A sonar array bearing estimation enhancement method and system

Abstract

The application provides a sonar array direction estimation enhancement method and method, and the method comprises the following steps: step 1, initializing a beam forming result, a direction estimation enhancement matrix and a different direction frequency separation matrix; step 2, sorting the beam forming result of a frequency point; step 3, calculating a direction range coordinate with the extreme point as the center and a direction density center in the coordinate range; step 4, calculating a direction range coordinate with the direction density center as the center and a new direction density center in the coordinate range; step 5, repeatedly calculating the direction estimation enhancement matrix, the beam value corresponding to the angle corresponding to a certain frequency point and the maximum beam output value in sequence; and repeatedly performing steps 2 to 5 on each frequency point in sequence to obtain the direction estimation enhancement matrix and the different direction frequency separation matrix. The application has the advantages that the weak target can be detected and tracked without dividing multiple sub-band processing result display windows.

Description

Technical Field

[0001] This invention belongs to the field of deep-sea exploration and underwater acoustic processing, and specifically relates to a method and system for enhancing the azimuth estimation of a sonar array. Background Technology

[0002] In real-world marine environments, the radiated noise of ship targets in underwater acoustic detection typically consists of a narrow-band discrete spectrum and a wide-band continuous spectrum. With advancements in vibration reduction and noise reduction technologies, the source level of radiated noise from underwater targets has been continuously lowered, making the narrow-band discrete spectrum the primary characteristic for target detection and identification. However, conventional sonar array signal processing methods require dividing the output into multiple sub-bands to distinguish the narrow-band discrete spectrum. Overly large sub-band divisions may introduce more broadband noise, reducing detection performance against weak targets; conversely, overly small sub-band divisions result in too many sub-bands requiring numerous display windows, which is detrimental to the detection and identification of weak underwater targets. Summary of the Invention

[0003] The purpose of this invention is to utilize the discrete characteristics of target radiated noise in the beam and frequency domains of array signal processing. Without requiring a specified number of targets, it achieves narrowband energy enhancement of the azimuth density center by processing different frequency points and performing weighted iterative calculations. Simultaneously, it separates the spectra of targets in different azimuths. The output after narrowband energy enhancement can be used to detect weak narrowband targets within the broadband processing results. The separation of the spectra of targets in different azimuths provides separated time-frequency data for target tracking and identification.

[0004] To achieve the above objectives, this invention proposes a sonar array azimuth estimation enhancement method, the method comprising:

[0005] Step 1: Represent the beamforming output at each moment as B(f,θ), where f is the frequency and θ is the angle; initialize the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) for different azimuths using an all-zero matrix;

[0006] Step 2: Set the i-th frequency point f i The beamforming result B(f) i Sort B in ascending order (θ) rank (f i ,θ);

[0007] Step 3: Sort the maximum value B of the beamforming result according to the extreme points. maxRank (f i ,θ), calculate the coordinates of the azimuth range centered on the extreme point and the center of azimuth density x2 within the coordinate range;

[0008] Step 4: calculate the range coordinate of the azimuth density center x2 and the new azimuth density center x3 in the coordinate range; if the azimuth density center x3 does not meet the set condition and the set repetition number is not reached, repeat step 4;

[0009] Step 5: repeat steps 3 and 4 to calculate the range coordinate corresponding to different extreme points and the maximum beam output value in the range, and the angle θ corresponding to the maximum beam output value max , and the corresponding beam value of the angle θ i ; max ;

[0010] Repeat steps 2 to 5 for each frequency point in turn to obtain the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f,θ).

[0011] As an improvement of the above method, step 1 specifically comprises:

[0012] Using narrowband frequency domain beamforming technology, the beamforming output result at each time is represented as B(f,θ), where the value range of frequency f is , the number of frequency points is N f , the value range of azimuth angle θ is , and the number of azimuth angles is N θ .

[0013] The beamforming satisfies the half-wavelength relationship of array beamforming, that is, the array element interval is less than half of the wavelength of the processed signal;

[0014] The azimuth estimation enhancement matrix A(θ) is initialized as an all-zero matrix A(θ) = zeros(1,N θ ), where zeros(1,N θ ) represents a 1×N θ all-zero vector;

[0015] The different azimuth frequency separation matrix F(f,θ) is initialized as an all-zero matrix F(f,θ) = zeros(N f ,N θ ), where zeros(N f ,N θ ) represents an N f ×N θ all-zero matrix.

