Active sonar gridding detection capability evaluation method, system, equipment and medium

Abstract

The invention provides an active sonar gridding detection capability evaluation method, system and device, and a medium. The method comprises the following steps: generating grid region set data of each sonar detection level; constructing a sonar detection capability index set; an active sonar equation is utilized, influence factors of the environment, sonar and a target are considered, the maximum detection distance is calculated, and detection range data are generated in combination with a blind area range; dynamically quantifying the detectability of the sonar in different grid areas; performing weight configuration on each index by adopting an order relation analysis method, and calculating the sonar detection capability in a weighted sum form until all grids are traversed and calculated; and displaying the evaluation value of the sonar detection capability index. According to the invention, fine dynamic quantification of the detection capability of the sonar sensor for different targets in different detection level grid areas is realized, and the method is more scientific and objective compared with a traditional mode; and index weight assignment is carried out by adopting an order relation analysis method, so that the method is simpler than a traditional analytic hierarchy process.

Description

Technical Field

[0001] The present invention relates to the field of intersection between sonar signal processing and effectiveness evaluation, and in particular to an active sonar grid detection capability evaluation method, system, device and medium. Background Art

[0002] After a century of development, sonar sensors have formed five generations of sonar sensors. Under the demand for large-area underwater and surface target detection and the development of unmanned technology, sonar has evolved from the initial bow array medium-frequency sonar to a multi-functional unmanned sonar, which is used in major platforms such as surface ships, submarines, and unmanned underwater vehicles. Its role in detection is becoming increasingly important. At present, most of the evaluation of sonar detection capabilities relies on the static performance index data of the sensor. If the environment, the detected target, the distance, etc. change, the static performance data will become invalid.

[0003] In order to improve the accuracy and scientificity of sonar performance evaluation in dynamic situations, it is urgent to develop a dynamic sonar detection capability evaluation method and establish a unified detection capability evaluation index system. With the discrete distance point idea as the core, by considering the impact of multi-dimensional factors such as environment, detected target, sonar sensor, etc. on sonar detection capability, a sonar dynamic detection capability measurement model combining theory, experience, and statistical experiments is constructed to achieve refined measurement of sonar's capabilities for each detection area. Summary of the invention

[0004] The present invention aims to provide a method, system, device and medium for evaluating the grid detection capability of active sonar to solve the above-mentioned problems.

[0005] The present invention provides an active sonar grid detection capability evaluation method, which is characterized by comprising the following steps:

[0006] Step 1: Discretely divide the underwater surveillance area into several sonar detection level areas according to the water depth, and generate grid area collection data of each sonar detection level;

[0007] Step 2: Combined with the daily sonar monitoring tasks of sea surface and underwater targets, a set of sonar detection capability indicators is constructed according to the principle of constructing a hierarchical indicator system;

[0008] Step 3: Use the active sonar equation to consider the influencing factors of the environment, sonar, and target, calculate the maximum detection distance, and generate the detection range data in combination with the blind area range;

[0009] Step 4: Dynamically quantify the detection capability of sonar in different grid areas by combining theory, experiment and experience;

[0010] Step 5: Use the order relationship analysis method to configure the weights of each indicator and calculate the sonar detection capability in the form of weighted sum until all grids are traversed and calculated;

[0011] Step 6: Display the evaluation values ​​of the sonar detection capability indicators, and distinguish and display the comprehensive detection capability of each grid area.

[0012] The present invention provides an active sonar grid detection capability evaluation system, comprising:

[0013] The regional gridding module is used to discretely divide the underwater surveillance area into several sonar detection level areas according to the water depth, and generate grid area collection data of each sonar detection level;

[0014] The detection capability indicator construction module is used to build a set of sonar detection capability indicators based on the principle of hierarchical indicator system construction in combination with the daily sonar monitoring tasks of sea surface and underwater targets;

[0015] The detection range calculation module is used to calculate the maximum detection distance by using the active sonar equation, taking into account the influencing factors of the environment, sonar, and target, and generate the detection range data in combination with the blind area range;

[0016] The grid area capability index measurement module is used to dynamically quantify the detection capability of the sonar in different grid areas by combining theory, experiment and experience;

[0017] The grid area comprehensive capability quantification module is used to configure the weights of various indicators using the order relationship analysis method, and calculate the sonar detection capability in the form of weighted sum until all grids are traversed and calculated;

[0018] The detection capability visualization module is used to display the evaluation values ​​of the sonar detection capability indicators and to differentiate the comprehensive detection capability of each grid area.

