A time delay detection method based on double pulse linear frequency modulation underwater acoustic signal

Abstract

The application 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: collecting a signal; performing downsampling and band-pass filter processing on the collected signal; converting the processed data into a one-dimensional array, finding the position of a local peak value through sliding window detection and local extreme value retrieval; performing data interception and elimination according to the position of the local peak value; setting a threshold value to eliminate data smaller than the threshold value; adding a judgment condition to eliminate data outside the condition, obtaining a reference segment; calculating the similarity between the reference segments to determine a time offset; and determining the actual rising edge position of the pulse according to the time offset to obtain a time delay value. The time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal solves the interference influence of an underwater acoustic channel on a received signal, solves the phenomenon that a multi-path interference signal cannot be accurately positioned at a peak value, and improves the accuracy of time delay estimation.

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:

[0004] Influence of multipath effect: Underwater environment is complex, and underwater acoustic signals are often affected by multiple propagation paths, resulting in multipath effect. This effect makes signal time delay detection more difficult, because reflection and refraction of signals may cause time delay estimation error.

[0005] 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.

[0006] Signal attenuation and distortion: Underwater acoustic signals 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.

[0007] 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.

[0008] 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.

[0009] Inadaptation to dynamic environmental changes: The underwater environment is usually dynamically changing, 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 that the environment is stable, but in a dynamic environment, the accuracy of time delay detection will be greatly affected.

[0010] Due to the fact that the peak position of the double pulse obtained by frequency domain matched filtering is generally at the center position, and due to the influence of the underwater acoustic channel and the environment, the signal contains noise such as known signals and reverberation, and the peak position is not necessarily at the center, but there is a certain error. In order to accurately obtain the rising edge position of the time measurement pulse and thus obtain the true time delay value and reduce the time measurement error, adjustment based on the peak position is required. SUMMARY

[0011] The purpose of the present application is to solve the problem of interference of the underwater acoustic channel on the received signal and the problem of inaccurate positioning of the peak of the multi-path interference signal affecting the accuracy of time delay detection.

[0012] To achieve the above purpose, the present application provides a time delay detection method based on double pulse linear frequency modulation underwater acoustic signal, comprising the following steps:

[0013] 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;

[0014] S2, performing down-sampling and band-pass filter processing on the collected original double pulse linear frequency modulation signal;

[0015] 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, and sequentially adding all local maximum values to find the position of the local peak value;

[0016] S4, intercepting the signal near the local peak value according to the position of the local peak value, removing the remaining data, and defining the intercepted signal as the final signal;

[0017] S5, after normalizing the final signal data, setting a threshold to remove data less than the threshold;

[0018] S6, adding a decision condition to remove data outside the time interval of the double pulse to obtain a reference segment;

[0019] S7, calculating the similarity score between the reference segments to determine the time offset between the pulses;

[0020] 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.

[0021] Preferably, in S1, 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, and the ranging pulse and the depth pulse are positive frequency linear frequency modulation signals with a frequency range of 14kHz-24kHz, and the pulse width and the bandwidth are adjusted according to the scene.

[0022] Preferably, in S2, the collected original double-pulse linear frequency modulation signal is subjected to downsampling processing, and the expression is:

[0023] ;

[0024] wherein, is the original signal, is a downsampling factor, is a signal sequence after downsampling;

[0025] The signal data after the downsampling processing is subjected to band-pass filtering, and the frequency domain processing expression of the band-pass filter is:

[0026]

[0027] wherein, is the Fourier transform of the downsampling signal is a frequency response function of the band-pass filter, and is defined as:

[0028]

[0029] wherein, , that is, the passband of the filter is 14kHz to 24kHz.

[0030] Preferably, S3 is specifically:

[0031] The data after the downsampling and band-pass filtering processing of S2 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, and the positions of all local maximum values are sequentially added to find the position of the peak value, and the expression is:

[0032] ;

[0033] wherein, represents an input discrete-time signal sequence, represents the position index of the maximum value of the signal.

[0034] 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.

[0035] Suppose the positions of the two peak values are:

[0036] ​ ;

[0037] wherein, are two maximum local peaks;

[0038] Let the window length be , and the length reserved before and after each peak be , the range of interception be:

[0039] ;

[0040] Define the final signal as:

[0041] .

