Multi-sensor cooperative search path planning method and device, electronic equipment and medium

Abstract

The application discloses a multi-sensor cooperative search path planning method and device, electronic equipment and medium, and belongs to the technical field of sensor cooperative control. The method comprises the following steps: acquiring performance parameters of multiple sensors and search environment parameters of a target area; establishing a sensor detection coverage capability model and an area cumulative detection coverage rate model corresponding to each sensor according to the performance parameters of each sensor and the search environment parameters of the target area; taking the load balancing of each sensor as a target, solving a search area division result according to the sensor detection coverage capability model and the search environment parameters; determining a constraint condition according to the search area division result, taking the maximum of the area cumulative detection coverage rate increment as a target, and solving the heading of each sensor at each time in a target time period according to the area cumulative detection coverage rate model to obtain a search path planning result. The application can improve the efficiency of multi-sensor cooperative search.

Description

Technical Field

[0001] This application belongs to the field of multi-sensor cooperative control technology, and in particular relates to a multi-sensor cooperative search path planning method, device, electronic device and medium. Background Technology

[0002] In fields such as target search and marine environmental monitoring, efficiently acquiring information from complex areas has become a core requirement for improving decision-making quality. However, single sensors have narrow detection ranges and limited information dimensions, making them insufficient to meet mission requirements. Therefore, current strategies typically employ multi-sensor collaborative search, planning search paths for each sensor and integrating the spatial coverage and complementary information advantages of multiple sensors to improve search efficiency.

[0003] In related technologies, mathematical models are typically built based on pre-collected prior information about the target, and an objective function is constructed to maximize the target detection probability or the number of detections. The search paths of each sensor are then directly solved. However, this strategy relies on accurate and sufficient prior information about the target. When the detection environment is complex or detection resources are limited, resulting in a severe lack of prior information, it is difficult to build an accurate mathematical model. This can easily lead to detection redundancy or blind spots between sensors, severely impacting search efficiency. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a multi-sensor cooperative search path planning method, apparatus, electronic device, and medium to improve the efficiency of multi-sensor cooperative search.

[0005] Firstly, this application provides a multi-sensor cooperative search path planning method, including:

[0006] Acquire performance parameters from multiple sensors and search environment parameters for the target area; Based on the performance parameters of each sensor and the search environment parameters of the target area, a sensor detection coverage capability model and a regional cumulative detection coverage model are established for each sensor. The sensor detection coverage capability model represents the effective detection area of ​​each sensor within the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area within the target time period. With the goal of load balancing for each sensor, the search area division result is solved based on the sensor detection coverage capability model and the search environment parameters; Based on the search area division results, the constraints are determined. With the goal of maximizing the incremental increase of the cumulative detection coverage of the area, the headings of each sensor at each time point within the target time period are solved according to the cumulative detection coverage model of the area, and the search path planning results are obtained.

[0007] The multi-sensor collaborative search path planning method provided in this application reduces the overlap of search areas between sensors by first dividing the search area corresponding to each sensor and then planning the search path for each sensor; by planning the search path with the goal of maximizing the incremental increase of the cumulative detection coverage of the area, it reduces the occurrence of coverage blind spots; thus, it can improve the efficiency of multi-sensor collaborative search.

[0008] According to one embodiment of this application, the performance parameters include: moving speed, sonar directivity index, and sonar detection threshold; the search environment parameters include: ambient background noise level; and the sensor detection coverage capability model is established using the following method: Obtain the detection probability threshold and the false alarm probability threshold; Calculate the signal margin threshold based on the detection probability threshold and the false alarm probability threshold; The effective detection radius is calculated based on the signal margin threshold, the sonar directivity index, the sonar detection threshold, and the ambient background noise level. Based on the effective detection radius and the moving speed, the effective detection area of ​​the sensor within the target time period is calculated to obtain the sensor detection coverage capability model.

[0009] In this embodiment, by first calculating the signal margin threshold required to meet the detection probability requirements, and then calculating based on the sensor detection capability and the ambient background noise level of the search environment, the sensor's detection coverage capability can be judged by comprehensively considering task requirements, sensor performance, and environmental conditions, thereby improving the accuracy of the sensor detection coverage capability model.

[0010] According to one embodiment of this application, when the sensor is in active operating mode, according to the equation:

[0011] The effective detection radius is obtained by solving the problem; in, SL represents the signal margin threshold, NL represents the active emission source level, DI represents the sonar directivity index, DT represents the sonar detection threshold, and TS represents the target reflection intensity. This indicates the effective detection radius.

[0012] According to one embodiment of this application, when the sensor is in passive operating mode, according to the equation:

[0013] The effective detection radius is obtained by solving the problem; in, denoted by , where sl represents the signal margin threshold, NL represents the target radiated noise level, DI represents the sonar directivity index, and DT represents the sonar detection threshold. This indicates the effective detection radius.

[0014] According to one embodiment of this application, the step of solving the search area division result based on the sensor detection coverage capability model and the search environment parameters, with the goal of load balancing of each sensor, includes: The sub-search regions corresponding to each sensor are used as the first optimization variables to initialize the particle swarm; the position of each particle in the particle swarm corresponds to a set of parameter values ​​of the first optimization variables. The sum of the squared differences of the detection ratio coefficients of each adjacent sensor is used as the first performance variable to construct the first objective function; the detection ratio coefficient is the ratio of the area of ​​the sub-search region corresponding to the target sensor to the detection coverage capability of the target sensor. Based on the first objective function, the position of each particle in the particle swarm is iteratively updated; If the iteration termination condition is met, the first optimized variable value corresponding to the historical optimal position of the particle swarm during the iteration update process is used as the search region partitioning result.

