Passive sonar buoy array method and apparatus

Abstract

The application relates to a passive sonar buoy array method and device. The method comprises the following steps: based on sonar performance prediction, a target motion route set in a concerned sea area is constructed according to target detection requirements on the target in the concerned sea area; a target motion depth distribution is set; a sonar buoy array laying area and laying depth on a possible navigation path are constructed; then, the sonar buoy array laying area is divided into grids, and performance data of a sonar buoy laid at each grid point in the sonar buoy array laying area are calculated; a target detection efficiency model of the sonar buoy array is constructed; constraint parameters of the sonar buoy array in the concerned sea area are obtained; optimal sonar buoy array laying parameters in the sonar buoy array laying area under the constraint parameters are calculated through a numerical optimization algorithm; and finally, the sonar buoy array is laid in the sonar buoy array laying area according to the sonar buoy array laying parameters. The sonar buoy array efficiency is improved.

Description

Passive sonar buoy deployment method and device Technical Field

[0001] This invention belongs to the field of sonar detection array technology, and relates to a method and device for deploying passive sonar buoys. Background Technology

[0002] Sonar buoy arrays are a sensor arrangement method used in sonar systems to detect and locate underwater targets. The purpose of sonar buoy arrays is to improve the detection performance and target localization accuracy of the sonar system through reasonable sensor placement. Currently, traditional sonar buoy array optimization methods mainly include: Sonar buoy density optimization: Using mathematical modeling and optimization algorithms, the optimal sonar buoy density and position are determined to maximize coverage of the monitoring area and improve target detection and localization performance. This method can be optimized based on target distribution, sensor characteristics, and performance indicators. Sensor layout optimization: By optimizing the relative positions and orientations of sensors, overlapping areas and blind spots are minimized, improving the coverage and detection efficiency of the sonar system. This can be achieved using optimization algorithms such as genetic algorithms and particle swarm optimization. Adaptive arraying: Utilizing mutual communication and cooperation between sensors, adaptive sonar buoy arrays are achieved. Sensors can adjust their positions and parameters by exchanging information in real time to adapt to dynamic changes in targets and environmental conditions. This method can improve the system's flexibility and adaptive performance. Multi-sensor fusion: Combining multiple sonar buoys with other sensors (such as underwater cameras, magnetometers, etc.), sensor fusion algorithms are used to improve target detection and localization accuracy. Multi-sensor fusion can be achieved through techniques such as Kalman filtering and particle filtering. However, traditional sonar buoy array optimization methods still suffer from low array efficiency. Summary of the Invention

[0003] To address the problems existing in the above-mentioned traditional methods, this invention proposes a passive sonar buoy deployment method and a passive sonar buoy deployment device, which can significantly improve the effectiveness of sonar buoy deployment.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0005] On the one hand, a method for deploying passive sonar buoys is provided, including:

[0006] Construct a set of possible navigation routes for the target in the sea area of ​​interest; the set of navigation routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest.

[0007] Construct the motion depth distribution of the target in the sea area of ​​interest; the motion depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest.

[0008] Based on the set of motion routes and the distribution of motion depth, the deployment area and deployment depth of sonar buoy arrays are constructed on possible navigation paths;

[0009] The deployment area of ​​the sonar buoy array is divided into grids, and the sonar performance prediction algorithm is called to calculate the performance data of the sonar buoys deployed at each grid point in the deployment area.

[0010] Based on the set of motion paths and the performance data of sonar buoys, a model of the detection effectiveness of the sonar buoy array against targets is constructed.

[0011] Obtain the constraint parameters of the sonar buoy array in the sea area of ​​interest, calculate the detection effectiveness model through numerical optimization algorithm, and obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters.

[0012] Sonar buoys are deployed within the sonar buoy array deployment area according to the sonar buoy array deployment parameters.

[0013] On the other hand, a passive sonar buoy deployment device is also provided, comprising:

[0014] The route construction module is used to construct a set of movement routes of the target in the sea area of ​​interest; the set of movement routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest.

[0015] The motion building module is used to construct the motion depth distribution of a target in the sea area of ​​interest; the motion depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest;

[0016] The region construction module is used to construct the deployment area and deployment depth of sonar buoy arrays on possible navigation paths based on the set of motion routes and the distribution of motion depth.

[0017] The performance calculation module is used to divide the sonar buoy array deployment area into grids and call the sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the sonar buoy array deployment area.

[0018] The model building module is used to construct a model of the sonar buoy array's target detection effectiveness based on the set of motion paths and the performance data of sonar buoys;

[0019] The optimization calculation module is used to obtain the constraint parameters of the sonar buoy array in the sea area of ​​interest, and calculate the detection effectiveness model through numerical optimization algorithm to obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters.

[0020] The buoy deployment module is used to deploy sonar buoys within the sonar buoy array deployment area according to the sonar buoy array deployment parameters.

[0021] One of the above technical solutions has the following advantages and beneficial effects:

[0022] The aforementioned passive sonar buoy deployment method and apparatus, based on sonar performance prediction and targeting the target detection needs in the sea area of ​​interest, constructs a set of target movement paths within the sea area of ​​interest, sets the target's movement depth distribution, and establishes sonar buoy array deployment areas and depths along possible navigation paths. Then, the sonar buoy array deployment area is gridded, and the performance data of the sonar buoys deployed at each grid point are calculated. This allows for the construction of a sonar buoy array target detection effectiveness model to evaluate the target movement detection effectiveness. Furthermore, the constraint parameters of the sonar buoy array in the sea area of ​​interest are obtained, and the optimal sonar buoy array deployment parameters within the deployment area under these constraints are calculated using a numerical optimization algorithm. Finally, the sonar buoy array is deployed within the deployment area based on these deployment parameters.