[0016] As an improvement of the above method, step 2 specifically comprises:

[0017] The beamforming result B(f i ,θ) of the i-th frequency point f i is sorted in ascending order to obtain B rank (fi , θ);

[0018] Given the number of noise points M noise , calculate the average value of the smallest M i values in the beamforming result B(f i , θ) of the frequency point f noise :

[0019]

[0020] Calculate the maximum points of the beamforming result B(f i , θ) of the frequency point f i , and sort them from large to small to get B maxRank (f i , θ) and the number of maximum points M t , given the maximum point range S = [-s, …, -2, -1, 0, 1, 2, …, s], where s is a positive integer.

[0021] As an improvement of the above method, step 3 specifically includes:

[0022] According to the extreme point sorting B maxRank (f i , θ), the extreme point direction x1 = θ j is taken in turn from large to small. The coordinate X1(θ) of the direction range centered on the extreme point is calculated as:

[0023] X1(θ) = x1 + S

[0024] Take the range coordinate X2(θ) in X1(θ) that satisfies the condition 1 ≤ θ ≤ 180 as:

[0025] X2(θ) = X1(θ)

[0026] Calculate the amplitude weighted mean A sum in the coordinate range X2(θ) as:

[0027]

[0028] Where N k is the number of X2(θ) coordinates.

[0029] Calculate the direction density center x2 in the coordinate range as:

[0030]

[0031] Where Round[·] represents the integer obtained by rounding.

[0032] As an improvement of the above method, step 4 specifically includes:

[0033] The coordinates X3(θ) of the azimuth range centered on the azimuth density center x2 are calculated as follows:

[0034] X3(θ)=x2+S

[0035] Let X4(θ) be the range of coordinates in X3(θ) that satisfy the condition 1≤θ≤180.

[0036] X4(θ)=X3(θ)

[0037] Calculate the new amplitude-weighted mean A within the coordinate range X4(θ). sum for:

[0038]

[0039] Where, N m The number of X4(θ) coordinates;

[0040] The new azimuth density center x3 within this coordinate range is calculated as follows:

[0041]

[0042] If the newly calculated azimuth density center x3 does not meet the condition |x3-x2|<1 and the set number of repetitions has not been reached, then repeat step 4.

[0043] As an improvement to the above method, step 5 specifically includes:

[0044] Repeat steps 3 and 4 sequentially to calculate the range coordinates X4(θ) corresponding to different extreme points, and calculate the maximum beam output value E within this range. signal :

[0045] E signal =max[B(f i ,X4(θ))]

[0046] Where max[·] represents finding the maximum value;

[0047] Let θ be the angle corresponding to the maximum beam output value. max The azimuth estimation enhancement matrix A(θ) is calculated. max )for:

[0048] A(θ max )=A(θ max )+E signal / E n o ise

[0049] The frequency point f is calculated. i and angle θ max The corresponding beam value F(f)i ,θ max ) is:

[0050] F(f i ,θ max ) = E signal

[0051] repeating steps 2 to 5 for each frequency point f i , the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f, θ) are obtained.

[0052] The application also provides a sonar array azimuth estimation enhancement system, which is realized based on the above method and comprises:

[0053] An initialization module is configured to represent the beamforming output result of each time point as B(f, θ), wherein f is frequency and θ is angle; and the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f, θ) are initialized by using a full zero matrix.

[0054] An ordering module is configured to sort the beamforming result B(f i , θ) of the i-th frequency point f i in ascending order to obtain B rank (f i , θ).

[0055] A first azimuth density center calculation module is configured to sort the maximum value B maxRank (f i , θ) of the beamforming result according to the extreme point, and calculate the azimuth range coordinates with the extreme point as the center and the azimuth density center x2 in the coordinate range.

[0056] A second azimuth density center calculation module is configured to calculate the azimuth range coordinates with the azimuth density center x2 as the center and the new azimuth density center x3 in the coordinate range; if the azimuth density center x3 does not meet the set condition and the set repetition number is not reached, the module is repeatedly executed; and

[0057] A calculation azimuth estimation enhancement matrix and different azimuth frequency separation matrix module is configured to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module to calculate the range coordinates corresponding to different extreme points and the maximum beam output value in the range, the angle θ max corresponding to the maximum beam output value, and then calculate the beam value corresponding to the azimuth estimation enhancement matrix, the frequency point f i and the angle θ max .