[0015] In this embodiment, by using the sum of the squared differences of the detection ratio coefficients of each adjacent sensor as the first performance variable and constructing the objective function, the area ratio of the sub-region corresponding to each sensor obtained by optimization can be made the same as the corresponding detection coverage ratio, thereby balancing the task load of each sensor; by using the particle swarm optimization algorithm to solve the search region partitioning result, the situation of the algorithm getting stuck in local optima can be reduced, thereby improving the quality of the optimized search region partitioning result.

[0016] According to one embodiment of this application, the cumulative detection coverage model of the region is established by the following method: Based on the target grid precision, the target area is divided into multiple grid points; For each target grid point, calculate the detection probability of each sensor for the target at the target grid point. If the detection probability is greater than or equal to the detection probability threshold, then add the target grid point to the set of coverable grid points. The ratio of the number of grid points in the coverable grid point set to the total number of grid points is calculated to obtain the cumulative detection coverage model of the region.

[0017] In this embodiment, by first dividing the target area into multiple grid points before performing calculations, the amount of computation can be reduced and the computational efficiency can be improved.

[0018] According to one embodiment of this application, the step of determining constraints based on the search area division results, with the objective of maximizing the incremental increase in regional cumulative detection coverage, and solving the heading of each sensor at each time point within the target time period based on the regional cumulative detection coverage model, includes: The headings of each sensor at the target time are combined as the second optimization variable to initialize the dung beetle population; the position of each dung beetle in the population corresponds to a set of candidate values ​​for the second optimization variable; The increment of the cumulative detection coverage of the region after the course combination decision is executed is used as the second performance variable to construct the second objective function; Based on the search region division results, set constraints; Based on the second objective function and the constraints, the position of each dung beetle in the dung beetle population is iteratively updated; When the iteration termination condition is met, the second optimized variable value corresponding to the optimal position of the dung beetle population is taken as the heading of each sensor at the target time.

[0019] In this embodiment, by using the combination of the headings of each sensor at the target time as the optimization variable, the combination of the headings of each sensor at each time is solved step by step, which can reduce the dimensionality explosion caused by the entire path planning; by using the dung beetle optimization algorithm, the way to explore the solution space during the solution process can be expanded, thereby further reducing the situation of getting trapped in local optima and improving the convergence speed of the algorithm; thus, the efficiency of solving the search path planning results can be improved.

[0020] Secondly, this application provides a multi-sensor cooperative search path planning device, comprising: The acquisition module is used to acquire performance parameters from multiple sensors and search environment parameters for the target area; The modeling module is used to establish a sensor detection coverage capability model and a regional cumulative detection coverage model for each sensor based on the performance parameters of each sensor and the search environment parameters of the target area. The sensor detection coverage capability model represents the effective detection area of ​​each sensor in the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area in the target time period. The first calculation module is used to solve the search area division result based on the sensor detection coverage capability model and the search environment parameters, with the goal of load balancing of each sensor. The second calculation module is used to determine the constraints based on the search area division results, with the objective of maximizing the incremental increase of the cumulative detection coverage of the area, and to solve the heading of each sensor at each time within the target time period based on the cumulative detection coverage model of the area, so as to obtain the search path planning results.

[0021] According to the multi-sensor collaborative search path planning device of this application, by first dividing the search area corresponding to each sensor and then planning the search path of each sensor, the overlap of search areas between sensors can be reduced; by planning the search path with the goal of maximizing the incremental increase of the cumulative detection coverage of the area, the occurrence of coverage blind spots can be reduced; thereby, the efficiency of multi-sensor collaborative search can be improved.

[0022] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-sensor cooperative search path planning method as described in the first aspect above.

[0023] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-sensor cooperative search path planning method as described in the first aspect above.

[0024] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the multi-sensor collaborative search path planning method as described in the first aspect above.

[0025] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the multi-sensor cooperative search path planning method as described in the first aspect above.

[0026] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: According to the multi-sensor collaborative search path planning method of this application, by first dividing the search area corresponding to each sensor and then planning the search path of each sensor, the overlap of search areas between sensors can be reduced; by planning the search path with the goal of maximizing the incremental increase of the cumulative detection coverage of the area, the occurrence of coverage blind spots can be reduced; thus, the efficiency of multi-sensor collaborative search can be improved.

[0027] In some embodiments, by first calculating the signal margin threshold required to meet the detection probability requirements, and then calculating based on the sensor's detection capability and the ambient background noise level of the search environment, the sensor's detection coverage capability can be judged by comprehensively considering task requirements, sensor performance, and environmental conditions, thereby improving the accuracy of the sensor's detection coverage capability model.

[0028] In some embodiments, by using the sum of the squared differences of the detection ratio coefficients of each adjacent sensor as the first performance variable and constructing the objective function, the area ratio of the sub-region corresponding to each sensor obtained by optimization can be made the same as the corresponding detection coverage ratio, thereby balancing the task load of each sensor; by using the particle swarm optimization algorithm to solve the search region partitioning result, the situation of the algorithm getting stuck in local optima can be reduced, thereby improving the quality of the optimized search region partitioning result.

[0029] In some embodiments, dividing the target area into multiple grid points before performing calculations can reduce the amount of computation and improve computational efficiency.

[0030] In some embodiments, by using the combination of the headings of each sensor at the target time as the optimization variable and solving the combination of the headings of each sensor at each time step by step, the dimensionality explosion caused by the whole path planning can be reduced; by using the dung beetle optimization algorithm, the way to explore the solution space during the solution process can be expanded, thereby further reducing the situation of getting trapped in local optima and improving the convergence speed of the algorithm; thus, the efficiency of solving the search path planning results can be improved.

[0031] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the multi-sensor collaborative search path planning method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the effective detection area of ​​the sensor provided in the embodiments of this application within a target time period; Figure 3 This is a schematic diagram of the search region division results provided in an embodiment of this application; Figure 4This is a schematic diagram of the structure of the multi-sensor collaborative search path planning device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0035] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0036] The multi-sensor cooperative search path planning method, apparatus, electronic device and medium provided in the embodiments of this application can be applied to any multi-sensor cooperative search scenario, such as multi-sonar sensor cooperative search for underwater targets, and the embodiments of this application do not limit this.