[0023] Compared to traditional methods, a novel deployment scheme was designed. This scheme evaluates the detection effectiveness of sonar buoys by constructing a target motion path set, setting the target motion depth distribution, and configuring corresponding sonar buoy deployment array parameters. To obtain a relatively optimal sonar buoy deployment position, the array's target detection effectiveness is used as a fitness function, and a numerical optimization algorithm is employed to obtain the optimal sonar buoy deployment parameters under constraints, significantly improving the sonar buoy deployment effectiveness. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 is a flowchart illustrating a passive sonar buoy array deployment method in one embodiment;

[0026] Figure 2 is a schematic diagram of the construction process of the target motion depth distribution in one embodiment;

[0027] Figure 3 is a schematic diagram of the theoretical detection probability calculation process of the target array in one embodiment;

[0028] Figure 4 is a schematic diagram of the detection probability calculation process of the sonar buoy array for the way set in one embodiment;

[0029] Figure 5 is a schematic diagram of the detection probability optimization calculation process in one embodiment;

[0030] Figure 6 is a block diagram of the module structure of a passive sonar buoy array device in one embodiment. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0033] It should be noted that, in this document, the reference to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The presentation of this phrase in various locations throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments.

[0034] Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments. The term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items, and all possible combinations thereof.

[0035] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] Referring to Figure 1, in one embodiment, a passive sonar buoy deployment method is provided, including the following processing steps S1 to S7:

[0037] S1, Construct a set of motion routes for the target in the sea area of ​​interest; the set of motion routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest. The target can be any surface / underwater vehicle that needs to be detected, tracked or monitored in the sea area of ​​interest (the sea area that needs to be monitored at present). All possible navigation paths of the target in the sea area of ​​interest constitute the set of motion routes.

[0038] S2, Construct the motion depth distribution of the target in the sea area of ​​interest; the motion depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest, and all possible motion depths of the target in the sea area of ​​interest constitute the motion depth distribution (data).

[0039] S3, based on the set of motion routes and the distribution of motion depth, constructs the deployment area and deployment depth of sonar buoy arrays on possible navigation paths.

[0040] S4: Divide the sonar buoy array deployment area into grids and call the sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the sonar buoy array deployment area.

[0041] S5. Based on the set of motion paths and the performance data of sonar buoys, a model of the sonar buoy array's target detection effectiveness is constructed.

[0042] S6. Obtain the constraint parameters of the sonar buoy array in the sea area of ​​interest, calculate the detection effectiveness model through numerical optimization algorithm, and obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters.

[0043] S7, deploy sonar buoys within the sonar buoy array deployment area according to the sonar buoy array deployment parameters.

[0044] The aforementioned passive sonar buoy deployment method, based on sonar performance prediction and targeting the target detection needs in the sea area of ​​interest, constructs a set of target movement paths within the sea area of ​​interest, sets the target's movement depth distribution, and establishes sonar buoy array deployment areas and depths along possible navigation paths. Then, the sonar buoy array deployment area is gridded, and the performance data of the sonar buoys deployed at each grid point are calculated. This allows for the construction of a sonar buoy array target detection effectiveness model to evaluate the target movement detection efficiency. Next, the constraint parameters of the sonar buoy array in the sea area of ​​interest are obtained, and the optimal sonar buoy array deployment parameters within the deployment area under these constraints are calculated using a numerical optimization algorithm. Finally, the sonar buoy array is deployed within the deployment area based on these deployment parameters.

[0045] Compared to traditional methods, a novel deployment scheme was designed. This scheme evaluates the detection effectiveness of sonar buoys by constructing a target motion path set, setting the target motion depth distribution, and configuring corresponding sonar buoy deployment array parameters. To obtain a relatively optimal sonar buoy deployment position, the array's target detection effectiveness is used as a fitness function, and a numerical optimization algorithm is employed to obtain the optimal sonar buoy deployment parameters under constraints, significantly improving the sonar buoy deployment effectiveness.

[0046] In one embodiment, further, step S1 described above may specifically include the following processing:

[0047] S11: Obtain the possible locations of the target within the sea area of ​​interest, and construct the target's initial movement area in the corresponding electronic map of the sea area of ​​interest. This can be understood as constructing the target's initial movement area in the electronic map based on the possible locations of the target. The method for constructing the target's initial movement area can include, for example, a grid-based area selection method or a custom area selection method, to achieve efficient and flexible area construction processing.

[0048] The grid-based region selection method can be as follows:

[0049] A grid area is constructed by setting the latitude and longitude of the center point of the motion area, the length and width of the area, and the rotation angle. Then, relevant area attributes are calculated according to the actual application scenario and the corresponding detection task area, such as the latitude and longitude of the area vertex, the bounding rectangle of the area, the centroid of the area, and the area of ​​the area. This allows us to determine the starting position, size, and shape of the target's motion area.

[0050] The custom region selection method can be shown as follows:

[0051] A custom target starting motion region is constructed by setting the latitude and longitude of polygon vertices on an electronic map. Then, based on the actual application scenario and the corresponding detection task area, relevant region attributes are calculated, including the latitude and longitude of the region vertices, the region's bounding rectangle, the region's centroid, and the region's area. This determines the target's starting motion region location, size, and shape. After constructing the target's starting motion region, the target motion type is set according to the tasks the target may perform.

[0052] S121, if the target's task area is determined based on the possible tasks the target may perform, then the target's termination movement area is constructed in the electronic map based on the target's task area.

