Underwater acoustic environment target detection method and system based on adaptive TVG

Abstract

The invention relates to the technical field of underwater acoustic detection, in particular to an underwater acoustic environment target detection method and system based on self-adaptive TVG, and the method comprises the steps: carrying out the data preprocessing of a received sonar echo signal, generating beam data, and carrying out the reverse time domain processing, thereby obtaining beam domain data; performing dynamic compression processing on the beam domain data by adopting a logarithmic method to obtain dynamically compressed beam domain data; carrying out filtering processing on the dynamically compressed beam domain data by adopting a median filtering method to obtain preliminary estimation data; performing sliding window smooth integral processing on the preliminary estimation data to obtain adaptive digital TVG curve data; by subtracting the adaptive digital TVG curve data from the horizontal beam in the dynamically compressed beam domain data, the beam data after reverberation interference suppression is obtained and pushed to the display terminal for image processing and display, accurate detection of a target in an underwater sound environment is realized, and the performance of underwater target detection is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater acoustic detection, and in particular to an underwater acoustic environment target detection method and system based on adaptive TVG. Background Art

[0002] In conventional sonar equipment, the reverberation of echoes is generally suppressed in hardware circuit design, i.e., time variable gain control (TVG). However, for different seawater environments and different transmitting pulses, the curves of TVG (time gain control) vary greatly, especially when the active pulse is relatively long and the sea depth is relatively shallow, the reverberation model will be mismatched, resulting in a decrease in sonar detection performance.

[0003] Constant false alarm detection is a commonly used processing method for sonar detection. Its basic principle is to use reference units in the distance direction of a single beam to estimate the background power level so that the detector maintains a constant false alarm probability. The reference units are usually selected from both sides of the detection unit. In the early days, people focused on the CFAR detection problem under Gaussian background, and proposed a series of detectors with CFAR characteristics. This method will show a trend of increasing false alarm probability at the edge of reverberation. It is often used to suppress reverberation interference under uniform reverberation background, and the computational overhead is large in the case of multiple beams.

[0004] The Chinese invention patent application with publication number CN118151157A introduces a multi-frame joint detection method, system and terminal for moving targets under deep-sea reverberation background. By preprocessing multi-frame echo data, an observation matrix containing a low-rank matrix and a sparse matrix is ​​obtained; the low-rank matrix and the sparse matrix are constrained respectively by non-convex norms and Markov random fields, and low-rank matrix optimization functions and sparse matrix optimization functions are constructed and combined; the low-rank matrix optimization function is solved, the sparse matrix optimization function is solved, and the reverberation low-rank matrix and the target sparse matrix are obtained; the target sparse matrix is ​​inversely quantized into multi-frame beam domain data to obtain the final detection result. The method has good detection effect under complex reverberation background, the target trajectory in the processing result is clearer, and false trajectories and discrete clutter are effectively suppressed, but the processing process has a large amount of computational complexity, and the data processing process cannot be dynamically adjusted according to different situations, and the versatility is poor. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In order to solve the deficiencies in the background technology, the present invention provides a method and system for underwater acoustic environment target detection based on adaptive TVG, which can also be applied to a single-depth multi-beam detection system by adaptively controlling the time-variable gain of echo beam data in different depth directions during data signal processing. This method can offset the impact of uneven reverberation noise in the underwater acoustic environment, reduce hardware complexity, compress the dynamic range of beam data, improve the quality of sonar target image display, and at the same time save the complex calculation of constant false alarm detection, save the calculation cost of multi-beam, and effectively improve the performance of underwater target detection.

[0007] (II) Technical solution

[0008] A method for detecting underwater acoustic environment targets based on adaptive TVG comprises the following steps:

[0009] S1, receiving the sonar echo signal through the array installed at the bottom of the ship and performing data preprocessing to generate beam data;

[0010] S2, performing inverse time domain processing on the beam data to obtain beam domain data, and dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data;

[0011] S3, using a median filter method to filter the dynamically compressed beam domain data to obtain preliminary estimated data;

[0012] S4, performing sliding window smoothing integration processing on the preliminary estimation data to obtain adaptive digital TVG curve data;

[0013] S5, obtaining beam data after reverberation interference suppression by subtracting the adaptive digital TVG curve data from the horizontal beam in the beam domain data after dynamic compression;

[0014] S6. Pushing the beam data after the reverberation interference is suppressed to a display terminal for image processing and display.