[0037] The multi-sensor collaborative search path planning method, apparatus, electronic device, and medium provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0038] Among them, the multi-sensor collaborative search path planning method can be applied to the terminal, specifically executed by the hardware or software in the terminal.

[0039] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that, in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).

[0040] The following embodiments describe a terminal including a display and a touch-sensitive surface. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.

[0041] The multi-sensor collaborative search path planning method provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the multi-sensor collaborative search path planning method. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, etc. The following uses an electronic device as the execution subject to illustrate the multi-sensor collaborative search path planning method provided in this application embodiment.

[0042] like Figure 1 As shown, the multi-sensor collaborative search path planning method includes steps 110, 120, 130 and 140.

[0043] Step 110: Obtain the performance parameters of multiple sensors and the search environment parameters of the target area.

[0044] In this embodiment of the application, the target area refers to the area where a detection target may exist and a search task needs to be carried out; the sensor refers to a device that can move in the target area and collect information, for example, a sonar.

[0045] Sensor performance parameters refer to parameters that describe the sensor's information acquisition capabilities, such as the sensor's moving speed, battery life, and effective detection radius. Users can pre-set the performance parameters of each sensor based on the specific sensors to be used in the search task.

[0046] Search environment parameters refer to parameters that describe the environmental characteristics of the target area, such as the boundary location of the target area, background noise level, ocean current direction and intensity, etc. These parameters can be preset by the user based on historical statistical data, real-time preliminary measurements, etc. In some embodiments, the search environment parameters may also include data describing the characteristics of the target being detected, such as the radiated sound source level of the target.

[0047] Step 120: Based on the performance parameters of each sensor and the search environment parameters of the target area, establish the sensor detection coverage capability model and the regional cumulative detection coverage model for each sensor. The sensor detection coverage capability model represents the effective detection area of ​​each sensor within the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area within the target time period.

[0048] In this embodiment of the application, effective detection means that the sensor can accurately detect the target present at the target location, and will not falsely report that a target has been detected when there is no target present at the target location.

[0049] The effective detection area refers to the area that a sensor can effectively detect within a target time period. The calculation method for the effective detection area can be preset by the user. For example, for each sensor s, a simulation environment can be built based on the sensor's performance parameters, the target area's search environment parameters, and physical characteristics to simulate the sensor's detection process within the target area. The effective detection area of ​​the sensor s per unit time can then be determined based on the simulation data. Then, based on the length of the preset target time period, the effective detection area of ​​sensor s within the target time period is determined. Thus, the sensor detection coverage capability model corresponding to sensor s is obtained.

[0050] Based on the detection capability of sensor s, it can be calculated whether sensor s will be within the target time period. Internal path Moving, capable of effectively detecting areas The effective detection area of ​​each sensor is calculated. Then, calculate the union of each region. and will As a set of sensors along the path within the target time period The corresponding path movement in the calculation represents the total area that can be effectively detected by each sensor; The ratio of the area of ​​the target area to the area of ​​the target area Thus, a regional cumulative detection coverage model is obtained.

[0051] Of course, other methods can also be used to establish sensor detection coverage capability models and regional cumulative detection coverage models for each sensor, such as establishing models based on measured statistical data obtained from historical missions. This application does not limit this approach.

[0052] Step 130: With the goal of load balancing for each sensor, solve for the search area division result based on the sensor detection coverage capability model and search environment parameters.

[0053] In the embodiments of this application, the load balancing of each sensor refers to the fact that during the task of collaborative search by each sensor, the workload of each sensor is close to that of other sensors, and the effective detection area of ​​sensor s can be used to characterize its detection capability.

[0054] The search area can be pre-defined by the user, for example, based on the effective detection area of ​​each sensor within the target time period. Divide the target area into S sub-regions. ,in The ratio of the area of ​​[the target area] to the total area of ​​the target region is [the ratio of the area of ​​the target area to The boundary coordinates of each sub-region can be used as optimization variables. Constraints can be constructed based on the boundary coordinates of the target region, and then... Construct an objective function and solve it to obtain the boundary coordinates of the sub-regions corresponding to each sensor, which are used as the results of the search area division.

[0055] Heuristic optimization algorithms such as Genetic Algorithm (GA) can be used to solve the problem. Of course, other methods such as classical algorithms based on mathematical programming, such as spatial recursive bisection method and Sequential Quadratic Programming (SQP), can also be used to solve the search region partitioning result. This application does not limit the specific methods used in this embodiment.

[0056] Step 140: Determine the constraints based on the search area division results. With the goal of maximizing the incremental increase of the cumulative detection coverage of the area, solve the heading of each sensor at each time point within the target time period based on the cumulative detection coverage model of the area to obtain the search path planning results.

[0057] In this embodiment, the search path planning result includes the heading of each sensor at each time point within the target time period. For example, the search path of sensor s. It can contain a series of turning points ,in It can be an angle value, representing the direction of movement of sensor s at time t, i.e., heading.

[0058] The heading of each sensor at the target time t can be determined. As optimization variables, with the constraint that the position of each sensor does not exceed the corresponding sub-region in the search area division result, starting from the initial time of the target time period, algorithms such as GA and SQP are used to sequentially solve for maximizing the increase in the cumulative detection coverage of the region relative to the previous time period. This continues until the end of the target time period is reached, thus obtaining the search path planning result. .

[0059] Of course, reinforcement learning-based architectures such as DQN (Deep Q-Learning) and deep learning-based architectures such as VIN (Value Iteration Network) can also be used to directly solve for the search path planning results. This application does not limit this approach.

[0060] The multi-sensor collaborative search path planning method provided in this application reduces the overlap of search areas between sensors by first dividing the search area corresponding to each sensor and then planning the search path for each sensor; and reduces the occurrence of coverage blind spots by planning the search path with the goal of maximizing the incremental increase of the cumulative detection coverage of the area; thereby improving the efficiency of multi-sensor collaborative search.