[0053] S131, Based on the starting and ending motion regions, the Monte Carlo random method is used to generate a set of motion paths.

[0054] It is understandable that if the task area corresponding to the possible tasks that the target can perform is determined, then the target's termination movement area can be constructed in the electronic map in the same way as the aforementioned step S11 based on the task area. Then, based on the set target movement area, such as the aforementioned starting movement area and termination movement area, the Monte Carlo random method is used to generate a set of routes.

[0055] Furthermore, regarding step S131 above, the process of generating the motion path set using the Monte Carlo random method can specifically include the following processing:

[0056] 1311, Randomly generate a coordinate point of the target within the initial motion area as the starting waypoint;

[0057] 1312. Randomly generate a coordinate point within the target's mission area as the termination waypoint and calculate the target's heading.

[0058] Return to step 1311 and repeat steps 1311 to 1312 until a set number of routes are obtained, generating a set of motion routes.

[0059] It can be understood that a coordinate point is randomly generated within the initial movement area as the starting waypoint. If the target mission area is determined, a coordinate point is randomly generated within the target's possible mission area as the ending waypoint. The target's heading is then calculated. Based on the target's heading and the speed and time corresponding to the target's possible missions, the target's actual path is calculated. Steps 1311 and 1312 are repeated until a set number of paths are obtained, generating a set of paths. The set number can be adjusted according to the actual detection needs of the application sea area, as long as it meets the required detection accuracy and time requirements.

[0060] In one embodiment, step S1 described above may further include the following processing:

[0061] S122, if the target's mission area is uncertain based on the possible missions the target may perform, then set the target's motion parameters; the target motion parameters include motion speed distribution, motion heading distribution, motion time, and number of targets;

[0062] 132. Based on the initial motion area and target motion parameters, a set of motion paths is generated using the Monte Carlo random method.

[0063] It is understandable that if the mission area corresponding to the possible mission of the target is uncertain, then the target motion parameters, such as motion speed distribution, motion heading distribution, motion time and quantity, can be set directly. Then, based on the set target motion parameters, a set of routes can be generated using the Monte Carlo random method based on the above-mentioned initial motion area, thereby realizing route generation when the mission area is uncertain.

[0064] In one embodiment, the process of generating the motion path set using the Monte Carlo random method in step S132 above may specifically include the following processing:

[0065] S1321, Randomly generate a coordinate point of the target within the initial motion area as the starting waypoint;

[0066] S1322, Calculate the target's actual flight path based on the velocity distribution, heading distribution, and time of motion;

[0067] Return to step S1321 and repeat steps 1321 to 1322 until a set number of routes are obtained, generating a set of motion routes.

[0068] It can be understood that a coordinate point of the target is randomly generated within the initial motion area as the starting waypoint. Then, based on the target's motion heading distribution, motion speed distribution, and motion time, the actual motion path of the target is calculated. Steps 1321 to 1322 are repeated until a set number of routes are obtained, and a set of motion paths is generated.

[0069] In one embodiment, as shown in FIG2, step S2 described above may specifically include the following processing:

[0070] S21, based on the target's possible navigation depth, set multiple navigation depths of the target and corresponding percentages;

[0071] S22, based on multiple navigation depths and their corresponding percentages, normalize the multiple navigation depths by percentage;

[0072] S23, calculate the expected value of the normalized multivariate navigation depth distribution and use it as the target navigation depth to obtain the constructed motion depth distribution.

[0073] It is understandable that the navigation depth can be estimated based on historical navigation data of similar targets or on-site detection. This allows setting multiple navigation depths and their percentages for the target, then normalizing the depth distribution by percentage, and finally calculating the expected value of the depth distribution as the target navigation depth. Similarly, the target's motion depth distribution can be constructed, which is simple and efficient.

[0074] In one embodiment, as shown in FIG3, step S3 described above may specifically include the following processing:

[0075] S31, Based on the set of motion routes and the possible missions of the target, construct the sonar buoy array deployment area on the possible navigation paths in the electronic map;

[0076] S32, based on the possible tasks the target may perform, set the array deployment type of the target to target array, and set the array parameters and deployment depth of the target array; the target array can be a coverage array, an interception array, or an encirclement array.

[0077] S33, Calculate the deployment coordinates of each sonar buoy in the target array based on the array parameters and the deployment area of ​​the sonar buoy array;

[0078] S34, calculate the theoretical detection probability of the target array against the target.

[0079] It is understandable that, based on the set of possible flight paths of the target and the possible missions the target may perform, the deployment area of ​​the sonar buoy array is constructed on the electronic map using the above step S11. According to the possible missions of the target, the deployment type of the sonar buoy array is set as a coverage array, an interception array, or an encirclement array, and the array parameters and deployment depth of the selected array are set. Array parameters may include, for example, the latitude and longitude of the deployment center, the deployment direction, the theoretical detection range of the buoys, and the deployment depth. For a coverage array, array parameters may also include the array spacing and the number of array rows and columns; for an interception array, array parameters may include the array pattern (M / N), the array spacing, and the number of M / N arrays, where M and N represent dense and sparse arrays, respectively; for an encirclement array, array parameters may include the number of buoy segments, deployment delay time, the root mean square error of the last contact position, the root mean square error of the deployment position, and the aircraft deployment speed. Based on the set array parameters and deployment area, the deployment coordinates of each sonar buoy in the selected array are calculated, and finally, the theoretical detection probability of the array against the target is calculated, completing this part of the deployment area and depth construction.

[0080] Furthermore, if the target array is a covering array, then step S34 above can specifically include the following processing:

[0081] S341, calculate the effective search area of ​​the sonar buoys in the coverage array based on the target's speed and the sonar parameters of the sonar buoys; the sonar parameters include the effective detection range, the watch time, and the remaining survival time.

