Sonar array orientation estimation enhancement method and system

Abstract

The invention provides a sonar array orientation estimation enhancement method and a sonar array orientation estimation enhancement method. The sonar array orientation estimation enhancement method comprises the following steps: step 1, initializing a beam forming result, an orientation estimation enhancement matrix and a different orientation frequency separation matrix; 2, sorting beam forming results of one frequency point; 3, calculating an azimuth range coordinate taking the extreme point as the center and an azimuth density center in the coordinate range; 4, calculating an azimuth range coordinate taking the azimuth density center as the center and a new azimuth density center in the coordinate range; 5, the step 3 and the step 4 are repeated in sequence to calculate a direction estimation enhancement matrix, a certain frequency point and a beam value corresponding to the angle corresponding to the maximum beam output value; and repeating the step 2 to the step 5 on each frequency point in sequence to obtain an orientation estimation enhancement matrix and a different orientation frequency separation matrix. The method has the advantage that the weak target can be detected and tracked without dividing a plurality of sub-band processing result display windows.

Description

Technical Field

[0001] The present invention belongs to the field of deep sea exploration and underwater acoustic processing, and in particular relates to a sonar array azimuth estimation enhancement method and system. Background Art

[0002] In actual marine environments, the radiation noise of ship targets in underwater acoustic detection is usually composed of a narrow-band discrete spectrum and a wide-band continuous spectrum. With the development of vibration reduction and noise reduction technology, the radiation noise source level of underwater targets continues to decrease, and the narrow-band discrete spectrum has become the main feature for detecting and identifying targets. However, in order to distinguish the narrow-band discrete spectrum, conventional sonar array signal processing methods need to divide the results into multiple different sub-bands for output when displaying the results. If the sub-band division is too large, more broadband noise may be introduced, reducing the detection performance of weak targets; if the sub-band division is too small, too many sub-band divisions will result in a large number of display windows, which is not conducive to the detection and identification of weak underwater targets. Summary of the invention

[0003] The purpose of the present invention is to utilize the discrete characteristics of target radiation noise in the array signal processing beam domain and frequency domain, and to achieve narrowband energy enhancement of broadband array beamforming azimuth estimation results and separation of target spectra in different azimuths by processing different frequency points and weighted iterative calculation of azimuth density centers without specifying the number of targets. The output result after narrowband energy enhancement can detect weak narrowband targets in the broadband processing result. The separation of target spectra in different azimuths can provide separated time-frequency data for target tracking and identification.

[0004] In order to achieve the above object, the present invention proposes a sonar array azimuth estimation enhancement method, the method comprising:

[0005] Step 1: The beamforming output at each moment is expressed as B(f,θ), where f is the frequency and θ is the angle; the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ) are initialized with an all-zero matrix;

[0006] Step 2: Set the i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ);

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

[0008] Step 4: Calculate the azimuth range coordinates centered on the azimuth density center x2 and the new azimuth density center x3 within the coordinate range; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions is not reached, repeat step 4;

[0009] Step 5: Repeat steps 3 and 4 to calculate the range coordinates corresponding to different extreme points, the maximum beam output value within the range, and the angle θ corresponding to the maximum beam output value. max , and then calculate the direction estimation enhancement matrix and frequency point f i and angle θ max The corresponding beam value;

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

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

[0012] Using narrowband frequency domain beamforming technology, the beamforming output at each moment is expressed as B(f,θ), where the value range of frequency f is The number of frequency points is N f , the azimuth angle θ is The number of azimuth angles is N θ ;

[0013] The 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;

[0014] Use the all-zero matrix to initialize the orientation estimation enhancement matrix A(θ)=zeros(1,N θ ), zeros(1,N θ ) represents 1×N θ An all-zero vector of ;

[0015] Use all zero matrices to initialize the frequency separation matrices for different azimuths F(f,θ)=zeros(N f ,N θ ), zeros(N f ,N θ ) indicates N f ×N θ An all-zero matrix.

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

[0017] The i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (fi ,θ);

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

[0019]

[0020] Calculate the frequency point f i The beamforming result B(f i ,θ) 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] Sort by extreme points B maxRank (f i ,θ), take the extreme point orientation x1=θ from large to small j ; Calculate the azimuth range coordinates X1(θ) centered at the extreme point as:

[0023] X1(θ)=x1+S

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

[0025] X2(θ)=X1(θ)

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

[0027]

[0028] Among them, N k is the number of X2(θ) coordinates;

[0029] Calculate the azimuth density center x2 within the coordinate range as:

[0030]

[0031] Here, Round[·] indicates the integer to be rounded off.

