step 005: Compare the detected track and the reference track to test the effect of the detection algorithm: As shown in Figure 11 Under the condition of 1% power, the matching detection effect is unstable, and the pulse detection deviation is 0.5-1.5ms.

[0056] As shown in Figure 12 Under the condition of 5% power, the detection deviation is stable within ±1ms, and the track solving deviation is controlled within 2-3m.

[0057] The detection algorithm effectively suppresses reverberation and noise, and the communication effect of the No. 3 and No. 4 buoys is significantly improved at a distance greater than 3km.

[0058] Therefore, the present application provides a time delay detection method based on double-pulse linear frequency modulation signal, which can be applied to occasions requiring high-precision time delay detection and anti-interference capability such as underwater acoustic positioning, ranging and track solving. In practical application, the present application can accurately determine the arrival time of the ranging and depth pulses in a complex, multi-path interference and low signal-to-noise ratio underwater acoustic environment, thereby greatly improving the track solving and positioning accuracy.

[0059] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal, characterized in that: The following steps are involved: S1, collecting a double-pulse linear frequency modulation signal emitted by an underwater sound source, where the double-pulse linear frequency modulation signal is composed of a double-pulse linear frequency modulation signal consisting of a ranging pulse and a depth-measuring pulse; S2, downsampling and bandpass filtering the collected original double-pulse linear frequency modulation signal; S3, converting the data processed by S2 into a one-dimensional array, finding the locations of all local maxima through sliding window detection and local extreme value retrieval, adding up all local maxima in sequence to obtain the maximum, and finding the location of the local peak; S4, intercepting the signal near the local peak according to the local peak position, clearing the remaining data, and defining the intercepted signal as the final signal; S5. After normalizing the final signal data, set a threshold to remove data smaller than the threshold; S6, adding a judgment condition, clearing the data outside the double-pulse time interval, and obtaining a reference segment; S7, calculating similarity scores between reference segments and determining time offsets between pulses; S8. Determine the actual rising edge positions of the ranging pulse and the depth measuring pulse according to the determined time offset, and obtain a time delay value.

2. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 1, characterized in that: In S1, the ranging pulse and the depth sounding pulse are linear frequency modulation signal pairs with the same pulse width, the same frequency band, and the same frequency modulation slope. The ranging pulse and the depth sounding pulse are positive frequency linear frequency modulation signals with a frequency range of 14kHz-24kHz. The pulse width and bandwidth are adjusted according to the scenario.

3. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 2, characterized in that: In S2, the collected original double-pulse linear frequency modulation signal is downsampled, and the expression is: ； in, is the original signal, is the downsampling factor, is the downsampled signal sequence; The downsampled signal data is passed through a bandpass filter. The frequency domain processing expression of the bandpass filter is: ； in, Downsampled signal The Fourier transform of is the frequency response function of the bandpass filter, defined as: ； in, , that is, the filter passband is 14kHz to 24kHz.

4. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 3, characterized in that: S3 specifically: The data after S2 downsampling and bandpass filtering is converted into a one-dimensional array. The positions of all local maxima are found by sliding window detection and local extreme value retrieval. All local maxima are summed up in sequence to get the maximum value, and the peak position is found. The expression is: ； in, represents the input discrete-time signal sequence, Indicates the index of the position where the maximum value of the signal is located.

5. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 4, characterized in that: In S4, after finding two local peaks, the signal near the local peaks is intercepted according to the positions of the two local peaks, and the remaining data is cleared; Assume that the locations of the two peaks are: ； in, are the two largest local peaks; Assume the window length is , the retention length before and after each peak , the interception range is: ； Defining the final signal for: 。 6. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 5, characterized in that: In S5, after the final signal data is normalized, a threshold is set to remove data below the threshold. The setting of the threshold depends on the system background noise and the noise in the underwater acoustic environment. The expression is: ； in, is the normalized signal data, is the maximum value in the signal data; ； in, T The energy threshold is set to filter out low-amplitude noise interference and retain only the effective pulse signal part. It is the signal after threshold processing, which only retains the original signal greater than T part.

7. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 6, characterized in that: In S6, a judgment condition is added to set the double-pulse time interval to be within 100ms-400ms, and the data with a double-pulse time interval outside 100ms-400ms is cleared. The expression is: ； in, is the sampling rate, Indicates the detected The location of the local peak, Indicates the detected The location of the peak.

8. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 7, characterized in that: S7 specifically: Intercept the ranging pulse segment data, overlap the two peak positions, and then perform point product summation and similarity test in sequence; : Signal segment near the first peak; : Signal segment near the second peak; in, and Indicates that the original signal is 、 The sampling value of N Indicates the length of the new segment to be intercepted. is the signal segment near the ranging pulse, It is the signal segment near the sounding pulse; Calculate the normalized cross-correlation to get the similarity score: ； in, S represents the normalized cross-correlation coefficient, Indicates the first signal segment The value of the sampling point, Indicates the second signal segment The value of the sampling point, N Indicates the length of the new number segment; like threshold, the two pulses are highly similar, where threshold is the similarity judgment threshold; The time offset between pulses is the horizontal axis from the highest to the lowest similarity, and is expressed as: ； in, and They represent the signal segments intercepted with the two pulse peaks as the center, Sliding offset Represents the L2 norm of the corresponding signal segment, by traversing ,calculate The maximum value of , the corresponding horizontal axis is the optimal time offset between the two pulses.

9. The time delay detection method based on double-pulse linear frequency modulation underwater acoustic signal according to claim 8, characterized in that: S8 specifically: Subtract the time offset from the peak position of the ranging pulse to obtain the accurate pulse rising edge position, which is the delay value; The peak position of the ranging pulse is defined as , is the optimal offset measured in sliding matching, is the pulse rising edge position, then: ； According to the sampling rate , calculate the delay : 。

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