[0082] S342, based on the effective search area, the contact probability of the sonar buoy, the probability of the sonar buoy working reliably after entering the water, and the listening time of the patrol aircraft to the coverage array, the theoretical detection probability of the coverage array on the target is calculated.

[0083] Specifically, when the selected array is a coverage array, the calculation of its theoretical detection probability of the target can be implemented as follows: the effective detection range of the sonar buoy is d. c The sonar buoy's shift work time is t c The remaining survival time of the sonar buoy FZ(k,i) in duty status is t. i (0≤t i ≤t c ), target speed v q According to the principle of relative motion, the effective search area S of the i-th buoy in the buoy array is... i for:

[0084] S i =πd c 2 +2d c v q t i (1)

[0085] The probability of contact with any buoy is the same, p. jc The probability that the buoy will work reliably after entering the water is p. k The patrol vehicle's listening time for the buoy array FZ(n) is t. jt (t jt ≤t i If the probability of finding the target within a search area of ​​area S is:

[0086]

[0087] In this way, the theoretical detection probability of the coverage array against the target can be calculated efficiently and accurately.

[0088] Furthermore, if the target array is an interception array, then step S34 above can specifically include the following processing:

[0089] S343, based on the effective detection range and contact probability of the sonar buoy, and the probability of the sonar buoy working reliably after entering the water, calculate the probability of a single array of the interception array detecting the target.

[0090] S344: Based on the probability of a single-column array detecting a target, the theoretical detection probability of the interception array composed of each single-column array against the target is calculated.

[0091] Specifically, when the selected array is an interception array, the theoretical detection probability of the target can be calculated as follows: For a single-column interception array FZ(L1), the probability of detecting the target P is... L for:

[0092]

[0093] In equation (3), k∈[1,2],ΔD x p represents the spacing between single-column arrays. jc Let p be the probability of buoy contact. k This represents the probability of a buoy working reliably after entering the water. For the complex array FZ(L) n The probability P(L) of a complex array (interception array) composed of n single-column arrays detecting a target is... n )for:

[0094]

[0095] Furthermore, if the target array is an encirclement array, then step S34 above can specifically include the following processing:

[0096] S345, based on the effective detection range and contact probability of the sonar buoy, and the probability of the sonar buoy working reliably after entering the water, calculate the probability of a single-layer encirclement array of the encirclement array detecting the target.

[0097] S346. Based on the probability of a single-layer encirclement array detecting a target, the theoretical detection probability of the encirclement array composed of each single-layer encirclement array against the target is calculated.

[0098] Specifically, when the selected array is an encirclement array, the theoretical detection probability of the target can be calculated as follows: For a single-layer encirclement array, the probability P of detecting the target is... L for:

[0099]

[0100] In equation (5), k∈[1,2], each layer in the multi-layer encirclement array has the same structure (the buoy spacing is kd). c If n b Target detection probability of layered encirclement array for:

[0101]

[0102] In one embodiment, step S4 described above may specifically include the following processing:

[0103] S41, based on the sonar buoy array deployment area, obtain the minimum bounding rectangle of the area;

[0104] S42, based on the set grid spacing, obtain the coordinates of each grid point of the minimum bounding rectangle of the region;

[0105] S43, based on sonar parameters, environmental parameters, calculation parameters, deployment depth and navigation depth, calls the sonar performance prediction algorithm to calculate the detection effectiveness of each grid point on the current motion path set and saves the detection probability data.

[0106] Specifically, based on the sonar buoy array deployment area, the minimum bounding rectangle of that area can be directly obtained from the electronic map. Then, according to the grid spacing, the coordinates of each grid point within the minimum bounding rectangle are read or automatically identified. Finally, based on the existing sonar parameters, environmental parameters, computational parameters, array deployment depth, and target navigation depth of the sonar buoy, the existing sonar performance prediction algorithm model is invoked to calculate and output the detection effectiveness of each grid point for the current target's motion path set, and the detection probability data is saved for later use. Sonar parameters include signal detection method, center frequency, integration time, average number of times, detection threshold, and receiver directivity, etc. Environmental parameters include sound speed, terrain, seabed, wind speed, and shipping density, etc. Computational parameters include propagation model, noise model, ground acoustic model, and spatiotemporal frequency, etc.

[0107] In one embodiment, as shown in FIG4, step S5 described above may specifically include the following processing:

[0108] S51, from the set of motion routes, obtain a route data and divide the route into multiple sub-route nodes at a set time interval; that is, obtain a route data L from the target's set of motion routes. n The route L is subjected to time intervals Δt. n Divide the route into N sub-route nodes.

[0109] S52, obtain the coordinate position of a sonar buoy from the sonar buoy array, and calculate the detection efficiency of the sonar buoy's coordinate position using bilinear interpolation based on the detection probability data; that is, obtain the coordinate position of a sonar buoy S from the sonar buoy array. n Based on the gridded detection probability data from step S43 above, the detection efficiency of the current coordinate P is calculated using bilinear interpolation.

[0110] S53, based on the detection effectiveness of the sonar buoy's coordinates, construct a detection probability table for each sub-route node; that is, based on the current sonar buoy S... n To assess the detection effectiveness, a detection probability table for each sub-path node in step S51 is constructed, as shown in Table 1 below:

[0111] Table 1

[0112]

[0113]

[0114] Among them, H qk =min{1-p s ,s∈[q,k]} represents the current sonar buoy S nThe maximum detection probability at the waypoint node [q,k]; P s The detection probability for each route node.