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

[0033] The azimuth range coordinate X3(θ) centered on the azimuth density center x2 is calculated as:

[0034] X3(θ)=x2+S

[0035] Take the range coordinate X4(θ) in X3(θ) that satisfies the condition 1≤θ≤180 as:

[0036] X4(θ)=X3(θ)

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

[0038]

[0039] Among them, N m is the number of X4(θ) coordinates;

[0040] Calculate the new azimuth density center x3 within the coordinate range as:

[0041]

[0042] If the newly calculated azimuth density center x3 does not satisfy the condition |x3-x2|<1 and the set number of repetitions is not reached, step 4 is repeated.

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

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

[0045] E signal =max[B(f i ,X4(θ))]

[0046] Among them, max[·] means finding the maximum value;

[0047] Set the angle corresponding to the maximum beam output value to θ max , calculate the orientation estimation enhancement matrix A(θ max )for:

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

[0049] Calculate the frequency point f i and angle θ max The corresponding beam value F(fi ,θ max )for:

[0050] F(f i ,θ max )=E signal

[0051] For each frequency point f i Repeat steps 2 to 5 to obtain the azimuth estimation enhancement matrix A(θ) and the different azimuth frequency separation matrix F(f,θ).

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

[0053] An initialization module is used to represent the beamforming output results at each moment as B(f,θ), where f is the frequency and θ is the angle; an all-zero matrix is ​​used to initialize the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ);

[0054] The sorting module is used to sort the i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ);

[0055] The first azimuth density center calculation module is used to sort the maximum value B of the beamforming result according to the extreme point maxRank (f i ,θ), calculate the azimuth range coordinates centered on the extreme point and the azimuth density center x2 within the coordinate range;

[0056] 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 the coordinate range; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions is not reached, this module is repeatedly executed; and

[0057] The module for calculating the azimuth estimation enhancement matrix and the different azimuth frequency separation matrix is ​​used to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module in sequence to calculate the range coordinates corresponding to different extreme points and the maximum beam output value within the range, and the angle θ corresponding to the maximum beam output value max , and then calculate the direction estimation enhancement matrix and frequency point f i and angle θ max The corresponding beam value;

[0058] The sorting module, the first azimuth density center calculation module and the second azimuth density center calculation module are called 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).

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

[0060] 1. The present invention can enhance the narrowband signal in the array broadband signal beamforming processing, and improve the detection performance of weak targets in the broadband processing results. It is not necessary to divide the processing result display windows into multiple sub-bands to realize the detection and tracking of weak targets.

[0061] 2. The present invention can also separate the characteristic frequency spectra of targets in different directions, and can further provide separated time-frequency data for target tracking and identification without further calculation.

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

[0063] 4. The present invention uses an improved density clustering iterative algorithm, which reduces the complexity of calculation through amplitude-weighted azimuth mean calculation and descending search, ensuring that multiple estimation outputs are unique. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The figure shows a flow chart of the sonar array azimuth estimation enhancement method implementation;

[0065] Figure 2 The figure shows the result of the azimuth estimation enhancement processing of the verification data using the sonar array azimuth estimation enhancement method;

[0066] Figure 3 Shown is the result of conventional beamforming on validation data compared with the sonar array bearing estimation enhancement method;

[0067] Figure 4 Shown are the results of frequency separation of different azimuths of the verification data using the sonar array azimuth estimation enhancement method. DETAILED DESCRIPTION

[0068] The technical solution of the present invention is described in detail below with reference to the accompanying drawings.

[0069] The present invention proposes a sonar array azimuth estimation enhancement method, which utilizes an existing mature array frequency domain beamforming algorithm to output beam domain and frequency domain estimation results, and uses a full-zero matrix to initialize an azimuth estimation enhancement matrix and a different azimuth frequency separation matrix; the beamforming results of different frequency points are sequentially sorted from small to large, the noise energy at different frequency points is calculated, and the maximum value points of the beamforming results are sorted from large to small; then, according to the extreme value points, the amplitude weighted mean and the azimuth density center within a certain range are iteratively calculated in sequence until the iteration stop requirement is met, and the maximum beam output value within the azimuth range of the last iterative calculation is calculated; finally, the iterative calculation is repeated for each frequency point in sequence to obtain the azimuth estimation enhancement result and the different azimuth frequency separation result.

[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, use narrowband frequency domain beamforming technology to express the beamforming output results at each moment as B(f,θ), where the value range of frequency f is The number of frequency points is N f , the azimuth angle θ is The number of azimuth angles is N θ The beamforming must satisfy the half-wavelength relationship of array beamforming, that is, the array element spacing is less than half the wavelength of the processed signal.