[0042] Preferably, in S5, after normalization processing of the final signal data, a threshold is set, and data less than the threshold is removed, the setting of the threshold depends on the system background noise and noise in the underwater acoustic environment, and the expression is:

[0043] ;

[0044] wherein, is the normalized signal data, is the maximum value in the signal data;

[0045] ;

[0046] wherein, T is the set energy threshold, 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 greater than T in the original signal is kept.

[0047] Preferably, in S6, a decision condition is added, the double pulse time interval is set within 100ms-400ms, and data outside the range of 100ms-400ms is removed, and the expression is:

[0048] ;

[0049] wherein, is the sampling rate, represents the position of the detected th local peak, represents the position of the detected th peak.

[0050] Preferably, S7 is specifically:

[0051] Extract the ranging pulse segment data, overlap the two peak positions, and then perform dot product summation and similarity check in sequence;

[0052] : The signal segment near the first peak;

[0053] : Signal segment near the second peak;

[0054] in, and Indicates the original signal at , The sampled values, N This indicates the length of the new number segment to be extracted. The signal segment near the ranging pulse. This refers to the signal segment near the sounding pulse.

[0055] The similarity score is obtained by calculating the normalized cross-correlation:

[0056] ;

[0057] in, S Represents the normalized cross-correlation coefficient. Indicates the first signal segment The value of each sampling point, Indicates the second signal segment The value of each sampling point, N Indicates the length of the new number segment to be extracted;

[0058] like threshold, where the two pulses are highly similar, is the similarity judgment threshold;

[0059] The time offset between pulses is represented by the x-axis from highest to lowest similarity, expressed as:

[0060] ;

[0061] in, and These represent signal segments centered on the two pulse peaks. Sliding offset This represents the L2 norm of the corresponding signal segment, obtained by traversing... ,calculate The maximum value of is the optimal time offset between the two pulses, which corresponds to the horizontal axis.

[0062] Preferably, S8 is as follows:

[0063] 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;

[0064] 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

[0065] ;

[0066] According to the sampling rate , the time delay is calculated :

[0067] .

[0068] The time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal has the following beneficial effects:

[0069] (1) The double-pulse peak is found by downsampling, bandpass filtering, normalization processing, matching filtering and adaptive linear enhancement ALE, and the calculation is accelerated and the calculation amount is reduced, so that the signal detection is completed within the transmission period.

[0070] (2) The method is simple to implement in engineering, similarity detection is used, the rising edge position is adjusted, the influence of the underwater acoustic channel on the received signal is solved, the phenomenon that the multi-path interference signal positioning peak is inaccurate is solved, the rising edge position of the direct sound is accurately positioned, the accuracy of the time delay estimation is improved, the robustness and reliability of the linear frequency modulation signal detection are enhanced, and then the reliability and precision of the time delay estimation under the underwater environmental noise and multi-path interference channel are improved. DETAILED DESCRIPTION

[0071] Figure 1 is a whole process schematic diagram of an embodiment of the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal;

[0072] Figure 2 is a data one-dimensional array lookup local maximum value schematic diagram of an embodiment of the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal;

[0073] Figure 3 is a two-peak spliced schematic diagram of an embodiment of the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal;

[0074] Figure 4 is a threshold processing schematic diagram of an embodiment of the time delay detection method based on the double-pulse linear frequency modulation underwater acoustic signal;

[0075] Figure 5 is a similarity comparison diagram of an embodiment of a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal of the present application;

[0076] Figure 6 is a similarity curve diagram of an embodiment of a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal of the present application;

[0077] Figure 7 is a time domain diagram 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 diagram of a double-pulse LFM signal, (b) is a time domain diagram of a double-pulse LFM signal after adding noise, and (c) is a time domain diagram of a signal after preprocessing;

[0078] Figure 8 is a track 1 diagram of an embodiment of a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal of the present application;

[0079] Figure 9 is a track 2 diagram of an embodiment of a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal of the present application;

[0080] Figure 10 is a track 3 diagram of an embodiment of a time delay detection method based on a double-pulse linear frequency modulation underwater acoustic signal of the present application;

[0081] Figure 11 is a test detection result diagram of an embodiment of the present application under a 1% power condition, wherein (a) is a time domain diagram of an original signal, (b) is a time domain diagram of a signal after matched filtering, and (c) is a time domain diagram after ALE (signal enhancement based on an adaptive filter principle).