[0019] The present invention provides an electronic device, comprising:

[0020] at least one processor; and a memory communicatively coupled to the at least one processor;

[0021] The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the above method by executing the instructions stored in the memory.

[0022] The present invention provides a computer-readable storage medium, which is used to store instructions. When the instructions are executed, the above method is implemented.

[0023] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] 1. The present invention comprehensively considers the influencing factors such as sonar, environment, target, sensor coverage size, etc., and adopts a combination of theory, experimental statistical analysis and experience to achieve fine dynamic quantification of the sonar sensor's detection capability for different targets in grid areas of different detection levels. Compared with the traditional static capability measurement method, it is more scientific and objective.

[0025] 2. The present invention adopts the ordinal relationship analysis method (G1 method) to assign index weights. Compared with the traditional hierarchical analysis method, it is more concise in calculation, does not require the calculation of characteristic vectors and consistency checks, and is more suitable for engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Flow chart of the active sonar grid detection capability evaluation method in an embodiment of the present invention.

[0027] Figure 2 This is a diagram of the sonar detection capability indicator set architecture in an embodiment of the present invention.

[0028] Figure 3 Schematic diagram of an active sonar grid detection capability evaluation system in an embodiment of the present invention.

[0029] Figure 4 It is a schematic diagram of an electronic device that implements the active sonar grid detection capability evaluation method and system in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0032] like Figure 1 As shown, this embodiment proposes an active sonar grid detection capability evaluation method, comprising the following steps:

[0033] Step 1: Divide the underwater surveillance area into several sonar detection levels according to the water depth, and then perform a series of The hexagon is divided into Grid Area Where m = 1, 2, ..., M is the sonar detection level area number, n = 1, 2, ..., N (m) is the grid area number of the mth sonar detection level area, to They are the six endpoint vectors of the nth grid area in the mth sonar detection level area, including the x and y direction coordinates in the Cartesian coordinate system. The grid area collection data of each sonar detection level is

[0034] Step 2: Combined with the daily sonar monitoring tasks of sea surface and underwater targets, according to the principle of constructing a hierarchical indicator system, a set of sonar detection capability indicators with three major capabilities and eight performance indicators is constructed. Figure 2 As shown:

[0035] The three major capabilities include target detection capability A1, positioning and tracking capability A2, and target identification capability A3;

[0036] The eight performance indicators include the target detection probability B1 and the maximum number of target detections B2 under the target detection capability A1, the lateral accuracy B3, ranging accuracy B4, target track continuity B5 and target tracking delay B6 under the positioning and tracking capability A2, and the target recognition probability B7 and target recognition accuracy B8 under the target recognition capability A3.

[0037] Step 3: Use the active sonar equation to consider the influencing factors of the environment, sonar, and target, calculate the maximum detection distance, and generate the detection range data E based on the blind spot azimuth range. t = {P1,P2,…,P Q ,P1}, where P1,P2,…,P Q are the endpoint vectors of the detection range, including the x and y coordinates in the Cartesian coordinate system. The specific maximum detection distance is calculated as follows:

[0038] Set the target scattering cross-sectional area, sonar sensor parameters (including sonar source level, acoustic signal frequency, acoustic signal bandwidth, sonar sensor array parameters, etc.), and environmental parameters, and calculate the propagation distance r when the sonar margin is 0 according to the active sonar equation. The calculation formula for the maximum detection distance is:

[0039] SE a =SL-2TL+TS-(NL-DI+DT)+AG

[0040] The meaning and calculation of each parameter are as follows:

[0041] ①SL is the active sonar source level;

[0042] ②TL is the propagation loss, including the wavefront expansion loss TL 1(扩展项) and dielectric sound absorption loss TL 2(吸收项) , the unit is dB, the specific calculation formula is as follows:

[0043] TL=TL 1(扩展项) +TL 2(吸收项)

[0044] Where, the wavefront expansion loss TL 1(扩展项) Calculated by engineering experience formula, specifically:

[0045] TL 1(扩展项) =β·10lgr

[0046] Where r is the propagation distance in meters, and β is a constant determined by the sound wave generation conditions. The specific corresponding table is shown in Table 1.

[0047] Table 1, β and wavefront expansion loss TL 1(扩展项) Correspondence table:

[0048] β <![CDATA[TL 1(扩展项) ]]> Applicable conditions 0 0 Plane wave propagation 1 10lgr Cylindrical wave propagation 3 / 2 15lgr Shallow sea sound propagation considering seabed sound absorption 2 20lgr Spherical wave propagation 3 30lgr Propagation loss of sound waves after passing through the shallow sea negative layer 4 40lgr After considering multipath interference, the sound propagation loss in the far field

[0049] Dielectric sound absorption loss TL 2(吸收项) The specific calculation formula is as follows:

[0050]

[0051] Where α is the sound absorption coefficient of seawater, expressed in decibels per kilometer, which is calculated using the empirical formula as follows:

[0052]

[0053] Where f is the sound signal frequency in kHz.

[0054] ③TS is the target intensity in dB, calculated using the Rayleigh scattering model:

[0055] TS=10*log10(σ rcs / π)

[0056] In the formula, σ rcs is the target scattering cross-sectional area.

[0057] ④AG is the space-time processing gain in dB and is calculated using the following formula.

[0058] AG=10lgBT

[0059] Among them, B is the bandwidth of the acoustic signal and T is the pulse width of the acoustic signal.

[0060] ⑤DI is the receiving directivity parameter, which is used to measure the noise suppression capability of the sonar sensor receiver. It can be calculated through the theory of spatial filtering. The specific formula is:

[0061]

[0062] Where b(θ) is the number of array elements n b , spacing d, azimuth θ, and the directivity function of a linear array; λ is the wavelength.

[0063] ⑥NL is the ambient noise spectrum level, which can be quantified according to the seven different curves in the Wenz ambient noise spectrum level diagram.

[0064] ⑦DT is the detection threshold of the sonar receiver, in dB.

[0065] Step 4: Access the grid area collection data, traverse the grid areas of each sonar detection level in turn, and calculate the intersection area of ​​each grid area and the detection range The area of ​​each grid Compare the values ​​to get the sensor coverage factor of the nth grid area at the mth sonar detection level Finally, determine whether the installed sensor coverage coefficient is zero. If it is not zero, the 8 performance indicators of sonar detection capability are calculated through the quantitative model. Otherwise, all 8 performance indicators of sonar detection capability are zero. The quantitative model process of the 8 performance indicators of sonar detection capability is as follows:

[0066] (1) Target detection probability B1

[0067] L different position points are uniformly and randomly sampled in the intersection of the grid area and the detection range, and the target detection probability value of each sampling point is obtained through the sonar direction finding accuracy calculation model. Measuring the target detection probability within the grid area by statistical averaging The specific formula is:

[0068]

[0069] Among them, the target discovery probability value of the lth position point Calculate using the following formula:

[0070]

[0071] Among them, t is the integral variable, p f is the false alarm probability, J0(·) is the first kind of zero-order Bessel function, SNR is the signal-to-noise ratio, and the sonar margin SE of the active sonar equation can be obtained by aand receiver noise.

[0072] (2) Maximum number of target discoveries B2

[0073] Through experiments, the number of detected targets is continuously increased, and the number of targets found by the sonar within the entire detection range is counted each time until the number of target discoveries no longer changes. Then the maximum number of target discoveries in the statistical results is searched as the maximum number of target discoveries in each grid area. The numerical value of the indicator.