[0058] The sorting module, the first orientation density center calculation module and the second orientation density center calculation module are sequentially called for each frequency point to obtain the orientation estimation enhancement matrix A (theta) and the different orientation frequency separation matrix F (f, theta).

[0059] Compared with the prior art, the advantages of the present application are that:

[0060] 1. The present application can enhance the narrowband signal in the array broadband signal beam forming processing, realize the detection performance improvement of weak targets in the broadband processing result, and realize the detection and tracking of weak targets without dividing multiple subband processing result display windows.

[0061] 2. The present application can also separate the characteristic spectrum of different orientation targets, and can further provide separated time-frequency data for the tracking and identification of targets.

[0062] 3. The present application simultaneously considers the density and weight value in a certain angle range in the calculation, and can ensure the accurate estimation of the orientation of different types of targets.

[0063] 4. The present application uses an improved density clustering iterative algorithm, reduces the complexity of the calculation through amplitude weighted orientation mean value calculation and descending search, and ensures the uniqueness of multiple estimation outputs. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 Fig. 1 shows the implementation flowchart of the sonar array orientation estimation enhancement method;

[0065] Figure 2 Fig. 2 shows the orientation estimation enhancement processing result of the verification data by using the sonar array orientation estimation enhancement method;

[0066] Figure 3 Fig. 3 shows the processing result of the verification data by using the conventional beam forming compared with the sonar array orientation estimation enhancement method;

[0067] Figure 4 Fig. 4 shows the different orientation frequency separation result of the verification data by using the sonar array orientation estimation enhancement method. DETAILED DESCRIPTION

[0068] The technical solutions of the present application will be described in detail below in combination with the drawings.

[0069] This invention proposes a sonar array azimuth estimation enhancement method. It utilizes existing mature array frequency domain beamforming algorithms to output beam domain and frequency domain estimation results. The azimuth estimation enhancement matrix and the frequency separation matrix for different azimuths are initialized with an all-zero matrix. The beamforming results at different frequency points are sequentially sorted from smallest to largest, and the noise energy at each frequency point is calculated. The maximum points of the beamforming results are then sorted from largest to smallest. Next, based on the extreme points, the amplitude-weighted mean and azimuth density center within a certain range are iteratively calculated until the iteration stops. The maximum beam output value within the azimuth range calculated in the final iteration is then calculated. Finally, the iterative calculation is repeated for each frequency point to obtain the azimuth estimation enhancement result and the frequency separation result for different azimuths.

[0070] Example 1

[0071] like Figure 1 As shown, Embodiment 1 of the present invention proposes a sonar array azimuth estimation enhancement method, comprising the following steps:

[0072] Step 1: First, using narrowband frequency domain beamforming technology, the beamforming output at each moment is represented as B(f,θ), where the frequency f ranges from... The number of frequency points is N f The azimuth angle θ takes the value of the azimuth angle θ. The number of azimuth angles is N θ Beamforming requires satisfying the half-wavelength relationship of array beamforming, that is, the spacing between array elements is less than half the wavelength of the processed signal.

[0073] The azimuth estimation enhancement matrix A(θ) = zeros(1,N) is initialized using an all-zero matrix. θ ), zeros(1,N θ ) represents 1×N θ The all-zero vector; the frequency separation matrix F(f,θ) = zeros(N) is initialized with an all-zero matrix. f N θ ), zeros(N f N θ ) represents N f ×N θ A matrix of all zeros;

[0074] Step 2: Set the frequency point f i The beamforming result B(f) i Sort B in ascending order (θ) rank (f i ,θ). Given the number of noise points M noise Calculate the frequency point f i The beamforming result B(f) i The smallest M, θ)noise The average of the values:

[0075]

[0076] Calculate the frequency point f i The beamforming result B(f) i Find the maximum points of (θ, θ) and sort them in descending order to obtain B. maxRank (f i ,θ), the number of maxima M t Given a range of maxima S = [-s,...,-2,-1,0,1,2,…,s], where s is a positive integer, usually s = 5.