[0115] S54, statistically analyze all sub-route nodes along the current route and calculate the probability of sonar buoy detection of targets along the route; specifically, the following formula can be used to statistically analyze all sub-route nodes of the current route and calculate the probability of sonar buoy S. n For the current route L n Detection probability Fd k :

[0116]

[0117] In equation (7): k∈[0,N], α=1-e -λΔt λ is a constant of 0.5, and Δt is the time interval.

[0118] S55, return to step S52, repeat steps S52 to S54, and iteratively calculate the route L corresponding to the route data of all sonar buoys in the sonar buoy array. n Detection probability Fd of the target k .

[0119] S56, based on the detection probabilities of each sonar buoy in the sonar buoy array for targets on the route corresponding to the route data, calculate the total detection probability of the sonar buoy array for targets on the route corresponding to the route data; specifically, calculate the detection probability Fd of all sonar buoys according to step S55. k The following formula is used to calculate the sonar buoy array's position on the current flight path L. n Detection probability:

[0120] Fd=KFd max +(1-K)Fd ind (8)

[0121] In equation (8): Fd max =max{Fd k} for the sonar buoy array on the current route L n The maximum detection probability, Where n is the number of buoys in the sonar buoy array, and K is a constant of 0.55.

[0122] S57, return to step S51, repeat steps S51 to S56, and cyclically calculate the detection probability Fd of the sonar buoy array for all routes.

[0123] S58, based on the detection probabilities of the sonar buoy array for all routes, calculate the detection probability of the sonar buoy array for the set of moving routes. Specifically, based on the detection probabilities Fd for all routes calculated in step S57, the detection probability of the sonar buoy array for the set of moving routes of the target is calculated using the following formula:

[0124]

[0125] In equation (9): N is the number of routes.

[0126] Through the above processing steps, the detection probability of the target's motion path set can be automatically calculated and output.

[0127] In one embodiment, the process of obtaining the constraint parameters of the sonar buoy array in the sea area of ​​interest in step S6 above may specifically include the following processing:

[0128] S61, Set the constraint parameters of the sonar buoy array according to the array deployment type;

[0129] S62, adds the available depth for sonar buoy array deployment.

[0130] It is understood that the array deployment type can be a coverage array, an interception array, or an encirclement array. Therefore, after selecting the array deployment type, the corresponding constraint parameters of the sonar buoy array can be set. For example, the constraint parameters of a coverage array include the array type, maximum deployment direction, maximum deployment spacing, and available deployment depth. The constraint parameters of an interception array include the array type, maximum number of deployments, and maximum deployment spacing. The constraint parameters of an encirclement array include the maximum number of array layers, maximum number of deployments, and deployment radius range. Then, the corresponding available array deployment depth can be set and added. Specifically, it can be set according to the available depth of the sonar and the detection needs.

[0131] In one embodiment, as shown in Figure 5, the process of calculating the detection performance model through a numerical optimization algorithm to obtain the optimal sonar buoy array deployment parameters within the sonar buoy array deployment area under constrained parameters in step S6 above may specifically include the following processing:

[0132] S63, under the constraint parameters of the sonar buoy array, randomly generate multiple sets of array parameters and corresponding iteration speeds, and calculate the deployment positions of each sonar buoy in the sonar buoy array based on the array parameters; specifically, under the constraint parameters of the sonar buoy array, randomly generate N sets of array parameters and their corresponding iteration speeds v, and calculate the deployment positions of each sonar buoy in the array based on the array parameters.

[0133] S64, the detection probability corresponding to each group of sonar buoy arrays is taken as the historical best detection probability for each group of arrays, and the maximum detection probability is obtained from the historical best detection probabilities as the global best detection probability; specifically, the detection probability CDP corresponding to N groups of arrays is calculated based on the above step S5. N The current detection probability is used as the historical best CDP for each array group. PBest , from CDP PBest The maximum detection probability is obtained as the globally optimal CDP. GBest Its globally optimal corresponding array parameter is gbest.

[0134] S65, update the array parameters according to the set update method based on the array parameters corresponding to the globally optimal detection probability; specifically, the set update method is as follows:

[0135] The iteration rate of the array parameters is updated according to the following formula:

[0136]

[0137] In formula (10): and These represent the values ​​of the array parameters of the i-th group and the iteration speed, respectively, during the d-th iteration. Let be the array parameters corresponding to the i-th array group with the maximum detection probability at the d-th iteration position; w is the velocity weight, decreasing from 0.9 to 0.4; c1 and c2 are the individual learning factor and the social learning factor, respectively, both constants; r1 and r2 are random numbers between 0 and 1. The array parameters for each group are updated as follows:

[0138]

[0139] S66, based on the detection probabilities corresponding to each group of arrays in the sonar buoy array, update the array parameters corresponding to the iterative position and the global optimal detection probability; specifically, calculate the detection probability CDP corresponding to N groups of arrays based on step S5. N And update pbest and gbest.

[0140] S67. If the maximum number of iterations has not been reached, return to step S65; otherwise, proceed to step S68. It can be understood that the maximum number of iterations can be set according to the actual iterative update needs.

[0141] S68. If the maximum number of iterations is reached, the last updated global optimal detection probability is used as the output optimal detection probability; the array parameters corresponding to the optimal detection probability are the optimal sonar buoy array deployment parameters.

[0142] Through the above processing steps, the optimized output of sonar buoy array deployment parameters was efficiently achieved.