[0015] Preferably, the specific steps of receiving the sonar echo signal by installing an array at the bottom of the ship's hold and performing data preprocessing to generate beam data are as follows:

[0016] S11, receiving the sonar echo signal through the array installed at the bottom of the ship hold to obtain the sonar echo signal data;

[0017] S12, performing data preprocessing on the sonar echo signal data to generate beam data;

[0018] The data preprocessing process includes the following steps:

[0019] S121, performing channel data FFT calculation on the sonar echo signal data to obtain channel FFT data, and then multiplying the channel FFT data with beamforming steering vectors in different directions in sequence, and multiplying the product with the transmission signal copy vector to obtain beam data.

[0020] Preferably, the beam data is subjected to inverse time domain processing to obtain beam domain data, and the beam domain data is subjected to dynamic compression processing using a logarithmic method to obtain the dynamically compressed beam domain data in the following specific steps:

[0021] S21, performing inverse time domain processing on the beam data, arranging the beam data into a three-dimensional matrix with N vertical beams, M horizontal beams and K time sampling points, to obtain beam domain data;

[0022] S22, dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data; the dynamic compression processing formula is as follows:

[0023] BEAM(i,j,k)=20*log 10 {beam(i,j,k)},i=0,1,…,N; j=0,1,…M; k=0,1,…,K;

[0024] Among them, BEAM(i,j,k) represents the beam domain data after dynamic compression; beam(i,j,k) represents the beam domain data before dynamic compression.

[0025] The logarithmic method is used for dynamic compression processing. The processing process does not require input parameters and has good tolerance and robustness.

[0026] Preferably, the specific steps of filtering the dynamically compressed beam domain data using a median filter method to obtain preliminary estimated data are as follows:

[0027] S31, using the median filtering method to filter all the azimuth echo data at each time sampling point of each vertical layer in the dynamically compressed beam domain data, to obtain preliminary estimated data C(i, k), wherein the preliminary estimated data represents a preliminary estimate of the attenuation of the reverberation noise on each vertical layer over time; the filtering process is as follows:

[0028] S311. Sort all horizontal scanning beams at the kth time sampling point in the i-th vertical layer in the dynamically compressed beam domain data to obtain a sequence Y={Y(1)…Y(M)}=sort[BEAM(i,1,k),BEAM(i,2,k),…,BEAM(i,M,k)], where BEAM(i,M,k) represents the Mth horizontal scanning beam at the kth time sampling point in the i-th vertical layer, and sort[] represents sorting the array from small to large;

[0029] S312, median the sequence Y to obtain preliminary estimated data C(i,k); the median formula is as follows:

[0030] C(i,k)=Y([M / 2] 取整 )or

[0031] Wherein, L represents 2%-4% of the total length of the sequence Y.

[0032] In order to prevent the influence of occasional mechanical vibration noise, the average calculation of the median interval is adopted, that is, the beam data at the same layer and the same distance are taken, sorted by size, and the middle 2%-4% of the data are taken as the preliminary estimate of the adaptive TVG.

[0033] Preferably, the specific steps of performing sliding window smoothing integration processing on the preliminary estimation data to obtain the adaptive digital TVG curve data are as follows:

[0034] S41, performing a sliding window smoothing process on each vertical layer preliminary estimation data in the preliminary estimation data C(i, k) in time to obtain adaptive digital TVG curve data, wherein the sliding window size is adjusted according to the pulse width of the transmitted signal; the sliding window smoothing process formula is as follows:

[0035]

[0036] Wherein, TVG(i,k) represents the adaptive digital TVG curve data, and Pt represents the pulse width length of the transmission pulse.

[0037] Preferably, the specific steps of obtaining the beam data after reverberation interference suppression by subtracting the adaptive digital TVG curve data from the horizontal beam in the beam domain data after dynamic compression are as follows:

[0038] S51, sequentially subtract the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the beam domain data after dynamic compression to obtain beam data after reverberation interference suppression; the formula is as follows:

[0039] Z(i,j,k)=BEAM(i,j,k)-TVG(i,k),

[0040] Wherein, Z(i,j,k) represents the beam data after reverberation interference suppression.