[0077] Step 3: Sort B according to extreme points maxRank (f i ,θ), take the extreme point orientation x1=θ in descending order. j The coordinates of the azimuth range centered on the extreme point are calculated as follows:

[0078] X1(θ)=x1+S

[0079] Since the output of the horizontal matrix has a certain range, we only take the coordinates within that range:

[0080] X2(θ) = X1(θ), (where θ satisfies: 1 ≤ θ ≤ 180)

[0081] The number of X2(θ) coordinates is N. k ; Calculate the amplitude-weighted average value within this coordinate range:

[0082]

[0083] The azimuth density center within this coordinate range is calculated as follows:

[0084]

[0085] In this context, Round[·] represents rounding an integer to the nearest whole number.

[0086] Step 4: Calculate the coordinates of the azimuth range centered on the density center x2:

[0087] X3(θ)=x2+S

[0088] Only the coordinates within this range are taken as:

[0089] X4(θ) = X3(θ), (where θ satisfies: 1 ≤ θ ≤ 180)

[0090] The number of X4(θ) coordinates is calculated as the new N. k ; Calculate the new amplitude-weighted average value within this coordinate range:

[0091]

[0092] The new direction density center in the coordinate range is calculated as:

[0093]

[0094] If the newly calculated density center does not satisfy the following end condition:

[0095] |x3-x2|<1

[0096] Step four is repeated until the condition is satisfied. Meanwhile, a maximum number of repetitions is set, and step four is also ended when the maximum number of repetitions is reached.

[0097] Step five: Steps three and four are repeated in turn to calculate the range coordinates X4(θ) corresponding to different extreme points, and the maximum beam output value in the range is calculated:

[0098] E signal =max[B(f i ,X4(θ))]

[0099] Where max[·] represents the maximum value. The angle corresponding to the maximum beam output value is θ max . Then the azimuth estimation enhancement matrix can be calculated as:

[0100] A(θ max )=A(θ max )+E signal / E n o ise

[0101] The beam value corresponding to the frequency point f i and the direction θ max is calculated as:

[0102] F(f i ,θ max )=E signal

[0103] Steps two to five are repeated in turn for each frequency point f i , and the azimuth estimation enhancement result A(θ) and the different azimuth frequency separation results F(f,θ) can be obtained.

[0104] Figure 2The figure shows the result of the azimuth estimation enhancement processing of the verification data according to the application. The data is a 192-element horizontal array, and the processing result is in the frequency band of 100Hz to 750Hz. The figure shows the change of the estimated azimuth of the target in 10 minutes, and different dark lines represent the change of the estimated azimuth of different targets over time. Target 1 changes from about 80° to about 160°, target 2 changes around about 170°, target 3 changes around about 90°, and target 4 changes from about 70° to about 80°.

[0105] Figure 3 The figure shows the processing result of the verification data according to the conventional beamforming of the application. Figure 2 Compared with Figure 3 , the azimuth estimation results of target 1 and target 3 are significantly enhanced. The detection performance of weak targets is improved in the wideband processing result.

[0106] Figure 4 The figure shows the different azimuth frequency separation results of the verification data according to the application. The application can also separate the frequency spectrum of different azimuth targets, and can further provide separated time-frequency data for target tracking and identification without further calculation. Target 3 can be observed in the time-frequency graph. The obvious stripe curve can be used for accurate positioning and tracking of the target.

[0107] Embodiment 2

[0108] Embodiment 2 of the application provides a sonar array azimuth estimation enhancement system, which is realized based on the above method, and the system comprises:

[0109] An initialization module is configured to represent the beamforming output result at each time point as B(f, θ), wherein f is the frequency and θ is the angle; and the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f, θ) are initialized by using a full zero matrix.

[0110] An ordering module is configured to sort the beamforming result B(f i , θ) of the i th frequency point f i in ascending order to obtain B rank (f i , θ).

[0111] A first azimuth density center calculation module is configured to calculate the azimuth range coordinates centered on the extreme value point and the azimuth density center x2 in the coordinate range according to the maximum value B maxRank (f i , θ) of the beamforming result.

[0112] The second azimuth density center calculation module is configured to calculate an azimuth range coordinate centered on the azimuth density center x2 and a new azimuth density center x3 in the coordinate range, repeatedly execute the module if the azimuth density center x3 does not meet the set condition and the set repetition number is not reached, and the like.