[0061] In some embodiments, performance parameters include: moving speed, sonar directivity index, and sonar detection threshold; search environment parameters include: ambient background noise level; and a sensor detection coverage capability model is established using the following methods: Obtain the detection probability threshold and the false alarm probability threshold; Calculate the signal margin threshold based on the detection probability threshold and the false alarm probability threshold; The effective detection radius is calculated based on the signal margin threshold, sonar directivity index, sonar detection threshold, and ambient background noise level. Based on the effective detection radius and moving speed, the effective detection area of ​​the sensor within the target time period is calculated, and the sensor detection coverage capability model is obtained.

[0062] In the embodiments of this application, the velocity (V) refers to the speed at which the sensor moves within the target area; the sonar directivity index (DI) is a value in decibels (dB) that describes the ability of a transducer (or array) to concentrate sound wave energy for transmission or reception; the ambient background noise level (NL) refers to the noise level present in the target area; and the sonar detection threshold (DT) refers to the number of decibels by which the signal sound level exceeds the noise sound level.

[0063] First, obtain the detection probability threshold and the false alarm probability threshold. The detection probability (PD) refers to the probability that the sensor successfully detects a target object at a certain location; the false alarm probability (Pf) refers to the probability that the sensor falsely reports a "target detected" when no target is actually present. The detection probability threshold can be preset by the user. And the false alarm probability threshold Pf, for example, can be set , .

[0064] After obtaining the detection probability threshold and the false alarm probability threshold, the signal excess threshold is calculated. The signal excess threshold (SE) refers to the minimum signal-to-noise ratio required for the sensor to successfully detect the target object. An equation can be constructed as follows:

[0065] And the signal margin threshold is obtained by solving. .

[0066] After calculating the signal margin threshold, the effective detection radius is calculated based on the signal margin threshold, sonar directivity index, sonar detection threshold, and ambient background noise level. The effective detection radius refers to the distance between the target object and the sensor when the detection probability reaches the detection probability threshold. It can be considered that at a certain moment, the sensor can effectively detect a target object with a radius of [missing information - likely a threshold value]. The target object that exists within the area.

[0067] In some embodiments, when the sensor is in active operating mode, according to the equation:

[0068] The effective detection radius can be obtained by solving the problem. in, SL represents the signal margin threshold, NL represents the active emission source level, DI represents the sonar directivity index, DT represents the sonar detection threshold, and TS represents the target reflection intensity. Indicates the effective detection radius.

[0069] In this embodiment, the active operating mode refers to the mode in which the sensor actively emits signals to the external environment and detects by receiving signals reflected back from the target object. The active emission source level (SL) is the sound level of the signal actively emitted by the sensor, and the target reflection intensity (TS) is the sound level of the signal reflected back from the target object.

[0070] In some embodiments, when the sensor is in passive operating mode, according to the equation:

[0071] The effective detection radius can be obtained by solving the problem. in, denoted by , where sl represents the signal margin threshold, NL represents the target radiated noise level, DI represents the sonar directivity index, and DT represents the sonar detection threshold. Indicates the effective detection radius.

[0072] In this embodiment, the passive operating mode refers to a mode in which the sensor does not emit signals to the external environment, but detects by listening to the signals emitted by the target object itself. The target radiated noise level is the sound level of the signal emitted by the target object itself.

[0073] After calculating the effective detection radius, based on the effective detection radius... Given the moving speed V, the effective detection area of ​​the sensor within the target time period is calculated. The calculation method for the effective detection area can be preset by the user. For example, ... Figure 2 As shown, it can be assumed that the sensor operates for a duration of... During the target time period, it moves at a uniform linear velocity V, and the effective detection area P is approximately determined as... .

[0074] Of course, for other types and operating modes of sensors, the effective detection radius and effective detection area can be calculated in other ways, and this application embodiment does not limit this.

[0075] In this embodiment, by first calculating the signal margin threshold required to meet the detection probability requirements, and then calculating based on the sensor detection capability and the ambient background noise level of the search environment, the sensor's detection coverage capability can be judged by comprehensively considering task requirements, sensor performance, and environmental conditions, thereby improving the accuracy of the sensor detection coverage capability model.

[0076] In some embodiments, with the goal of load balancing among the sensors, the search area division result is solved based on the sensor detection coverage capability model and search environment parameters, including: The sub-search regions corresponding to each sensor are used as the first optimization variable to initialize the particle swarm; the position of each particle in the particle swarm corresponds to a set of parameter values ​​of the first optimization variable. The sum of the squared differences of the detection ratio coefficients of each adjacent sensor is used as the first performance variable to construct the first objective function; the detection ratio coefficient is the ratio of the area of ​​the sub-search region corresponding to the target sensor to the detection coverage capability of the target sensor. Based on the first objective function, the position of each particle in the particle swarm is iteratively updated; If the iteration termination condition is met, the first optimization variable value corresponding to the historical best position of the particle swarm during the iteration update process is used as the search region partitioning result.

[0077] Particle Swarm Optimization (PSO) is a stochastic optimization algorithm based on swarm intelligence. It searches the solution space by simulating the social interaction and information sharing mechanisms of particles in a swarm. In PSO, each particle has two attributes: position and velocity. The particle's position represents a candidate solution to the optimization problem in the solution space, while the velocity determines the particle's direction and distance of movement within the solution space.

[0078] In this embodiment, the sub-search region corresponding to each sensor is used as the first optimization variable. For example, such as Figure 3As shown, assuming the target area is a rectangle centered at point O, the detection coverage capabilities are respectively... , and If the three sensors perform a collaborative search task, the target area needs to be divided into three sub-search areas. The partitioning can be set to start from point O, with directions of... , and Three rays , and ,Will , The area enclosed by the boundary of the target area is The area is defined as the sub-search area corresponding to sensor 1. , The area enclosed by the boundary of the target area is The area is defined as the sub-search area corresponding to sensor 2. , The area enclosed by the boundary of the target area is The region is determined as the sub-search region corresponding to sensor 3. Therefore, the first optimization variable is the partitioning result. .