[0041] Preferably, the specific steps of pushing the beam data after reverberation interference suppression to a display terminal for image processing and display are as follows:

[0042] S61. Push the beam data Z(i, j, k) after reverberation interference suppression to a display terminal through a communication network, and perform image processing and display.

[0043] The present invention also discloses an underwater acoustic environment target detection system based on adaptive TVG, including a data acquisition module, a dynamic compression module, a filtering processing module, a sliding window smoothing module and a terminal display module;

[0044] The data acquisition module receives the sonar echo signal through the array installed at the bottom of the ship, and multiplies the channel FFT data with the beamforming steering vectors in different directions in sequence, and multiplies the product with the transmission signal copy vector to obtain the beam data;

[0045] The dynamic compression module performs inverse time domain processing on the beam data, arranges the beam data into a three-dimensional matrix to obtain beam domain data, and then uses a logarithmic method to perform dynamic compression processing on the beam domain data to obtain dynamically compressed beam domain data;

[0046] The filtering processing module uses a median filtering method to filter all azimuth echo data at each time sampling point of each vertical layer in the dynamically compressed beam domain data to obtain preliminary estimation data;

[0047] The sliding window smoothing module obtains adaptive digital TVG curve data by performing temporal sliding window smoothing processing on each vertical layer preliminary estimation data in the preliminary estimation data;

[0048] The terminal display module sequentially subtracts the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the dynamically compressed beam domain data to obtain the beam data after reverberation interference suppression, and pushes it to the display terminal through the communication network for image processing and display.

[0049] (III) Beneficial effects

[0050] 1. The received sonar echo signal is preprocessed to generate beam data and perform inverse time domain processing to obtain beam domain data; the beam domain data is dynamically compressed by the logarithmic method to obtain dynamically compressed beam domain data; the dynamically compressed beam domain data is filtered by the median filter method to obtain preliminary estimated data; the preliminary estimated data is subjected to sliding window smoothing integral processing to obtain adaptive digital TVG curve data; the beam data after reverberation interference suppression is obtained by subtracting the adaptive digital TVG curve data from the horizontal beam in the dynamically compressed beam domain data and pushing it to the display terminal for image processing and display, thereby realizing accurate detection of targets in an underwater acoustic environment and effectively improving the performance of underwater target detection;

[0051] 2. In terms of anti-reverberation, the present invention can effectively suppress unbalanced reverberation, remove strong reverberation, do not affect the echo signal of weak targets, improve the detection capability, and lay the foundation for later image processing;

[0052] 3. In terms of improving computational efficiency, the adaptive TVG method can replace the computationally intensive constant false alarm detection at a relatively low cost, which is very friendly to the huge computing system of multi-channel multi-beam systems;

[0053] 4. In terms of versatility, it is applicable to both broadband and narrowband signals, and the adaptive TVG estimation can not only clearly locate the target’s direction, distance and depth information when detecting close-range targets, but also improve the target’s detection distance when detecting weak signal echo signals with long pulse width and long distance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0055] Figure 1 It is a schematic diagram of the adaptive digital TVG estimation process of the present invention;

[0056] Figure 2 A schematic diagram of adaptive digital TVG estimation on a single vertical depth level of the present invention;

[0057] Figure 3 It is a schematic diagram of the processing effects of two TVGs in the close-range fish target detection test of the present invention;

[0058] Figure 4 It is a schematic diagram of two TVG processing vertical layered sections in the close-range fish target detection test of the present invention;

[0059] Figure 5 It is a schematic diagram of the processing effects of two TVGs in the long-distance submarine target detection test of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0062] Embodiment 1 of a method and system for detecting underwater acoustic environment targets based on adaptive TVG is as follows:

[0063] See also Figure 1-Figure 2 , a method for detecting underwater acoustic environment targets based on adaptive TVG, the method comprising the following steps:

[0064] S1, receiving the sonar echo signal through the array installed at the bottom of the ship and performing data preprocessing to generate beam data;

[0065] S11, receiving the sonar echo signal through the array installed at the bottom of the ship hold to obtain the sonar echo signal data;

[0066] S12, performing data preprocessing on the sonar echo signal data to generate beam data;

[0067] The data preprocessing process includes the following steps:

[0068] S121, performing channel data FFT calculation on the sonar echo signal data to obtain channel FFT data, and then multiplying the channel FFT data with beamforming steering vectors in different directions in sequence, and multiplying the product with the transmission signal copy vector to obtain beam data.