[0143] It should be understood that although the steps in the flowcharts of Figures 1 to 5 are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in Figures 1 to 5 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0144] Referring to Figure 6, in one embodiment, a passive sonar buoy deployment device 100 is provided, including a route construction module 11, a motion construction module 12, a region construction module 13, a performance calculation module 14, a model construction module 15, an optimization calculation module 16, and a buoy deployment module 17. The route construction module 11 is used to construct a set of motion routes of the target in the sea area of ​​interest; the set of motion routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest. The motion construction module 12 is used to construct the motion depth distribution of the target in the sea area of ​​interest; the motion depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest. The region construction module 13 is used to construct the sonar buoy array deployment area and deployment depth on the possible navigation paths based on the set of motion routes and the motion depth distribution. The performance calculation module 14 is used to divide the sonar buoy array deployment area into grids and call a sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the sonar buoy array deployment area. The model building module 15 is used to construct a target detection effectiveness model for the sonar buoy array based on the set of motion paths and the performance data of the sonar buoys. The optimization calculation module 16 is used to obtain the constraint parameters of the sonar buoy array in the sea area of ​​interest, calculate the detection effectiveness model through numerical optimization algorithms, and obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters. The buoy deployment module 17 is used to deploy sonar buoys within the deployment area of ​​the sonar buoy array according to the sonar buoy array deployment parameters.

[0145] The aforementioned passive sonar buoy deployment device 100, based on sonar performance prediction and the target detection needs in the sea area of ​​interest, constructs a set of target movement paths within the sea area of ​​interest, sets the target movement depth distribution, and establishes sonar buoy array deployment areas and depths along possible navigation paths. Then, it divides the sonar buoy array deployment area into a grid and calculates the performance data of the sonar buoys deployed at each grid point, thereby constructing a sonar buoy array target detection effectiveness model to evaluate the target movement detection effectiveness. Next, it obtains the constraint parameters of the sonar buoy array in the sea area of ​​interest, and uses a numerical optimization algorithm to calculate the optimal sonar buoy array deployment parameters within the deployment area under these constraints. Finally, it completes the sonar buoy array deployment within the deployment area based on the sonar buoy array deployment parameters.

[0146] Compared to traditional methods, a novel deployment scheme was designed. This scheme evaluates the detection effectiveness of sonar buoys by constructing a target motion path set, setting the target motion depth distribution, and configuring corresponding sonar buoy deployment array parameters. To obtain a relatively optimal sonar buoy deployment position, the array's target detection effectiveness is used as a fitness function, and a numerical optimization algorithm is employed to obtain the optimal sonar buoy deployment parameters under constraints, significantly improving the sonar buoy deployment effectiveness.

[0147] In one embodiment, each module of the passive sonar buoy array device 100 can also be used to implement the functions of corresponding steps in other embodiments of the passive sonar buoy array method.

[0148] For specific limitations regarding the passive sonar buoy array device 100 in this embodiment, please refer to the corresponding limitations in the various embodiments of the passive sonar buoy array method above, which will not be repeated here. Each module in the passive sonar buoy array device 100 can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in hardware or independently of a device with data processing capabilities, or stored in software in the memory of the aforementioned device, so that the processor can call and execute the operations corresponding to each module. The aforementioned device can be, but is not limited to, various types of data computing and processing devices already existing in the art.

[0149] In one embodiment, a sonar buoy deployment device is also provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following processing steps: constructing a set of motion paths of the target in a sea area of ​​interest; using the set of motion paths to simulate the possible navigation paths of the target in the sea area of ​​interest; constructing a motion depth distribution of the target in the sea area of ​​interest; using the motion depth distribution to simulate the possible navigation depths of the target in the sea area of ​​interest; constructing a sonar buoy array deployment area and deployment depth on the possible navigation paths based on the set of motion paths and the motion depth distribution; and performing sonar buoy deployment on the sonar buoy array. The deployment area of ​​the sonar buoy array is divided into grids, and a sonar performance prediction algorithm is invoked to calculate the performance data of the sonar buoys deployed at each grid point in the deployment area. Based on the set of motion paths and the performance data of the sonar buoys, a detection effectiveness model of the sonar buoy array against the target is constructed. The constraint parameters of the sonar buoy array in the sea area of ​​interest are obtained, and the detection effectiveness model is calculated through a numerical optimization algorithm to obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters. Sonar buoys are deployed within the deployment area of ​​the sonar buoy array according to the sonar buoy array deployment parameters.

[0150] It is understood that, in addition to the memory and processor mentioned above, the sonar buoy deployment equipment also includes other hardware and software components not listed in this specification. The specific components can be determined according to the model of the sonar buoy deployment equipment in different application scenarios, and will not be listed and described in detail in this specification.

[0151] In one embodiment, when the processor executes the computer program, it can also implement the steps or sub-steps added in the various embodiments of the passive sonar buoy array method described above.

[0152] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When executed by a processor, the computer program performs the following processing steps: constructing a set of motion paths of a target in a sea area of ​​interest; the set of motion paths is used to simulate possible navigation paths of the target in the sea area of ​​interest; constructing a motion depth distribution of the target in the sea area of ​​interest; the motion depth distribution is used to simulate possible navigation depths of the target in the sea area of ​​interest; based on the set of motion paths and the motion depth distribution, constructing a sonar buoy array deployment area and deployment depth on the possible navigation paths; and deploying the sonar buoy array... The deployment area is divided into grids, and a sonar performance prediction algorithm is invoked to calculate the performance data of sonar buoys deployed at each grid point in the sonar buoy array deployment area. Based on the set of motion paths and the performance data of the sonar buoys, a detection effectiveness model of the sonar buoy array against targets is constructed. The constraint parameters of the sonar buoy array in the sea area of ​​interest are obtained, and the detection effectiveness model is calculated through a numerical optimization algorithm to obtain the optimal sonar buoy array deployment parameters within the deployment area under the constraint parameters. Sonar buoys are deployed within the deployment area of ​​the sonar buoy array according to the sonar buoy array deployment parameters.