[0079] After setting the first optimization variable, initialize the particle swarm. The number of particles N in the swarm can be preset by the user; in this case, N particles are randomly generated during the algorithm initialization phase. .in, The position of particle i corresponds to the partitioning result. A set of candidate values; Let be the velocity of particle i, used to control the direction and distance of particle i's movement in the solution space.

[0080] After the particle swarm initialization is completed, the sum of the squared differences of the detection ratio coefficients of each adjacent sensor is used as the first performance variable to construct the first objective function; where the detection ratio coefficient is the ratio of the area of ​​the sub-search region corresponding to the target sensor to the detection coverage capability of the target sensor.

[0081] For example, the first objective function is constructed as follows:

[0082] in, The magnitude of this value reflects the degree to which the corresponding candidate solution satisfies the optimization objective. In the embodiments of this application, The smaller the value, the closer the corresponding candidate solution is to the optimal solution.

[0083] In some embodiments, boundary conditions can also be set according to other parameters preset by the user. For example, such as Figure 3 As shown, if the initial positions of sensors 1, 2, and 3 have been predetermined, and each sensor's initial position should be within its corresponding sub-region, then the boundary conditions can be set as follows:

[0084] in, , These represent the angles between the initial positions of sensors 1, 2, and 3 and the line connecting them to point O.

[0085] Finally, the iteration termination condition of the algorithm is determined. This termination condition can be preset by the user; for example, a maximum number of iterations, Max_epoch, can be preset, and the iteration will terminate and the result output after reaching the Max_epoch iteration.

[0086] After setting the particle swarm parameters, the first objective function, boundary conditions, iteration termination conditions, and other algorithm parameters, the position of each particle in the particle swarm is updated iteratively.

[0087] In the k-th iteration, the first objective function value corresponding to each particle position is first calculated. .

[0088] Then, update the optimal historical position of each particle. The optimal historical position of the particle swarm as a whole .

[0089] Calculated and Then, the velocity and position of each particle are updated according to user-preset rules. For example, it can be done using the formula:

[0090]

[0091] Update the velocity of particle i to Location updated to Where w is the inertia weighting coefficient, This is an individual learning factor used to control the degree to which a particle references its own best historical position; This is a social learning factor used to control the degree to which the overall optimal historical position of the particle reference swarm is determined. and It is a random number in the range (0, 1).

[0092] You can also use the formula:

[0093] Boundary treatment is applied to the position and velocity of each particle. Among these, The maximum particle speed can be preset by the user.

[0094] After completing boundary handling, begin the (k+1)th iteration; stop iterating after reaching the Max_epoch, and then... As a result of the search area division.

[0095] In this embodiment, by using the sum of the squared differences of the detection ratio coefficients of each adjacent sensor as the first performance variable and constructing the objective function, the area ratio of the sub-region corresponding to each sensor obtained by optimization can be made the same as the corresponding detection coverage ratio, thereby balancing the task load of each sensor; by using the particle swarm optimization algorithm to solve the search region partitioning result, the situation of the algorithm getting stuck in local optima can be reduced, thereby improving the quality of the optimized search region partitioning result.

[0096] In some embodiments, a regional cumulative detection coverage model is established using the following methods: Based on the target grid precision, the target area is divided into multiple grid points; For each target grid point, calculate the detection probability of each sensor for the target at the target grid point. If the detection probability is greater than or equal to the detection probability threshold, add the target grid point to the set of grid points that can be covered. The ratio of the number of grid points in the coverable grid point set to the total number of grid points is calculated to obtain the regional cumulative detection coverage model.

[0097] In this embodiment, the target region is first divided into multiple grid points based on the target grid precision. The target grid precision, used to determine the level of detail in the region's cumulative detection coverage model, can be preset by the user. For example, the target grid precision can be set to [value missing]. Assume the target area is The rectangle is then divided into... 1 grid point, of which 1 grid point The coordinates are:

[0098] After dividing the data into multiple grid points, the detection probability of each sensor for the target at each grid point is calculated. Users can pre-set the detection probability calculation method based on sensor type and collaborative working mode.

[0099] For example, in the case of coordinated detection by S=3 sonar sensors in passive operating modes, the target time accuracy can be preset by the user. The duration is The target time period is divided into Individual time period.

[0100] First, follow the formula:

[0101] The calculation yields the target grid point for each sensor during the k-th sub-time period. Detection probability of the target .in, These represent the s-th sensor; This represents the grid point coordinates corresponding to the location of the s-th sensor at the end of the k-th sub-time period; Indicates the target radiated noise from the grid points spread to Signal margin at the location; This indicates that the s-th sensor has effectively detected... The probability of being the target; This indicates that each sensor effectively detected... The maximum probability of finding the target is taken as the detection probability.

[0102] Calculate the detection probability Then, the detection probability is compared with the user-preset detection probability threshold. Comparison. If Then Add to the set of overlayable grid points In the middle. Among them, the set of grid points that can be covered. It is a set of coordinates of multiple grid points, representing the area that can be effectively detected by each sensor from the start time of detection to the end time of the kth sub-time period.

[0103] Get the set of coverable grid points Then, calculate Number of grid points The ratio of the total number of grid points , which is the cumulative detection coverage of the region at the end of the kth sub-time period.

[0104] In this embodiment, by first dividing the target area into multiple grid points before performing calculations, the amount of computation can be reduced and the computational efficiency can be improved.