[0069] S2, performing inverse time domain processing on the beam data to obtain beam domain data, and dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data;

[0070] S21, performing inverse time domain processing on the beam data, arranging the beam data into a three-dimensional matrix with N vertical beams, M horizontal beams and K time sampling points, to obtain beam domain data;

[0071] S22, dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data; the dynamic compression processing formula is as follows:

[0072] BEAM(i,j,k)=20*log 10 {beam(i,j,k)},i=0,1,…,N; j=0,1,…M; k=0,1,…,K;

[0073] Among them, BEAM(i,j,k) represents the beam domain data after dynamic compression; beam(i,j,k) represents the beam domain data before dynamic compression.

[0074] S3, using a median filter method to filter the dynamically compressed beam domain data to obtain preliminary estimated data;

[0075] S31, using the median filtering method to filter all the azimuth echo data at each time sampling point of each vertical layer in the dynamically compressed beam domain data, to obtain preliminary estimated data C(i, k), wherein the preliminary estimated data represents a preliminary estimate of the attenuation of the reverberation noise on each vertical layer over time; the filtering process is as follows:

[0076] S311. Sort all horizontal scanning beams at the kth time sampling point in the i-th vertical layer in the dynamically compressed beam domain data to obtain a sequence Y={Y(1)…Y(M)}=sort[BEAM(i,1,k),BEAM(i,2,k),…,BEAM(i,M,k)], where BEAM(i,M,k) represents the Mth horizontal scanning beam at the kth time sampling point in the i-th vertical layer, and sort[ ] represents sorting the array from small to large;

[0077] S312, median the sequence Y to obtain preliminary estimated data C(i,k); the median formula is as follows:

[0078] C(i,k)=Y([M / 2] 取整 )or

[0079] Wherein, L represents 2%-4% of the total length of the sequence Y.

[0080] S4, performing sliding window smoothing integration processing on the preliminary estimation data to obtain adaptive digital TVG curve data;

[0081] S41, performing a sliding window smoothing process on each vertical layer preliminary estimation data in the preliminary estimation data C(i, k) in time to obtain adaptive digital TVG curve data, wherein the sliding window size is adjusted according to the pulse width of the transmitted signal; the sliding window smoothing process formula is as follows:

[0082]

[0083] Wherein, TVG(i,k) represents the adaptive digital TVG curve data, and Pt represents the pulse width length of the transmission pulse.

[0084] S5, obtaining beam data after reverberation interference suppression by subtracting the adaptive digital TVG curve data from the horizontal beam in the beam domain data after dynamic compression;

[0085] S51, sequentially subtract the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the beam domain data after dynamic compression to obtain beam data after reverberation interference suppression; the formula is as follows:

[0086] Z(i,j,k)=BEAM(i,j,k)-TVG(i,k),

[0087] Wherein, Z(i,j,k) represents the beam data after reverberation interference suppression.

[0088] S6. Pushing the beam data after the reverberation interference is suppressed to a display terminal for image processing and display.

[0089] S61. Push the beam data Z(i, j, k) after reverberation interference suppression to a display terminal through a communication network, and perform image processing and display.

[0090] A second embodiment of a method and system for underwater acoustic environment target detection based on adaptive TVG is as follows:

[0091] An underwater acoustic environment target detection system based on adaptive TVG uses the above-mentioned underwater acoustic environment target detection method based on adaptive TVG, including a data acquisition module, a dynamic compression module, a filtering processing module, a sliding window smoothing module and a terminal display module;

[0092] The data acquisition module receives the sonar echo signal through the array installed at the bottom of the ship, and multiplies the channel FFT data with the beamforming steering vectors in different directions in sequence, and multiplies the product with the transmission signal copy vector to obtain the beam data;

[0093] The dynamic compression module performs inverse time domain processing on the beam data, arranges the beam data into a three-dimensional matrix to obtain beam domain data, and then uses a logarithmic method to perform dynamic compression processing on the beam domain data to obtain dynamically compressed beam domain data;

[0094] The filtering processing module uses a median filtering method to filter all azimuth echo data at each time sampling point of each vertical layer in the dynamically compressed beam domain data to obtain preliminary estimation data;

[0095] The sliding window smoothing module obtains adaptive digital TVG curve data by performing temporal sliding window smoothing processing on each vertical layer preliminary estimation data in the preliminary estimation data;

[0096] The terminal display module sequentially subtracts the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the dynamically compressed beam domain data to obtain the beam data after reverberation interference suppression, and pushes it to the display terminal through the communication network for image processing and display.