[0082] Figure 12 is a test detection result diagram of an embodiment of the present application under a 5% power condition, wherein (a) is a time domain diagram of an original signal, (b) is a time domain diagram of a signal after matched filtering, and (c) is a time domain diagram after ALE (signal enhancement based on an adaptive filter principle). DETAILED DESCRIPTION

[0083] The technical solutions of the present application are described in further detail below by means of the accompanying drawings and embodiments. Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the usual meanings understood by persons having ordinary skills in the art to which the present application belongs. The words "first", "second", and similar words used in the present application do not represent any order, number, or importance, but are only used to distinguish different components.

[0084] This invention proposes a complete detection scheme, which includes multiple steps such as downsampling, bandpass filtering, matching detection, similarity verification, and pulse rising edge determination. It can effectively suppress reverberation and noise interference, and expand the applicability of the detection algorithm under different measurement ranges and transmission power conditions.

[0085] like Figure 1 As shown, a time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal includes the following steps:

[0086] S1. Acquire the double-pulse linear frequency modulated signal emitted by the underwater sound source.

[0087] The dual-pulse linear frequency modulation (LFM) signal designed in this invention consists of a ranging pulse and a depth-sounding pulse. The ranging pulse and the depth-sounding pulse are a pair of LFM signals with the same pulse width, frequency band, and frequency modulation slope. The ranging pulse and the depth-sounding pulse are positive frequency modulation (PFM) signals with a frequency range of 14kHz-24kHz. The pulse width and bandwidth are adjusted according to the scene.

[0088] The underwater sound source emits a dual-pulse signal in each cycle. By detecting the leading edge of the ranging pulse, the signal propagation time can be obtained, and the propagation distance can be calculated using the underwater sound speed. The depth information of the underwater sound source is obtained by detecting the time interval between the trailing edge of the ranging pulse and the leading edge of the depth-sensing pulse. Initially, the interval is 100ms, corresponding to a depth of 0m. For every 1m increase in water depth, the interval increases by 1ms, up to a maximum of 400ms at a depth of 300m. The system can obtain high-precision depth information of the target by calculating the time interval between the ranging and depth-sensing pulses. The time interval between the dual pulses also effectively reduces the impact of reverberation on the detection of the rising edge of the pulse signal.

[0089] The signal delay detection process of this invention is as follows:

[0090] S2. The acquired raw double-pulse linear frequency modulated signal is downsampled and processed by a bandpass filter.

[0091] First, downsampling is performed. Since the highest frequency of the signal is reduced, the sampling frequency can be appropriately reduced. Downsampling the acquired original double-pulse linear frequency modulated signal can reduce the computational load on the subsequent processor. The expression is:

[0092] ;

[0093] in, The original signal, To reduce the sampling factor, It is the downsampled signal sequence.

[0094] The signal data after down-sampling processing is passed through a band-pass filter, and the frequency domain processing expression of the band-pass filter is:

[0095]

[0096] wherein, is the Fourier transform of the down-sampled signal , is the frequency response function of the band-pass filter, defined as:

[0097]

[0098] 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.

[0099] S3, as shown in the figure, the data processed by 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 added in turn to find the position of the local peak value, and the expression is: Figure 2

[0100]

[0101] wherein, represents the input discrete-time signal sequence, represents the position index of the maximum value of the signal.

[0102] S4, as shown in the figure, 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. Figure 3 Suppose the positions of the two peak values are:

[0103]

[0104]

[0105] wherein, are the two maximum local peak values;

[0106] Suppose the window length is , the length of each peak value is , and the interception range is:

[0107]

[0108] The final signal is defined as:

[0109] ​​​​​​​.

[0110] S5, such as Figure 4 As shown, after normalizing the final signal data, a threshold is set to remove data below this threshold. The threshold setting depends on the system's background noise and the noise in the underwater acoustic environment, and the expression is:

[0111] ;

[0112] in, It is the normalized signal data. It is the maximum value in the signal data.

[0113] ;

[0114] in, T The set energy threshold, whose value is determined based on environmental factors such as the system's background noise level and background interference intensity, is used to filter out low-amplitude noise interference, retaining only the effective pulse signal portion. This is the signal after thresholding; only the values ​​greater than a certain threshold in the original signal are retained. T The part.