[0105] In some embodiments, constraints are determined based on the search area division results, with the objective of maximizing the incremental increase in the cumulative detection coverage of the area. Based on the cumulative detection coverage model of the area, the headings of each sensor at each time point within the target time period are calculated, including: The combined headings of each sensor at the target time are used as the second optimization variable to initialize the dung beetle population; the position of each dung beetle in the population corresponds to a set of candidate values ​​for the second optimization variable. The increment of the cumulative detection coverage of the region after the course combination decision is used as the second performance variable to construct the second objective function; Based on the search region division results, set constraints; Based on the second objective function and constraints, the position of each dung beetle in the dung beetle population is updated iteratively; When the iteration termination condition is met, the value of the second optimization variable corresponding to the optimal position of the dung beetle population is taken as the heading of each sensor at the target time.

[0106] The Improved Dung Beetle Optimizer (IDBO) is a heuristic optimization algorithm based on swarm intelligence. It searches the solution space by simulating the complex social behaviors of dung beetles, such as rolling balls, reproduction, and theft. In IDBO, each dung beetle has positional and behavioral role attributes. The dung beetle's position corresponds to a candidate solution in the solution space, while its behavioral role determines its search strategy and movement within the solution space. For example, four behavioral roles can be defined: rolling dung beetle, small dung beetle, reproducing dung beetle, and theft dung beetle.

[0107] In this embodiment, the heading combination of each sensor at the target time is used as the second optimization variable. For example, if the start time of the kth sub-time period is taken as the target time, the second optimization variable is... Where S is the total number of sensors, This represents the heading of the s-th sensor at the start of the k-th sub-time period.

[0108] After setting the second optimization variable, the dung beetle population is initialized. The user can pre-define the number M of dung beetles in the population and the proportion of dung beetles representing each behavioral role. During the algorithm initialization phase, M dung beetles are randomly generated, with each dung beetle's position corresponding to a combination of headings. A set of candidate values. The ICMIC (Iterative Chaotic Map with Infinite Collapses) strategy can be used to generate M sets of S-dimensional random numbers. As the initial location of each dung beetle in the dung beetle population, among which Let be the initial position of the i-th dung beetle.

[0109] After initializing the dung beetle population, the increment of the cumulative detection coverage of the region after the course combination decision is executed is used as the second performance variable to construct the second objective function.

[0110] For example, the second objective function is constructed as follows:

[0111] in, This represents the cumulative detection coverage of the region at the end of the k-th sub-time period. This represents the cumulative detection coverage of the region at the end of the (k-1)th sub-time period (i.e., the start of the kth sub-time period). This means executing a course combination decision. This can lead to an increase in the cumulative detection coverage of the region. The larger the value, the closer the corresponding candidate solution is to the optimal solution.

[0112] Based on the search area division results, set constraints. For example, the sub-search area assigned to sensor s is... The rectangle within the grid point coordinate range can be determined based on the position of sensor s at the end of the (k-1)th sub-time period. Calculate and execute course combination decision Then, the position reached at the end of the kth sub-time period. If the coordinates are outside the range, it is determined that the constraint is exceeded and the corresponding boundary processing is performed.

[0113] Finally, the iteration termination condition of the algorithm is determined. This termination condition can be preset by the user; for example, a maximum number of iterations can be preset. In the iteration to the The iteration terminates after this step and the result is output.

[0114] After setting the dung beetle population parameters, the second objective function, boundary conditions, iteration termination conditions, and other algorithm parameters, the position of each dung beetle in the dung beetle population is updated iteratively.

[0115] In the t-th iteration, the second objective function value corresponding to each dung beetle position is first calculated. .

[0116] Then, update the optimal historical position of each dung beetle individual up to the t-th iteration. The optimal historical position of the dung beetle population as a whole .

[0117] Calculated and Then, the positions of each dung beetle are updated according to user-preset rules. For example, the positions of each rolling dung beetle can be updated first. The rolling dung beetle simulates the behavior of dung beetles pushing dung balls in a straight line in nature, and is used for global search in the solution space. An improved sinusoidal guidance strategy incorporating dynamic inertia weights can be adopted, according to the formula:

[0118] Update the positions of all rolling dung beetles. Among them, As a sinusoidal phase control factor, it can be... Random numbers between; The individual learning factor can be a random number between (0,1). For dynamic inertia weights, according to the formula:

[0119] The calculation yielded the following result. and These are the maximum and minimum values ​​of the dynamic inertia weight, respectively, and index is the exponent parameter, which can be preset by the user.

[0120] These are dynamic baseline coefficients used to control the search step size, according to the formula:

[0121] Calculated.

[0122] After calculating the location of each dung beetle, update the locations of the breeding dung beetle, baby dung beetle, and thieving dung beetle.

[0123] Among these methods, dung beetle breeding simulates the natural behavior of dung beetles burying dung balls underground to lay eggs and reproduce, and is used to further develop areas near optimal locations. This can be done using the following formula:

[0124] Update the locations of all breeding dung beetles. Among them, Individual learning factors for dung beetle breeding Social learning factors for the reproduction of dung beetles and It can be preset by the user.

[0125] The dung beetle is a new character added to the improved dung beetle optimization algorithm compared to the classic dung beetle optimization algorithm. It simulates dung beetles of different ages in nature and is used to balance global exploration and local exploitation during the search process. It can be expressed by the formula:

[0126] Update the location of each dung beetle. Among them, To explore weights, they can be preset by the user; This refers to the development weight; r is the random perturbation coefficient, which can be preset by the user.

[0127] The dung beetle stealing technique simulates the behavior of dung beetles stealing high-quality dung balls from other dung beetles in nature, and is used to accelerate convergence by utilizing globally optimal information. It can be done using the formula:

[0128] Update the location of each dung beetle. Here, 's' is the theft coefficient, which can be preset by the user. and A random number within the range (0,1); The location of the food source can be determined by pre-defined rules, such as based on the location corresponding to the current location. The dung beetles in the population are sorted, and one dung beetle is randomly selected from the top 20% of the dung beetles. The location of the selected dung beetle is used as the food source location.