[0153] In one embodiment, when the computer program is executed by the processor, it can also implement the steps or sub-steps added in the various embodiments of the passive sonar buoy array method described above.

[0154] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus DRAM (RDRAM), and interface DRAM (DRDRAM), etc.

[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, all of which fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for deploying passive sonar buoys, characterized in that, This includes constructing a set of target movement routes in a sea area of ​​interest; the set of movement routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest; constructing a depth distribution of the target movement in the sea area of ​​interest; the depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest; based on the set of movement routes and the depth distribution, constructing a sonar buoy array deployment area and deployment depth along the possible navigation paths; dividing the sonar buoy array deployment area into grids and calling a sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the deployment area; based on the set of movement routes and the performance data of the sonar buoys, constructing a detection effectiveness model of the sonar buoy array against the target; obtaining the constraint parameters of the sonar buoy array in the sea area of ​​interest, and performing numerical optimization... The algorithm calculates the detection performance model to obtain the optimal sonar buoy array deployment parameters within the sonar buoy array deployment area under the constrained parameters; sonar buoys are deployed within the sonar buoy array deployment area according to the sonar buoy array deployment parameters; the steps of dividing the sonar buoy array deployment area into grids and calling the sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the sonar buoy array deployment area include: obtaining the minimum bounding rectangle of the sonar buoy array deployment area; obtaining the coordinates of each grid point of the minimum bounding rectangle of the area according to the set grid spacing; and calling the sonar performance prediction algorithm to calculate the detection performance of each grid point for the current set of moving routes and saving the detection probability data according to the sonar parameters, environmental parameters, calculation parameters, deployment depth and navigation depth.

2. The passive sonar buoy deployment method according to claim 1, characterized in that, The steps of constructing a set of movement routes for a target in a sea area of ​​interest include: obtaining the possible locations of the target in the sea area of ​​interest; constructing the target's initial movement area in an electronic map corresponding to the sea area of ​​interest; constructing the target's initial movement area by means of a grid-based area selection method or a custom area selection method; if the target's task area is determined based on the target's possible tasks, then constructing the target's final movement area in the electronic map based on the target's task area; and generating the set of movement routes using a Monte Carlo random method based on the initial movement area and the final movement area.

3. The passive sonar buoy deployment method according to claim 2, characterized in that, The step of constructing a set of movement routes of a target in a sea area of ​​interest further includes: if it is determined that the task area of ​​the target is uncertain based on the possible tasks the target may perform, then setting target movement parameters of the target; the target movement parameters include movement speed distribution, movement heading distribution, movement time, and number of targets; and generating the set of movement routes using the Monte Carlo random method based on the initial movement area and the target movement parameters.

4. The passive sonar buoy deployment method according to claim 2, characterized in that, The process of generating the motion path set using the Monte Carlo random method includes: randomly generating a coordinate point of the target within the initial motion area as the starting waypoint; randomly generating a coordinate point within the target's mission area as the ending waypoint and calculating the target's motion heading; returning to the step of randomly generating a coordinate point of the target within the initial motion area as the starting waypoint, until a set number of routes are obtained, thereby generating the motion path set.

5. The passive sonar buoy deployment method according to claim 3, characterized in that, The process of generating the set of motion routes using the Monte Carlo random method includes: randomly generating a coordinate point of the target within the initial motion area as the starting waypoint; calculating the actual motion route of the target based on the motion speed distribution, the motion heading distribution, and the motion time; returning to the step of randomly generating a coordinate point of the target within the initial motion area as the starting waypoint, until a set number of routes are obtained, thereby generating the set of motion routes.

6. The passive sonar buoy deployment method according to any one of claims 1 to 5, characterized in that, The step of constructing the motion depth distribution of the target in the sea area of ​​interest includes: setting multiple navigation depths of the target and corresponding percentages based on the possible navigation depths of the target; normalizing the multiple navigation depths by percentage based on the multiple navigation depths and corresponding percentages; calculating the expected value of the depth distribution of the normalized multiple navigation depths and using it as the target navigation depth of the target to obtain the constructed motion depth distribution.

7. The passive sonar buoy deployment method according to claim 6, characterized in that, The steps of constructing a sonar buoy array deployment area and deployment depth on the possible navigation paths based on the set of motion routes and the motion depth distribution include: constructing the sonar buoy array deployment area on the possible navigation paths in an electronic map based on the set of motion routes and the possible missions of the target; setting the array deployment type of the target as a target array based on the possible missions of the target, and setting the array parameters and deployment depth of the target array; the target array is a coverage array, an interception array, or an encirclement array; calculating the deployment coordinates of each sonar buoy in the target array based on the array parameters and the sonar buoy array deployment area; and calculating the theoretical detection probability of the target array against the target.

8. The passive sonar buoy deployment method according to claim 7, characterized in that, The target array is a coverage array. The step of calculating the theoretical detection probability of the target array against the target includes: calculating the effective search area of ​​the sonar buoys in the coverage array based on the target's sailing speed and the sonar parameters of the sonar buoys; the sonar parameters include effective detection range, watch time, and remaining survival time; and calculating the theoretical detection probability of the coverage array against the target based on the effective search area, the contact probability of the sonar buoys, the probability of the sonar buoys working reliably after entering the water, and the listening time of the patrol aircraft on the coverage array.