[0115] S6. Add a decision condition to clear data outside the double pulse time interval, obtaining the reference segment. Set the double pulse time interval to be within 100ms-400ms, and clear data outside this range. The expression is:

[0116] ;

[0117] in, Sampling rate, Indicates the detected number The location of a local peak Indicates the next one detected, i.e., the first The location of each peak.

[0118] S7. Calculate the similarity score between reference segments to determine the time offset between pulses.

[0119] Because the transmission time interval of the double-pulse signal is short, and the underwater acoustic environment it passes through is basically the same, the signal reaching the hydrophone after being emitted from the sound source is basically the same. For example... Figure 5 As shown, based on the previous processing results, the ranging pulse segment data is truncated, the two peak positions are overlapped, and then the dot product is summed and a similarity check is performed.

[0120] : The signal segment near the first peak;

[0121] : Signal segment near the second peak;

[0122] in, and Indicates the original signal at , The sampled values, N This indicates the length of the new number segment to be extracted. The signal segment near the ranging pulse. The signal segment near the sounding pulse is used for subsequent sliding matching processing to calculate the normalized cross-correlation value in order to extract the accurate time offset.

[0123] The similarity score is obtained by calculating the normalized cross-correlation:

[0124] ;

[0125] in, S The normalized similarity score measures the degree of similarity between two signal segments. Indicates the first signal segment The value of each sampling point, Indicates the second signal segment The value of each sampling point, N This indicates the length of the new segment, i.e., the number of sampling points contained in each segment; the similarity score result range is... .

[0126] like threshold, where the two pulses are highly similar, and threshold is the similarity judgment threshold.

[0127] like Figure 6 As shown, the obtained similarity curve shows that the similarity gradually decreases. The time offset between pulses is represented by the horizontal axis from the highest to the lowest similarity, and the expression is:

[0128] ;

[0129] in, and These represent signal segments centered on the two pulse peaks. Sliding offset This represents the L2 norm of the corresponding signal segment, obtained by traversing... ,calculate The maximum value of is the optimal time offset between the two pulses, which corresponds to the horizontal axis.

[0130] S8, such as Figure 7As shown, according to the determined time offset, the actual rising edge position of the ranging pulse and the depth pulse is determined, and the time delay value is obtained.

[0131] 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.

[0132] 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

[0133] ;

[0134] According to the sampling rate , the time delay is calculated:

[0135] ;

[0136] The unit is second (s).

[0137] Embodiment one

[0138] Step 001: Four positioning buoys are laid in the offshore area, labeled as 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.

[0139] 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%).

[0140] Step 003: As shown in Figures 8-10 , the shore-based platform drifts with the current, and continuously collects response data of the four buoys, detects 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.

[0141] 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 band-pass filter is applied to the down-sampled signal to limit the frequency to 14 kHz-24 kHz, suppress low-frequency and high-frequency noise, and improve the detection signal quality. In the pre-processed signal, the reference positions of the ranging pulse and the depth pulse are determined by sliding window detection and local extremum retrieval, and are marked as local peak A and local peak B.

[0142] 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 detection errors caused by the main peak offset. The similarity test adopts a normalized cross-correlation method to determine the offset between the two pulses by calculating the similarity coefficient, thereby excluding the influence of reverberation and interference on the detection accuracy.

[0143] Step 005: Compare the detection track and the reference track to test the effect of the detection algorithm:

[0144] As shown in Figure 11 , under the condition of 1% power, the matching detection effect is unstable, and the pulse detection deviation is 0.5-1.5 ms.

[0145] As shown in Figure 12 , under the condition of 5% power, the detection deviation is stable within ±1 ms, and the track solution deviation is controlled within 2-3 meters.

[0146] 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 of more than 3 km.

[0147] Therefore, the present application provides a time delay detection method based on a double-pulse linear frequency modulation signal. The technology 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.