[0113] The calculation module of the azimuth estimation enhancement matrix and the different azimuth frequency separation matrix is configured to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module in sequence to calculate the range coordinate corresponding to different extreme points and the maximum beam output value in the range, the angle θ corresponding to the maximum beam output value max , and the beam value corresponding to the angle θ. i max The calculation module of the azimuth estimation enhancement matrix and the different azimuth frequency separation matrix is configured to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module in sequence to calculate the range coordinate corresponding to different extreme points and the maximum beam output value in the range, the angle θ corresponding to the maximum beam output value

[0114] The sorting module, the first azimuth density center calculation module and the second azimuth density center calculation module are called in sequence for each frequency point to obtain the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f, θ).

[0115] The application further provides a computer device, which comprises at least one processor, a memory, at least one network interface and a user interface. The components in the device are coupled together through a bus system. It can be understood that the bus system is used to realize the connection communication between the components. The bus system comprises a data bus, a power supply bus, a control bus and a state signal bus in addition to the data bus.

[0116] The user interface can comprise a display, a keyboard or a clicking device, for example, a mouse, a trackball, a touchpad or a touch screen.

[0117] ​It can be appreciated that the memory in the embodiments disclosed in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM, PROM), an erasable programmable read-only memory (Erasable PROM, EPROM), an electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or a flash memory. The volatile memory can be a random access memory (Random Access Memory, RAM) used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synchlink DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). The memory described herein is intended to include but not limited to these and any other suitable types of memory.

[0118] In some embodiments, the memory stores elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and an application program.

[0119] Among them, the operating system includes various system programs, such as framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program includes various application programs, such as media player (Media Player), browser (Browser), etc., for implementing various application services. The program for implementing the method of the embodiments of the present disclosure can be included in the application program.

[0120] In the above-mentioned embodiments, the processor can also be used to execute the steps of the above-mentioned method by invoking the programs or instructions stored in the memory, in particular, the programs or instructions stored in the application program.

[0121] execute the steps of the above-mentioned method.

[0122] The method can be applied to a processor or implemented by the processor. The processor can be an integrated circuit chip having a signal processing capability. In implementation, the steps of the method can be completed by an integrated logic circuit of hardware in the processor or by an instruction in the form of software. The processor can be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The methods disclosed above can be implemented or executed by the processor. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed above can be directly embodied as a hardware code executed by the processor or a combination of hardware and software modules in the processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage media is located in the storage memory, and the processor reads information in the storage memory and combines the hardware to complete the steps of the method.

[0123] It can be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSP Devices), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.

[0124] For a software implementation, the techniques can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in memory and executed by processors. The memory can be implemented within the processor or external to the processor.

[0125] The application further provides a nonvolatile storage medium for storing the computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for enhancing the azimuth estimation of a sonar array, the method comprising: Step 1: Represent the beamforming output at each moment as B(f,θ), where f is the frequency and θ is the angle; initialize the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) for different azimuths using an all-zero matrix; Step 2: Set the i-th frequency point f i The beamforming result B(f) i Sort B in ascending order (θ) rank (f i ,θ); Step 3: Sort the maximum value B of the beamforming result according to the extreme points. maxRank (f i ,θ), calculate the coordinates of the azimuth range centered on the extreme point and the azimuth density center x2 within the coordinates of that range; Step 4: Calculate the azimuth range coordinates centered on the azimuth density center x2 and the new azimuth density center x3 within that range coordinates; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions has not been reached, repeat step 4. Step 5: Repeat steps 3 and 4 to calculate the range coordinates corresponding to different extreme points, the maximum beam output value within that range, and the angle θ corresponding to the maximum beam output value. max Then, the azimuth estimation enhancement matrix and frequency point f are calculated. i and angle θ max The corresponding beam value; Steps 2 to 5 are repeated for each frequency point to obtain the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) for different azimuths.

2. The sonar array azimuth estimation enhancement method according to claim 1, characterized in that, Step 1 specifically includes: Using narrowband frequency domain beamforming technology, the beamforming output at each moment is represented as B(f,θ), where the frequency f ranges from... The number of frequency points is N f The azimuth angle θ takes the value of the azimuth angle θ. The number of azimuth angles is N θ ; Beamforming satisfies the half-wavelength relationship of array beamforming, that is, the array element spacing is less than half the wavelength of the processed signal. The azimuth estimation enhancement matrix A(θ) = zeros(1,N) is initialized using an all-zero matrix. θ ), zeros(1,N θ ) represents 1×N θ A vector of all zeros; The frequency separation matrix F(f,θ) = zeros(N) is initialized using an all-zero matrix. f N θ ), zeros(N f N θ ) represents N f ×N θ A matrix of all zeros.