[0073] Use the all-zero matrix to initialize the orientation estimation enhancement matrix A(θ)=zeros(1,N θ ), zeros(1,N θ ) represents 1×N θ All zero vectors; use all zero matrices to initialize the frequency separation matrices of different orientations F(f,θ)=zeros(N f ,N θ ), zeros(N f ,N θ ) indicates N f ×N θ The all-zero matrix of

[0074] Step 2: Set the frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ). Given the number of noise points M noise , calculate the frequency point f i The beamforming result B(f i ,θ) with the smallest Mnoise The average of the values:

[0075]

[0076] Calculate the frequency point f i The beamforming result B(f i ,θ) and sort them from large to small to get B maxRank (f i ,θ), 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, usually s = 5.

[0077] Step 3: Sort B by extreme points maxRank (f i ,θ), take the extreme point orientation x1=θ from large to small j ; Calculate the azimuth range coordinates centered on the extreme point as:

[0078] X1(θ)=x1+S

[0079] Since the horizontal array output results have a certain range, only the coordinates within this range are taken as:

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

[0081] The number of X2(θ) coordinates is N k ; Calculate the weighted mean of the amplitude within the coordinate range:

[0082]

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

[0084]

[0085] Here, Round[·] indicates the integer to be rounded off.

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

[0087] X3(θ)=x2+S

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

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

[0090] Calculate the number of X4(θ) coordinates as the new N k ; Calculate the new amplitude weighted mean within the coordinate range as:

[0091]

[0092] Calculate the new azimuth density center within the coordinate range as:

[0093]

[0094] If the newly calculated density center does not meet the following termination conditions:

[0095] |x3-x2|<1

[0096] Then repeat step 4 until the condition is met. At the same time, set the maximum number of repetitions, and end step 4 when the maximum number of repetitions is reached.

[0097] Step 5: Repeat steps 3 and 4 to calculate the range coordinates X4(θ) corresponding to different extreme points, and calculate the maximum beam output value within the range:

[0098] E signal =max[B(f i ,X4(θ))]

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

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

[0101] Calculate the frequency point f i and orientation θ max The corresponding beam values ​​are:

[0102] F(f i ,θ max )=E signal

[0103] For each frequency point f i Repeating steps 2 to 5, we can obtain the azimuth estimation enhancement result A(θ) and the frequency separation results of different azimuths F(f,θ).

[0104] Figure 2The figure shows the enhanced processing result of the azimuth estimation of the verification data by the present invention. 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 target azimuth within 10 minutes, and the different dark lines represent the change of the estimated azimuth of different targets over time. Among them, target 1 changes from about 80° to about 160° in azimuth; target 2 changes from about 170° in azimuth; target 3 changes from about 90° in azimuth; target 4 changes from about 70° to about 80° in azimuth.

[0105] Figure 3 Shown is the result of processing verification data using conventional beamforming as a comparison with the present invention. Figure 2 Compared to 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 broadband processing results.

[0106] Figure 4 The figure shows the frequency separation results of different orientations of the verification data by the present invention. The present invention can also separate the characteristic spectra of targets in different orientations, and can further provide separated time-frequency data for target tracking and identification without further calculation. Obvious stripe curves can be observed for target 3 in the time-frequency diagram, and this feature can be used to accurately locate and track the target.

[0107] Example 2

[0108] Embodiment 2 of the present invention provides a sonar array azimuth estimation enhancement system, which is implemented based on the above method, and the system includes:

[0109] An initialization module is used to represent the beamforming output results at each moment as B(f,θ), where f is the frequency and θ is the angle; an all-zero matrix is ​​used to initialize the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ);

[0110] The sorting module is used to sort the i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ);

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

[0112] 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 the coordinate range; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions is not reached, this module is repeatedly executed; and

[0113] The module for calculating the azimuth estimation enhancement matrix and the different azimuth frequency separation matrix is ​​used to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module in sequence to calculate the range coordinates corresponding to different extreme points and the maximum beam output value within the range, and the angle θ corresponding to the maximum beam output value max , and then calculate the direction estimation enhancement matrix and frequency point f i and angle θ max The corresponding beam value;

[0114] The sorting module, the first azimuth density center calculation module and the second azimuth density center calculation module are called 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).

[0115] The present invention may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0116] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0117] It is understood that the memory in the embodiments disclosed in the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

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

[0119] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0120] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0121] Execute the steps of the above method.