[0129] In some embodiments, the user can pre-define a perturbation algorithm to perturb the position of each dung beetle in the population, thereby reducing the likelihood of the algorithm converging too early or getting trapped in local optima. For example, a Gaussian-Cauchy dynamic hybrid mutation strategy can be used, according to the formula:

[0130] The location of each dung beetle within the disturbed dung beetle population. , The position of the i-th dung beetle in the dung beetle population before it is disturbed; To The positions of each dung beetle after the disturbance; The mean is Standard deviation is Gaussian random numbers; The position parameter is Scale parameters are Cauchy random numbers. , , and It can be preset by the user.

[0131] After perturbing the positions of individual dung beetles in the population, a greedy mechanism can be used, based on the formula:

[0132] Compare the second objective function values ​​corresponding to the positions of each dung beetle before and after the disturbance, select the better dung beetle position, and discard the worse dung beetle positions.

[0133] After updating the positions of each dung beetle, boundary processing is performed on each dung beetle position according to the constraints and preset boundary processing rules.

[0134] After completing the boundary handling, begin the (t+1)th iteration; when iterating to... After this, the iteration stops, and the optimal historical position of the dung beetle population as a whole is determined. The corresponding heading combination serves as the heading of each sensor at the start of the kth sub-time period. .

[0135] Calculated Then, the start time of the (k+1)th sub-time period is taken as the target time. The dung beetle population is re-initialized, the second objective function is constructed, constraints are set, and the next round of solving begins.

[0136] In this embodiment, by using the combination of the headings of each sensor at the target time as the optimization variable, the combination of the headings of each sensor at each time is solved step by step, which can reduce the dimensionality explosion caused by the entire path planning; by using the dung beetle optimization algorithm, the way to explore the solution space during the solution process can be expanded, thereby further reducing the situation of getting trapped in local optima and improving the convergence speed of the algorithm; thus, the efficiency of solving the search path planning results can be improved.

[0137] The multi-sensor cooperative search path planning method provided in this application can be executed by a multi-sensor cooperative search path planning device. This application uses the example of a multi-sensor cooperative search path planning device executing the multi-sensor cooperative search path planning method to illustrate the multi-sensor cooperative search path planning device provided in this application.

[0138] This application also provides a multi-sensor collaborative search path planning device.

[0139] like Figure 4 As shown, the multi-sensor collaborative search path planning device includes: The acquisition module 410 is used to acquire performance parameters of multiple sensors and search environment parameters of the target area; Modeling module 420 is used to establish sensor detection coverage capability model and regional cumulative detection coverage model for each sensor based on the performance parameters of each sensor and the search environment parameters of the target area. The sensor detection coverage capability model represents the effective detection area of ​​each sensor in the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area in the target time period. The first calculation module 430 is used to solve the search area division result based on the sensor detection coverage capability model and search environment parameters with the goal of load balancing of each sensor. The second calculation module 440 is used to determine the constraints based on the search area division results. With the goal of maximizing the incremental increase of the cumulative detection coverage of the area, it solves the heading of each sensor at each time within the target time period based on the cumulative detection coverage model of the area, and obtains the search path planning results.

[0140] According to the multi-sensor collaborative search path planning device of this application, by first dividing the search area corresponding to each sensor and then planning the search path of each sensor, the overlap of search areas between sensors can be reduced; by planning the search path with the goal of maximizing the incremental increase of the cumulative detection coverage of the area, the occurrence of coverage blind spots can be reduced; thereby, the efficiency of multi-sensor collaborative search can be improved.

[0141] In some embodiments, the modeling module 420 is further configured to: Obtain the detection probability threshold and the false alarm probability threshold; Calculate the signal margin threshold based on the detection probability threshold and the false alarm probability threshold; The effective detection radius is calculated based on the signal margin threshold, sonar directivity index, sonar detection threshold, and ambient background noise level. Based on the effective detection radius and moving speed, the effective detection area of ​​the sensor within the target time period is calculated, and the sensor detection coverage capability model is obtained.

[0142] In some embodiments, the first computing module 430 is further configured to: The sub-search regions corresponding to each sensor are used as the first optimization variable to initialize the particle swarm; the position of each particle in the particle swarm corresponds to a set of parameter values ​​of the first optimization variable. The sum of the squared differences of the detection ratio coefficients of each adjacent sensor is used as the first performance variable to construct the first objective function; the detection ratio coefficient is the ratio of the area of ​​the sub-search region corresponding to the target sensor to the detection coverage capability of the target sensor. Based on the first objective function, the position of each particle in the particle swarm is iteratively updated; If the iteration termination condition is met, the first optimization variable value corresponding to the historical best position of the particle swarm during the iteration update process is used as the search region partitioning result.

[0143] In some embodiments, the modeling module 420 is further configured to: Based on the target grid precision, the target area is divided into multiple grid points; For each target grid point, calculate the detection probability of each sensor for the target at the target grid point. If the detection probability is greater than or equal to the detection probability threshold, add the target grid point to the set of grid points that can be covered. The ratio of the number of grid points in the coverable grid point set to the total number of grid points is calculated to obtain the regional cumulative detection coverage model.

[0144] In some embodiments, the second computing module 440 is further configured to: The combined headings of each sensor at the target time are used as the second optimization variable to initialize the dung beetle population; the position of each dung beetle in the population corresponds to a set of candidate values ​​for the second optimization variable. The increment of the cumulative detection coverage of the region after the course combination decision is used as the second performance variable to construct the second objective function; Based on the search region division results, set constraints; Based on the second objective function and constraints, the position of each dung beetle in the dung beetle population is updated iteratively; When the iteration termination condition is met, the value of the second optimization variable corresponding to the optimal position of the dung beetle population is taken as the heading of each sensor at the target time.