[0097] Embodiment 3 of a method and system for underwater acoustic environment target detection based on adaptive TVG is as follows:

[0098] A close-range fish target detection test was conducted near the Xisha Islands in an area with a depth of 200-300m. The test time is March 31, 2024.

[0099] See also Figure 3-Figure 4 The sonar is installed on the starboard side of the fishing boat. The transmitted signal is a CW signal with a center frequency of 25.5kHz, a pulse width of 0.5ms, and a sound source level of 219dB. The calculation is divided into 6 layers of beams at an interval of 5° in the vertical direction. It can be clearly found that there is a relatively large fish school reflecting object at a distance of about 154m, 20° on the starboard side, and a vertical beam of 17.5°.

[0100] In order to illustrate the effectiveness of this method, the conventional fixed TVG processing is used for comparison. The fixed TVG control gain adopts an empirical formula between the expansion attenuation of spherical waves and cylindrical waves:

[0101] TVG = 15*log10(R);

[0102] Among them, R represents the distance traveled by propagation;

[0103] Conventional methods for fixed TVG processing are shown in Figure 3 Left column, adaptive TVG method see Figure 3 Right column. The dynamic range used in the plot is 15dB. It can be seen that for close-range detection, the conventional method has obvious limitation in the surface layer of the emitted sound wave. Figure 4,Since the fixed TVG cannot evenly reduce noise at each depth level, the interpolated target graphics are split into multiple bright spot blocks, forming false targets, and unable to display effective target depth scale information.

[0104] A fourth embodiment of a method and system for underwater acoustic environment target detection based on adaptive TVG is as follows:

[0105] A long-range submarine target detection test will be conducted in Sanya at a sea depth of about 60m. The test time is November 25, 2024.

[0106] See also Figure 5 ,The sonar is installed on the bottom of the fishing boat, and the transmitted signal is an HFM signal with a center frequency of 23.5kHz, a pulse width of 200ms, and a sound source level of 219dB. Because it is in the shallow sea area, no stratification is required in the vertical direction when calculating. When drawing, the dynamic range used by the adaptive TVG processing is only 5dB, while the fixed tvg processing uses a dynamic range of 30dB due to the long pulse width and violent reverberation.

[0107] After adaptive TVG processing, the target can be clearly found at a distance of about 4070m and 20° to starboard. Figure 5 As shown on the right, by Figure 5 By comparing the fixed TVG effect shown on the left, it can be concluded that adaptive TVG can effectively reduce the impact of shallow sea reverberation and greatly improve the target detection distance.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0109] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for underwater acoustic environment target detection based on adaptive TVG, characterized in that: The steps include: S1, receiving the sonar echo signal through the array installed at the bottom of the ship and performing data preprocessing to generate beam data; S2, performing inverse time domain processing on the beam data to obtain beam domain data, and dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data; S3, using a median filter method to filter the dynamically compressed beam domain data to obtain preliminary estimated data; S4, performing sliding window smoothing integration processing on the preliminary estimation data to obtain adaptive digital TVG curve data; S5, obtaining beam data after reverberation interference suppression by subtracting the adaptive digital TVG curve data from the horizontal beam in the beam domain data after dynamic compression; S6. Pushing the beam data after the reverberation interference is suppressed to a display terminal for image processing and display.

2. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 1, characterized in that: The S1 comprises the following steps: S11, receiving the sonar echo signal through the array installed at the bottom of the ship hold to obtain the sonar echo signal data; S12, performing data preprocessing on the sonar echo signal data to generate beam data.

3. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 2 is characterized in that: The data preprocessing in S12 includes the following steps: S121, performing channel data FFT calculation on the sonar echo signal data to obtain channel FFT data, and then multiplying the channel FFT data with beamforming steering vectors in different directions in sequence, and multiplying the product with the transmission signal copy vector to obtain beam data.

4. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 3 is characterized in that: The S2 comprises the following steps: S21, performing inverse time domain processing on the beam data, arranging the beam data into a three-dimensional matrix with N vertical beams, M horizontal beams and K time sampling points, to obtain beam domain data; S22, dynamically compressing the beam domain data using a logarithmic method to obtain dynamically compressed beam domain data; the dynamic compression processing formula is as follows: BEAM(i,j,k)=20*log 10 {beam(i,j,k)},i=0,1,…,N;j=0,1,…M;k=0,1,…,K; Among them, BEAM(i,j,k) represents the beam domain data after dynamic compression; beam(i,j,k) represents the beam domain data before dynamic compression.

5. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 4 is characterized in that: The S3 comprises the following steps: S31. Use the median filtering method to filter all azimuth echo data at each time sampling point in each vertical layer in the dynamically compressed beam domain data to obtain preliminary estimated data C(i,k), which represents the preliminary estimate of the attenuation of reverberation noise on each vertical layer over time.

6. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 5, characterized in that: The filtering process in S31 includes the following steps: S311. Sort all horizontal scanning beams at the kth time sampling point in the i-th vertical layer in the dynamically compressed beam domain data to obtain a sequence Y={Y(1)…Y(M)}=sort[BEAM(i,1,k),BEAM(i,2,k),…,BEAM(i,M,k)], where BEAM(i,M,k) represents the Mth horizontal scanning beam at the kth time sampling point in the i-th vertical layer, and sort[] represents sorting the array from small to large; S312, median the sequence Y to obtain preliminary estimated data C(i,k); the median formula is as follows: C(i,k)=Y([M / 2] 取整 )or Wherein, L represents 2%-4% of the total length of the sequence Y.

7. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 6 is characterized in that: The S4 comprises the following steps: S41, performing a sliding window smoothing process on each vertical layer preliminary estimation data in the preliminary estimation data C(i, k) in time to obtain adaptive digital TVG curve data, wherein the sliding window size is adjusted according to the pulse width of the transmitted signal; the sliding window smoothing process formula is as follows: Wherein, TVG(i,k) represents the adaptive digital TVG curve data, and Pt represents the pulse width length of the transmission pulse.

8. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 7 is characterized in that: The S5 comprises the following steps: S51, sequentially subtract the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the beam domain data after dynamic compression to obtain beam data after reverberation interference suppression; the formula is as follows: Z(i,j,k)=BEAM(i,j,k)-TVG(i,k), Wherein, Z(i,j,k) represents the beam data after reverberation interference suppression.

9. The method for detecting underwater acoustic environment targets based on adaptive TVG according to claim 8, characterized in that: The S6 comprises the following steps: S61. Push the beam data Z(i, j, k) after reverberation interference suppression to a display terminal through a communication network, and perform image processing and display.

10. A system for realizing the underwater acoustic environment target detection method based on adaptive TVG as described in any one of claims 1 to 9, comprising a data acquisition module, a dynamic compression module, a filtering processing module, a sliding window smoothing module and a terminal display module, characterized in that: The data acquisition module receives the sonar echo signal through the array installed at the bottom of the ship, and multiplies the channel FFT data with the beamforming steering vectors in different directions in sequence, and multiplies the product with the transmission signal copy vector to obtain the beam data; The dynamic compression module performs inverse time domain processing on the beam data, arranges the beam data into a three-dimensional matrix to obtain beam domain data, and then uses a logarithmic method to perform dynamic compression processing on the beam domain data to obtain dynamically compressed beam domain data; The filtering processing module uses a median filtering method to filter all azimuth echo data at each time sampling point of each vertical layer in the dynamically compressed beam domain data to obtain preliminary estimation data; The sliding window smoothing module obtains adaptive digital TVG curve data by performing temporal sliding window smoothing processing on each vertical layer preliminary estimation data in the preliminary estimation data; The terminal display module sequentially subtracts the adaptive digital TVG curve data of the corresponding vertical layer from the horizontal beam of each vertical layer in the dynamically compressed beam domain data to obtain the beam data after reverberation interference suppression, and pushes it to the display terminal through the communication network for image processing and display.

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