[0148] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: the technical solutions of the present application can still be modified or replaced by equivalents, 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 dual-pulse linear frequency modulated underwater acoustic signals, characterized in that, Includes the following steps: S1. Acquire the dual-pulse linear frequency modulated signal emitted by the underwater sound source. The dual-pulse linear frequency modulated signal is composed of a ranging pulse and a depth-sounding pulse. S2. The acquired raw double-pulse linear frequency modulated signal is downsampled and processed by a bandpass filter; S3. Convert the data processed by S2 into a one-dimensional array. Use sliding window detection and local extremum retrieval to find the location of all local maximum values. Add all the local maximum values ​​in sequence and take the maximum value to find the location of the local peak. S4. Extract the signal near the local peak based on the local peak position, clear the remaining data, and define the extracted signal as the final signal; S5. After normalizing the final signal data, set a threshold to clear data that is less than the threshold. S6. Add decision conditions, clear data outside the double pulse time interval, and obtain the reference segment; S7. Calculate the similarity score between reference segments and determine the time offset between pulses; S8. Based on the determined time offset, determine the actual rising edge position of the ranging pulse and the depth sounding pulse, and obtain the time delay value.

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

3. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 2, characterized in that, In S2, the acquired raw double-pulse linear frequency modulated signal is downsampled, as expressed by: ； in, The original signal, To reduce the sampling factor, It is the downsampled signal sequence; The downsampled signal data is then passed through a bandpass filter. The frequency domain expression for the bandpass filter is as follows: ； in, downsampled signal Fourier transform, The frequency response function of a bandpass filter is defined as: ； in, That is, the filter passband is 14kHz to 24kHz.

4. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 3, characterized in that, S3 specifically refers to: The data after S2 downsampling and bandpass filtering is transformed into a one-dimensional array. The locations of all local maxima are then found using sliding window detection and local extremum retrieval. The peak value is found by summing all the local maxima sequentially and taking the maximum. The expression is: ； in, This represents the input discrete-time signal sequence. This indicates the index of the location where the signal's maximum value is located.

5. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 4, characterized in that, In S4, after finding two local peaks, the signal near the local peaks is extracted based on their positions, and the remaining data is cleared. Let the locations of the two peaks be: ； in, These are two maximum local peaks; Let the window length be Length is retained before and after each peak. The interception range is: ； Define the final signal for: 。 6. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 5, characterized in that, In S5, after normalizing the final signal data, a threshold is set to remove data below this threshold. The threshold setting depends on the system's background noise and the noise in the underwater acoustic environment, and is expressed as: ； in, It is the normalized signal data. It is the maximum value in the signal data; ； in, T The set energy threshold is used to filter out low-amplitude noise interference, retaining only the effective pulse signal portion. This is the signal after thresholding; only the values ​​greater than a certain threshold in the original signal are retained. T The part.

7. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 6, characterized in that, In S6, a decision condition is added, setting the double pulse time interval to within 100ms-400ms. Data with double pulse time intervals outside this range is cleared. The expression is: ； in, Sampling rate, Indicates the detected number The location of a local peak Indicates the detected number The location of each peak.

8. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 7, characterized in that, S7 specifically refers to: Extract the ranging pulse segment data, overlap the two peak positions, and then perform dot product summation and similarity check in sequence; : The signal segment near the first peak; : Signal segment near the second peak; in, and Indicates the original signal at , The sampled values, N This indicates the length of the new number segment to be extracted. The signal segment near the ranging pulse. This refers to the signal segment near the sounding pulse. The similarity score is obtained by calculating the normalized cross-correlation: ； in, S Represents the normalized cross-correlation coefficient. Indicates the first signal segment The value of each sampling point, Indicates the second signal segment The value of each sampling point, N Indicates the length of the new number segment to be extracted; like threshold, where the two pulses are highly similar, is the similarity judgment threshold; The time offset between pulses is represented by the x-axis from highest to lowest similarity, expressed as: ； in, and These represent signal segments centered on the two pulse peaks. Sliding offset This represents the L2 norm of the corresponding signal segment, obtained by traversing... ,calculate The maximum value of is the optimal time offset between the two pulses, which corresponds to the horizontal axis.

9. The time delay detection method based on a dual-pulse linear frequency modulated underwater acoustic signal according to claim 8, characterized in that, S8 specifically refers to: The accurate pulse rising edge position, i.e., the instantaneous delay value, is obtained by subtracting the time offset from the peak position of the ranging pulse. Define the peak position of the ranging pulse as , This is the optimal offset measured during sliding matching. If the position is the rising edge of the pulse, then: ； Based on sampling rate Calculate latency : 。

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