[0145] The multi-sensor collaborative search path planning device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0146] The multi-sensor collaborative search path planning device in this application embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems; this application embodiment does not specifically limit it.

[0147] In some embodiments, such as Figure 3 As shown, this application embodiment also provides an electronic device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the program is executed by the processor 501, it implements the various processes of the above-described multi-sensor collaborative search path planning method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0148] It should be noted that the electronic devices in the embodiments of this application include the aforementioned mobile electronic devices and non-mobile electronic devices.

[0149] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described multi-sensor collaborative search path planning method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0150] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0151] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described multi-sensor collaborative search path planning method.

[0152] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0153] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described multi-sensor collaborative search path planning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0154] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0155] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0157] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0158] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0159] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A multi-sensor collaborative search path planning method, characterized in that, include: Acquire performance parameters from multiple sensors and search environment parameters for the target area; Based on the performance parameters of each sensor and the search environment parameters of the target area, a sensor detection coverage capability model and a regional cumulative detection coverage model are established for each sensor. The sensor detection coverage capability model represents the effective detection area of ​​each sensor within the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area within the target time period. With the goal of load balancing for each sensor, the search area division result is solved based on the sensor detection coverage capability model and the search environment parameters; Based on the search area division results, the constraints are determined. With the goal of maximizing the incremental increase of the cumulative detection coverage of the area, the headings of each sensor at each time point within the target time period are solved according to the cumulative detection coverage model of the area, and the search path planning results are obtained.

2. The method according to claim 1, characterized in that, The performance parameters include: moving speed, sonar directivity index, and sonar detection threshold; the search environment parameters include: ambient background noise level; the sensor detection coverage capability model is established using the following method: Obtain the detection probability threshold and the false alarm probability threshold; Calculate the signal margin threshold based on the detection probability threshold and the false alarm probability threshold; The effective detection radius is calculated based on the signal margin threshold, the sonar directivity index, the sonar detection threshold, and the ambient background noise level. Based on the effective detection radius and the moving speed, the effective detection area of ​​the sensor within the target time period is calculated to obtain the sensor detection coverage capability model.

3. The method according to claim 2, characterized in that, When the sensor is in active operating mode, according to the equation: The effective detection radius is obtained by solving the problem; in, SL represents the signal margin threshold, NL represents the active emission source level, DI represents the sonar directivity index, DT represents the sonar detection threshold, and TS represents the target reflection intensity. This indicates the effective detection radius.

4. The method according to claim 2, characterized in that, When the sensor is in passive operating mode, according to the equation: The effective detection radius is obtained by solving the problem; in, denoted by , where sl represents the signal margin threshold, NL represents the target radiated noise level, DI represents the sonar directivity index, and DT represents the sonar detection threshold. This indicates the effective detection radius.

5. The method according to claim 1, characterized in that, The step of achieving load balancing for each sensor, and solving for the search area division result based on the sensor detection coverage capability model and the search environment parameters, includes: The sub-search regions corresponding to each sensor are used as the first optimization variables to initialize the particle swarm; the position of each particle in the particle swarm corresponds to a set of parameter values ​​of the first optimization variables. The sum of the squared differences of the detection ratio coefficients of each adjacent sensor is used as the first performance variable to construct the first objective function; the detection ratio coefficient is the ratio of the area of ​​the sub-search region corresponding to the target sensor to the detection coverage capability of the target sensor. Based on the first objective function, the position of each particle in the particle swarm is iteratively updated; If the iteration termination condition is met, the first optimized variable value corresponding to the historical optimal position of the particle swarm during the iteration update process is used as the search region partitioning result.

6. The method according to claim 1, characterized in that, The cumulative detection coverage model for the region is established using the following method: Based on the target grid precision, the target area is divided into multiple grid points; For each target grid point, calculate the detection probability of each sensor for the target at the target grid point. If the detection probability is greater than or equal to the detection probability threshold, then add the target grid point to the set of coverable grid points. The ratio of the number of grid points in the coverable grid point set to the total number of grid points is calculated to obtain the cumulative detection coverage model of the region.

7. The method according to claim 1, characterized in that, The step of determining constraints based on the search area division results, with the objective of maximizing the incremental increase in the cumulative detection coverage of the area, and solving the heading of each sensor at each time point within the target time period based on the cumulative detection coverage model of the area, includes: The headings of each sensor at the target time are combined as the second optimization variable to initialize the dung beetle population; the position of each dung beetle in the population corresponds to a set of candidate values ​​for the second optimization variable; The increment of the cumulative detection coverage of the region after the course combination decision is executed is used as the second performance variable to construct the second objective function; Based on the search region division results, set constraints; Based on the second objective function and the constraints, the position of each dung beetle in the dung beetle population is iteratively updated; When the iteration termination condition is met, the second optimized variable value corresponding to the optimal position of the dung beetle population is taken as the heading of each sensor at the target time.

8. A multi-sensor collaborative path planning device, characterized in that, include: The acquisition module is used to acquire performance parameters from multiple sensors and search environment parameters for the target area; The modeling module is used to establish a sensor detection coverage capability model and a regional cumulative detection coverage model for each sensor based on the performance parameters of each sensor and the search environment parameters of the target area. The sensor detection coverage capability model represents the effective detection area of ​​each sensor in the target time period, and the regional cumulative detection coverage model represents the ratio of the total area of ​​the area that can be effectively detected by each sensor to the area of ​​the target area in the target time period. The first calculation module is used to solve the search area division result based on the sensor detection coverage capability model and the search environment parameters, with the goal of load balancing of each sensor. The second calculation module is used to determine the constraints based on the search area division results, with the objective of maximizing the incremental increase of the cumulative detection coverage of the area, and to solve the heading of each sensor at each time within the target time period based on the cumulative detection coverage model of the area, so as to obtain the search path planning results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-sensor collaborative search path planning method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-sensor cooperative search path planning method as described in any one of claims 1-7.

Images