[0122] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above-disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the above-disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0123] It is understood that the embodiments described in the present invention can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0124] For software implementation, the technology of the present invention can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present invention. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0125] The present invention may also provide a non-volatile storage medium for storing a 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 invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. A sonar array azimuth estimation enhancement method, the method comprising: Step 1: The beamforming output at each moment is expressed as B(f,θ), where f is the frequency and θ is the angle; the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ) are initialized with an all-zero matrix; Step 2: Set the i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ); Step 3: Sort the maximum value B of the beamforming result by extreme value points maxRank (f i ,θ), calculate the azimuth range coordinates centered on the extreme point and the azimuth density center x2 within the coordinate range; Step 4: Calculate the azimuth range coordinates centered on the azimuth density center x2 and the new azimuth density center x3 within the coordinate range; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions is not 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 the range, and the angle θ corresponding to the maximum beam output value. max , and then calculate the direction estimation enhancement matrix and frequency point f i and angle θ max The corresponding beam value; Repeat steps 2 to 5 for each frequency point in turn to obtain the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ).

2. The sonar array azimuth estimation enhancement method according to claim 1, characterized in that: The step 1 specifically includes: Using narrowband frequency domain beamforming technology, the beamforming output at each moment is expressed as B(f,θ), where the value range of frequency f is The number of frequency points is N f , the azimuth angle θ is The number of azimuth angles is N θ ; The 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; Use the all-zero matrix to initialize the orientation estimation enhancement matrix A(θ)=zeros(1,N θ ), zeros(1,N θ ) represents 1×N θ An all-zero vector of ; Use all zero matrices to initialize the frequency separation matrices for different azimuths F(f,θ)=zeros(N f ,N θ ), zeros(N f ,N θ ) indicates N f ×N θ An all-zero matrix.

3. The sonar array azimuth estimation enhancement method according to claim 2, characterized in that: The step 2 specifically includes: The i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ); Given the number of noise points M noise , calculate the frequency point f i The beamforming result B(f i ,θ) is the smallest M noise The average of the values: Calculate the frequency point f i The beamforming result B(f i ,θ) 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.

4. The sonar array azimuth estimation enhancement method according to claim 3, characterized in that: The step 3 specifically includes: Sort by extreme points B maxRank (f i ,θ), take the extreme point orientation x1=θ from large to small j ; The azimuth range coordinates X1(θ) centered at the extreme point are calculated as: X1(θ)=x1+S Take the range coordinates X2(θ) in X1(θ) that satisfy the condition 1≤θ≤180 as: X2(θ)=X1(θ) Calculate the amplitude weighted mean A within the coordinate range X2(θ) sum for: Among them, N k is the number of X2(θ) coordinates; Calculate the azimuth density center x2 within the coordinate range as: Here, Round[·] indicates the integer to be rounded off.

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

6. The sonar array azimuth estimation enhancement method according to claim 5, characterized in that: The step 5 specifically includes: Repeat steps 3 and 4 to calculate the range coordinates X4(θ) corresponding to different extreme points, and calculate the maximum beam output value E in the range. signal : E signal =max[B(f i ,X4(θ))] Among them, max[·] means finding the maximum value; Set the angle corresponding to the maximum beam output value to θ max , calculate the orientation estimation enhancement matrix A(θ max )for: A(θ max )=A(θ max )+E signal / E noise Calculate the frequency point f 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 i Repeat steps 2 to 5 to obtain the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ).

7. A sonar array azimuth estimation enhancement system, implemented based on any of the methods described in claims 1-6, characterized in that: The system comprises: An initialization module is used to represent the beamforming output results at each moment as B(f,θ), where f is the frequency and θ is the angle; an all-zero matrix is ​​used to initialize the azimuth estimation enhancement matrix A(θ) and the azimuth frequency separation matrix F(f,θ); The sorting module is used to sort the i-th frequency point f i The beamforming result B(f i ,θ) Sort by small to large to get B rank (f i ,θ); The first azimuth density center calculation module is used to sort the maximum value B of the beamforming result according to the extreme point maxRank (f i ,θ), calculate the azimuth range coordinates centered on the extreme point and the azimuth density center x2 within the coordinate 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 the coordinate range; if the azimuth density center x3 does not meet the set conditions and the set number of repetitions is not reached, this module is repeatedly executed; and The module for calculating the azimuth estimation enhancement matrix and the different azimuth frequency separation matrix is ​​used to repeatedly call the first azimuth density center calculation module and the second azimuth density center calculation module in sequence to calculate the range coordinates corresponding to different extreme points and the maximum beam output value within the range, and the angle θ corresponding to the maximum beam output value max , and then calculate the direction estimation enhancement matrix and frequency point f 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 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).

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