[0074] (3) Distance measurement accuracy B3

[0075] L different position points are uniformly randomly sampled in the intersection of the grid area and the detection range, and the direction finding accuracy value Δθ of each sampling point is obtained through the sonar direction finding accuracy calculation model. (l) , measure the direction finding accuracy within the grid area by statistical averaging

[0076]

[0077] Among them, the direction finding accuracy value Δθ of the lth position point (l ) is calculated by the following formula:

[0078]

[0079] Where λ is the wavelength of the underwater acoustic signal, d is the spacing between array elements, and N is array is the number of array elements, is the position of the lth position point in the grid area.

[0080] (4) Direction finding accuracy B4

[0081] L different position points are uniformly and randomly sampled in the intersection of the grid area and the detection range, and the ranging accuracy in the grid area is measured by statistical averaging. The specific formula is as follows:

[0082]

[0083] In the formula, the ranging accuracy of the lth position point is The sonar ranging accuracy calculation model is used to calculate the solution, specifically:

[0084]

[0085] Among them, r( l ) is the distance to the lth position point in the grid area.

[0086] (5) Target track continuity B5

[0087] The measured track data in each grid area is formed according to the position of each track point, and the target continuity index value in each grid area is calculated by the following formula.

[0088]

[0089] in, is the starting time of the ith track, is the end time of the ith track, is the number of tracks in the nth grid area of ​​the mth sonar detection level, is the number of position points of the ith track, T s is the scanning period of active sonar.

[0090] (6) Target tracking delay B6

[0091] Through experiments, multiple groups of experimental targets are tracked, and the generated track point time and the real target corresponding time in the entire detection range are counted to generate multiple groups of tracking delays, and the target tracking delay in each grid area is calculated by statistical average. The specific formula is as follows:

[0092]

[0093] Among them, N t is the number of track points counted, are multiple groups of tracking delays, and q is the statistical track point number.

[0094] (7) Target recognition probability B7

[0095] Active sonar detects a target, that is, identifies the target and finally gives an identification result. Therefore, the target identification probability is calculated in the same way as the target discovery probability.

[0096] (8) Target recognition accuracy

[0097] Based on the signal-to-noise ratio (SNR) of each grid area (m,n) , the target recognition accuracy is calculated by the empirical formula, the specific formula is as follows:

[0098]

[0099] Among them, A is the database completeness weight, is the target recognition probability of the nth grid area at the mth sonar detection level, C is the increasing slope, and I is the fitting coefficient.

[0100] Step 5: Through expert scoring, the sequential relationship analysis method (G1 method) is used to calculate the weights of the performance indicators under each capability and the capability indicators under the comprehensive capability in turn, and based on the capability indicator data quantified and calculated in each grid area, the weighted sum method is used to calculate the three major capabilities and comprehensive detection capability of the sonar. By judging whether all grid areas have been traversed, it is determined whether to perform step 6. If it is determined that all grid areas have not been traversed, the operation of step 4 is repeated until the traversal is completed. The specific process of the sequential relationship analysis method (G1 method) is as follows.

[0101] (1) Sort each indicator based on expert experience and generate a sequence relationship

[0102] Invite experts to rank the evaluation indicators and generate ranking results, where x (k) is the kth indicator determined by ranking according to expert experience, w (k) is the weight of the kth index after sorting, satisfying w (1) >w (2) >…>w (K) , the subscript K is the number of indicators.

[0103] (2) Calculate the relative importance ratio r of each adjacent index after sorting (k)

[0104] Setting r (k) For x (k-1) X (k) The relative importance ratio is shown in Table 2.