3. The sonar array azimuth estimation enhancement method according to claim 2, characterized in that, Step 2 specifically includes: Let the i-th frequency point f i The beamforming result B(f) i Sort B in ascending order (θ) rank (f i ,θ); Given the number of noise points M noise Calculate the frequency point f i The beamforming result B(f) i The smallest M in ,θ) noise The average of the values: Calculate the frequency point f i The beamforming result B(f) i Find the maximum points of (θ, θ) and sort them in descending order to obtain B. maxRank (f i (θ) and the number of maxima M t Given a range of maxima S = [-s,...,-2,-1,0,1,2,...,s], where s is a positive integer.

4. The sonar array azimuth estimation enhancement method according to claim 3, characterized in that, Step 3 specifically includes: Sort by extreme points B maxRank (f i ,θ), take the extreme point orientation x1=θ in descending order. j ; The coordinates X1(θ) of the azimuth range centered on the extreme point are calculated as follows: X1(θ)=x1+S Let X1(θ) be the range of coordinates X2(θ) that satisfy the condition 1≤θ≤180. X2(θ)=X1(θ) Calculate the amplitude-weighted average value A within the coordinate range X2(θ). sum for: Where, N k The number of X2(θ) coordinates; The azimuth density center x2 within this coordinate range is calculated as follows: In this context, Round[·] represents rounding an integer to the nearest whole number.

5. The sonar array azimuth estimation enhancement method according to claim 4, characterized in that, Step 4 specifically includes: The coordinates X3(θ) of the azimuth range centered on the azimuth density center x2 are calculated as follows: X3(θ)=x2+S Let X4(θ) be the range of coordinates in X3(θ) that satisfy the condition 1≤θ≤180. X4(θ)=X3(θ) Calculate the new amplitude-weighted mean A within the coordinate range X4(θ). sum for: Where, N m The number of X4(θ) coordinates; The new azimuth density center x3 within this coordinate range is calculated as follows: If the newly calculated azimuth density center x3 does not meet the condition |x3-x2|<1 and the set number of repetitions has not been reached, then repeat step 4.

6. The sonar array azimuth estimation enhancement method according to claim 5, characterized in that, Step 5 specifically includes: Repeat steps 3 and 4 sequentially to calculate the range coordinates X4(θ) corresponding to different extreme points, and calculate the maximum beam output value E within this range. signal : E signal =max[B(f i ,X4(θ))] Where max[·] represents finding the maximum value; Let θ be the angle corresponding to the maximum beam output value. max The azimuth estimation enhancement matrix A(θ) is calculated. max )for: A(θ max )=A(θ max )+E signal / E noise The frequency point f is calculated. i and angle θ max The corresponding beam value F(f) i ,θ max )for: F(f i ,i max )=E signal For each frequency point f in sequence i Repeat steps 2 to 5 to obtain the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) for different azimuths.

7. A sonar array orientation estimation enhancement system, used to implement the method according to any one of claims 1-6, characterized in that, The system includes: The initialization module is used to represent the beamforming output at each time step as B(f,θ), where f is the frequency and θ is the angle; the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) of different azimuths are initialized using an all-zero matrix. The sorting module is used to sort the i-th frequency point f i The beamforming result B(f) i Sort B in ascending order (θ) rank (f i ,θ); The first azimuth density center calculation module is used to sort the maximum value B of the beamforming results according to the extreme points. maxRank (f i ,θ), calculate the coordinates of the azimuth range centered on the extreme point and the azimuth density center x2 within the coordinates of that range; The second azimuth density center calculation module is used to calculate the azimuth range coordinates centered on the azimuth density center x2 and the new azimuth density center x3 within that range coordinates; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions has not been reached, this module is executed repeatedly; and The module for calculating the azimuth estimation enhancement matrix and the frequency separation matrix for different azimuths is used to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module to calculate the range coordinates corresponding to different extreme points, the maximum beam output value within that range, and the angle θ corresponding to the maximum beam output value. max Then, the azimuth estimation enhancement matrix and frequency point f are calculated. i and angle θ max The corresponding beam value; The sorting module, the first azimuth density center calculation module, and the second azimuth density center calculation module are called sequentially for each frequency point to obtain the azimuth estimation enhancement matrix A(θ) and the frequency separation matrix F(f,θ) for different azimuths.

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