9. The passive sonar buoy deployment method according to claim 7, characterized in that, The target array is an interception array. The step of calculating the theoretical detection probability of the target array against the target includes: calculating the probability of a single column of the interception array detecting the target based on the effective detection range and contact probability of the sonar buoy, and the probability of the sonar buoy working reliably after entering the water; and calculating the theoretical detection probability of the interception array composed of each single column array against the target based on the probability of the single column array detecting the target.

10. The passive sonar buoy deployment method according to claim 7, characterized in that, The target array is an encirclement array. The step of calculating the theoretical detection probability of the target array for the target includes: calculating the probability of a single-layer encirclement array of the encirclement array detecting the target based on the effective detection range and contact probability of the sonar buoy, and the probability of the sonar buoy working reliably after entering the water; and calculating the theoretical detection probability of the encirclement array composed of each single-layer encirclement array for the target based on the probability of the single-layer encirclement array detecting the target.

11. The passive sonar buoy deployment method according to claim 1, characterized in that, The step of constructing a detection performance model of the sonar buoy array for the target based on the set of moving routes and the performance data of the sonar buoys includes: acquiring route data from the set of moving routes and dividing the route into multiple sub-route nodes at a set time interval; acquiring the coordinate position of a sonar buoy from the sonar buoy array and calculating the detection performance of the sonar buoy's coordinate position using bilinear interpolation based on the detection probability data; constructing a detection probability table for each of the sub-route nodes based on the detection performance of the sonar buoy's coordinate position; statistically analyzing all the sub-route nodes on the route and calculating the detection probability of the sonar buoy for the target on the route; and returning to the step of acquiring the coordinate position of a sonar buoy from the sonar buoy array and calculating the detection probability of the sonar buoy for the target using bilinear interpolation based on the detection probability data. The steps include: calculating the detection effectiveness of the sonar buoy's coordinate position using interpolation; iteratively calculating the detection probability of each sonar buoy in the sonar buoy array for the target on the route corresponding to the route data; calculating the total detection probability of the sonar buoy array for the target on the route corresponding to the route data based on the detection probabilities of each sonar buoy in the sonar buoy array for the target on the route data; returning to the step of obtaining a route data from the set of moving routes and dividing the route into multiple sub-route nodes at a set time interval, iteratively calculating the detection probability of the sonar buoy array for all routes; and calculating the detection probability of the sonar buoy array for the set of moving routes based on the detection probabilities of the sonar buoy array for all routes.

12. The passive sonar buoy deployment method according to claim 11, characterized in that, The process of obtaining the constraint parameters of the sonar buoy array in the sea area of ​​interest includes: setting the constraint parameters of the sonar buoy array according to the array deployment type of the sonar buoys; and adding the available deployment depth of the sonar buoy array.

13. The passive sonar buoy deployment method according to claim 12, characterized in that, The process of calculating the detection performance model using a numerical optimization algorithm to obtain the optimal sonar buoy array deployment parameters within the deployment area under the constrained parameters includes: randomly generating multiple sets of array parameters and corresponding iteration speeds under the constrained parameters of the sonar buoy array, and calculating the deployment positions of each sonar buoy in the sonar buoy array based on the array parameters; taking the detection probability corresponding to each current array group in the sonar buoy array as the historical optimal detection probability for each array group, and obtaining the maximum detection probability from the historical optimal detection probabilities as the global optimal detection probability; and determining the optimal deployment parameters based on the array parameters corresponding to the global optimal detection probability. The array parameters are updated at an iterative rate according to a set update method; the iteration position and the array parameters corresponding to the global optimal detection probability are updated based on the detection probabilities of each array group in the sonar buoy array; if the maximum number of iterations has not been reached, the step of updating the array parameters at an iterative rate according to the set update method based on the array parameters corresponding to the global optimal detection probability is returned; if the maximum number of iterations has been reached, the last updated global optimal detection probability is used as the output optimal detection probability; the array parameters corresponding to the optimal detection probability are the optimal sonar buoy array deployment parameters.

14. A passive sonar buoy deployment device, characterized in that, include: The route construction module is used to construct a set of movement routes of a target within a sea area of ​​interest; The set of motion routes is used to simulate the possible navigation paths of the target in the sea area of ​​interest; The motion construction module is used to construct the motion depth distribution of the target in the sea area of ​​interest; the motion depth distribution is used to simulate the possible navigation depth of the target in the sea area of ​​interest; the area construction module is used to construct the sonar buoy array deployment area and deployment depth on the possible navigation path based on the motion route set and the motion depth distribution; The performance calculation module is used to divide the sonar buoy array deployment area into grids and call the sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point in the sonar buoy array deployment area. The model building module is used to construct a detection performance model of the sonar buoy array against the target based on the set of motion routes and the performance data of the sonar buoys; the optimization calculation module is used to obtain the constraint parameters of the sonar buoy array in the sea area of ​​interest, calculate the detection performance model through a numerical optimization algorithm, and obtain the optimal sonar buoy array deployment parameters within the deployment area of ​​the sonar buoy array under the constraint parameters. The buoy deployment module is used to deploy sonar buoys within the sonar buoy array deployment area according to the sonar buoy array deployment parameters. The steps of dividing the sonar buoy array deployment area into a grid and calling a sonar performance prediction algorithm to calculate the performance data of the sonar buoys deployed at each grid point within the deployment area include: obtaining the minimum bounding rectangle of the deployment area; obtaining the coordinates of each grid point of the minimum bounding rectangle according to the set grid spacing; and calculating the detection effectiveness of each grid point for the current set of moving routes and saving the detection probability data based on sonar parameters, environmental parameters, calculation parameters, deployment depth, and navigation depth using the sonar performance prediction algorithm.

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