[0105] Table 2, relative importance ratio r (k) The assignment table:

[0106] <![CDATA[r (k) ]]> illustrate 1 <![CDATA[Indicator x (k-1) and x (k) are equally important]]> 1.2 <![CDATA[Indicator x (k-1) Compared to x (k) Slightly more important <!-- 6 -->]]> 1.4 <![CDATA[Indicator x (k-1) is more important than x (k) significantly]]> 1.6 <![CDATA[Indicator x (k-1) Compared to x (k) Strongly important]]> 1.8 <![CDATA[Indicator x (k-1) Ratio x (k) Extremely important]]>

[0107] (3) Calculate the final indicator weight w (K)

[0108]

[0109] Among them, w (K) is the weight of the last ranked indicator.

[0110] (4) Calculate the remaining indicator weights in reverse order. The specific formula is as follows:

[0111] w (k-1) =w (k) ·r (k) ,k=K,K-1,…,3,2

[0112] Step 6: Display the evaluation values ​​of the three major capabilities and eight performance indicators in the form of radar charts, tables, etc., and use different shades of colors to represent the sonar's comprehensive detection capabilities for the grid area.

[0113] Based on the same concept as the above method, Figure 3 As shown, this embodiment also provides an active sonar grid detection capability evaluation device, including:

[0114] The regional gridding module is used to discretely divide the underwater surveillance area into several sonar detection level areas according to the water depth, and generate grid area collection data of each sonar detection level;

[0115] The detection capability indicator construction module is used to build a set of sonar detection capability indicators based on the principle of hierarchical indicator system construction in combination with the daily sonar monitoring tasks of sea surface and underwater targets;

[0116] The detection range calculation module is used to calculate the maximum detection distance by using the active sonar equation, taking into account the influencing factors of the environment, sonar, and target, and generate the detection range data in combination with the blind area range;

[0117] The grid area capability index measurement module is used to dynamically quantify the detection capability of the sonar in different grid areas by combining theory, experiment and experience;

[0118] The grid area comprehensive capability quantification module is used to configure the weights of various indicators using the order relationship analysis method, and calculate the sonar detection capability in the form of weighted sum until all grids are traversed and calculated;

[0119] The detection capability visualization module is used to display the evaluation values ​​of the sonar detection capability indicators and to differentiate the comprehensive detection capability of each grid area.

[0120] The above describes an active sonar grid detection capability evaluation method and system provided in an embodiment of the present application, as a possible product form of the above system, such as Figure 4 As shown, an embodiment of the present application further provides an electronic device, including:

[0121] At least one processor; and a communication interface that is communicatively connected to the at least one processor; the at least one processor executes instructions stored in the memory, so that the electronic device executes the method steps executed by any device in the above method embodiments through the communication interface.

[0122] Optionally, the memory is located outside the electronic device.

[0123] Optionally, the electronic device includes the memory, the memory is connected to the at least one processor, and the memory stores instructions that can be executed by the at least one processor. Figure 4 The dashed lines indicate that the memory is optional for the electronic device.

[0124] The processor and the memory may be coupled via an interface circuit or may be integrated together, which is not limited here.

[0125] The specific connection medium between the processor, memory and communication interface is not limited in the embodiments of the present application. Figure 4 The processor, memory and communication interface are connected by a bus. Figure 4 The connections between the other components are shown in bold lines, which are only for illustration and are not intended to be limiting. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 In the example, only one thick line is used to represent the bus, but it does not mean that there is only one bus or one type of bus. It should be understood that the processor mentioned in the embodiment of the present application can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented by software, the processor can be a general-purpose processor, which is implemented by reading the software code stored in the memory.

[0126] Exemplarily, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0127] It should be understood that the memory mentioned in the embodiments of 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 and 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 (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DirectRambus RAM, DR RAM).

[0128] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) can be integrated into the processor.

[0129] It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0130] As another possible product form, an embodiment of the present application also provides a computer-readable storage medium, which is used to store instructions. When the instructions are executed, the computer executes the method steps executed by any device in the above method example.

[0131] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0132] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating active sonar grid detection capability, characterized in that: The steps include: Step 1: Divide the underwater surveillance area into several sonar detection level areas according to the water depth, and generate grid area collection data of each sonar detection level; Step 2: Combined with the daily sonar monitoring tasks of sea surface and underwater targets, a set of sonar detection capability indicators is constructed according to the principle of constructing a hierarchical indicator system; Step 3: Use the active sonar equation to consider the influencing factors of the environment, sonar, and target, calculate the maximum detection distance, and generate the detection range data in combination with the blind area range; Step 4: Dynamically quantify the detection capability of sonar in different grid areas by combining theory, experiment and experience; Step 5: Use the order relationship analysis method to configure the weights of each indicator and calculate the sonar detection capability in the form of weighted sum until all grids are traversed and calculated; Step 6: Display the evaluation values ​​of the sonar detection capability indicators, and distinguish and display the comprehensive detection capability of each grid area.

2. The active sonar grid detection capability evaluation method according to claim 1 is characterized in that: In step 2, a sonar detection capability indicator set with three capabilities and eight performance indicators is constructed: The three major capabilities include target detection capability A1, positioning and tracking capability A2, and target identification capability A3; The eight performance indicators include the target detection probability B1 and the maximum number of target detections B2 under the target detection capability A1, the lateral accuracy B3, ranging accuracy B4, target track continuity B5 and target tracking delay B6 under the positioning and tracking capability A2, and the target recognition probability B7 and target recognition accuracy B8 under the target recognition capability A3.

3. The active sonar grid detection capability evaluation method according to claim 1 is characterized in that: In step 3, the maximum detection distance SE a The calculation is as follows: SE a =SL-2TL+TS-(NL-DI+DT)+AG SL is the active sonar source level, TL is the propagation loss, TS is the target strength, AG is the space-time processing gain, DI is the receiving directivity parameter, NL is the ambient noise spectrum level, and DT is the detection threshold of the sonar receiver.

4. The active sonar grid detection capability evaluation method according to claim 3 is characterized in that: The specific calculation formula of the transmission loss TL is as follows: <h2 style=";text-align:left;direction:ltr">TL=TL<h2 style=";text-align:left;direction:ltr"> 1(扩展项) <h2 style=";text-align:left;direction:ltr"> +TL<h2 style=";text-align:left;direction:ltr"> 2(吸收项) The wavefront expansion loss TL 1(扩展项) Calculated by engineering experience formula, specifically: TL 1(扩展项) =β·10lgr In the formula, r is the propagation distance, β is a constant determined by the conditions of sound wave generation; The medium sound absorption loss TL 2(吸收项) The specific calculation formula is as follows: Where α is the sound absorption coefficient of seawater, which is calculated by the empirical formula as follows: Where f is the frequency of the sound signal.

5. The active sonar grid detection capability evaluation method according to claim 4 is characterized in that: In step 4, the grid area capability index measurement module accesses the grid area set data generated by the regional gridding subdivision module, traverses the grid areas of each sonar detection level in turn, calculates the intersection area of ​​each grid area and the detection range, and compares it with the area of ​​each grid area to obtain the sensor coverage coefficient of the nth grid area of ​​the mth sonar detection level. Finally, it is determined whether the sensor coverage coefficient is zero. If its value is not zero, the performance index value of the sonar detection capability is calculated through the quantitative model. Otherwise, all the sonar detection capability performance indicators are zero.

6. The active sonar grid detection capability evaluation method according to claim 5 is characterized in that: The quantitative models are as follows: (1) Quantitative model for the discovery probability B1 of performance indicator target: L different position points are uniformly and randomly sampled in the intersection of the grid area and the detection range, and the target detection probability value of each sampling point is obtained through the sonar direction finding accuracy calculation model. Measuring the target detection probability within the grid area by statistical averaging The specific formula is: Among them, the target discovery probability value of the lth position point Calculate using the following formula: Among them, t is the integral variable, p f is the false alarm probability, J0(·) is the first kind zero-order Bessel function, SNR is the signal-to-noise ratio; (2) Quantitative model for the maximum target discovery number B2 of the performance indicator: Through experiments, the number of detected targets is continuously increased, and the number of targets discovered by the sonar within the entire detection range is counted each time until the number of target discoveries no longer changes. Then the maximum value of the number of target discoveries in the statistical results is searched as the value of the maximum target discovery number indicator in each grid area. (3) Quantitative model for the performance indicator lateral accuracy B3: L different position points are uniformly randomly sampled in the intersection of the grid area and the detection range, and the direction finding accuracy value Δθ of each sampling point is obtained through the sonar direction finding accuracy calculation model. (l ), measures the direction finding accuracy within the grid area by statistical averaging Among them, the direction finding accuracy value Δθ of the lth position point (l ) is calculated by the following formula: Where λ is the wavelength of the underwater acoustic signal, d is the spacing between array elements, and N is array is the number of array elements, is the position of the lth position point in the grid area; (4) Quantization model for the performance indicator ranging accuracy B4: L different position points are uniformly and randomly sampled in the intersection of the grid area and the detection range, and the ranging accuracy in the grid area is measured by statistical averaging. The specific formula is as follows: In the formula, the ranging accuracy of the lth position point is The sonar ranging accuracy calculation model is used to calculate the solution, specifically: Among them, r (l ) is the distance of the lth position point in the grid area, Δθ (l) is the direction finding accuracy value of the lth position point, is the position of the lth position point in the grid area; (5) Quantitative model for performance indicator target track continuity B5: The measured track data in each grid area is formed according to the position of each track point, and the target continuity index value in each grid area is calculated by the following formula: in, is the starting time of the ith track, is the end time of the ith track, is the number of tracks in the nth grid area of ​​the mth sonar detection level, is the number of position points of the ith track, T s is the scanning period of active sonar; (6) Quantitative model for performance indicator target tracking delay B6: Through experiments, multiple groups of experimental targets are tracked, and the generated track point time and the real target corresponding time in the entire detection range are counted to generate multiple groups of tracking delays, and the target tracking delay in each grid area is calculated by statistical average. The specific formula is as follows: Among them, N t is the number of track points counted, are multiple groups of tracking delays, and q is the number of the statistical track points; (7) The quantitative model of the performance indicator target recognition probability B7 is consistent with the target discovery probability B1; (8) Quantitative model for the performance indicator target recognition accuracy B8: Based on the signal-to-noise ratio (SNR) of each grid area (m,n) , the target recognition accuracy is calculated by the empirical formula, the specific formula is as follows: Among them, A is the database completeness weight, is the target recognition probability of the nth grid area at the mth sonar detection level, C is the increasing slope, and I is the fitting coefficient.

7. The active sonar grid detection capability evaluation method according to claim 1 is characterized in that: The order relationship analysis method described in step 5 includes: Sort each indicator based on expert experience to generate a sequence relationship; Calculate the relative importance ratio of each adjacent indicator after sorting; Calculate the weight of the last indicator according to the relative importance ratio; The remaining indicator weights are calculated in reverse order.

8. An active sonar grid detection capability evaluation system, comprising: The regional gridding module is used to discretely divide the underwater surveillance area into several sonar detection level areas according to the water depth, and generate grid area collection data of each sonar detection level; The detection capability indicator construction module is used to build a set of sonar detection capability indicators based on the principle of hierarchical indicator system construction in combination with the daily sonar monitoring tasks of sea surface and underwater targets; The detection range calculation module is used to calculate the maximum detection distance by using the active sonar equation, taking into account the influencing factors of the environment, sonar, and target, and generate the detection range data in combination with the blind area range; The grid area capability index measurement module is used to dynamically quantify the detection capability of the sonar in different grid areas by combining theory, experiment and experience; The grid area comprehensive capability quantification module is used to configure the weights of various indicators using the order relationship analysis method, and calculate the sonar detection capability in the form of weighted sum until all grids are traversed and calculated; The detection capability visualization module is used to display the evaluation values ​​of the sonar detection capability indicators and to differentiate the comprehensive detection capability of each grid area.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the method as described in any one of claims 1 to 7 by executing the instructions stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is implemented.

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