Optimized station distribution method and device for passive positioning sensor, and storage medium

Abstract

The invention discloses an optimized station arrangement method and device for a passive positioning sensor and a storage medium, and belongs to the technical field of environment monitoring, and the method comprises the steps: inputting obtained sensor parameters and target information into a pre-optimized sensor station arrangement model, and obtaining an optimal sensor station arrangement position; the optimization method of the sensor station distribution model comprises the following steps: constructing a positioning scene in which a plurality of sensors position a plurality of targets through a passive cross positioning technology; establishing time constraint, energy constraint and station distribution position constraint conditions; based on the positioning scene and the constraint condition, taking a weighted root-mean-square error under a station arrangement formation as a target function, and constructing a sensor station arrangement model; performing iterative optimization on the objective function by using a genetic algorithm to obtain an optimized sensor station distribution model; according to the method, time, energy and station distribution position constraints are constructed, and the station distribution position of the sensor is iteratively optimized by using the genetic algorithm, so that high-precision target positioning is finally realized.

Description

Technical Field

[0001] The present invention relates to an optimized sensor station layout method, device and storage medium for passive positioning sensors, belonging to the technical field of environmental monitoring. Background Art

[0002] In the field of modern monitoring and positioning applications, passive positioning technology has been widely used due to its unique advantages (such as no need for the target to emit signals and strong concealment). In the passive positioning technology system, the layout method of sensors plays a decisive role in positioning accuracy.

[0003] Traditional sensor layout methods, such as triangular layout and rectangular layout, mainly rely on analytical methods. This method can play a certain role in relatively simple and regular environments, and determines the sensor layout through geometric principles. However, when facing complex scenarios, its limitations become prominent. There are often many constraint conditions in complex scenarios, such as the limitations of the sensor's own resources (including hardware performance, storage capacity, etc.), and the differences in target danger levels (higher-precision positioning is required for high-danger-level targets, and stricter requirements are imposed on the sensor layout). Traditional analytical methods are difficult to flexibly and effectively meet these multi-constraint conditions, resulting in a significant reduction in positioning accuracy.

[0004] With the rise of intelligent algorithms, algorithms such as particle swarm optimization are also applied to the optimization of sensor layout. The particle swarm optimization algorithm can optimize the layout scheme to a certain extent by simulating the foraging behavior of bird flocks. However, this algorithm has deficiencies in practical applications. It does not fully consider the joint constraints of time and energy consumption. In actual deployment, the working time of sensors is limited, and energy supply is often a scarce resource. A long-time and high-energy-consuming operation mode will cause the sensors to fail prematurely, affecting the stability and sustainability of the entire positioning system. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art, and provide an optimized sensor station layout method, device and storage medium for passive positioning sensors. By constructing constraints on time, energy and station layout positions, and using a genetic algorithm to iteratively optimize the sensor station layout positions, high-precision target positioning is finally achieved.

[0006] To achieve the above purpose, the present invention is implemented by the following technical solutions:

[0007] In the first aspect, the present invention provides an optimized sensor station layout method for passive positioning sensors, including:

[0008] Input the obtained sensor parameters and target information into a pre-optimized sensor station layout model to obtain the optimal sensor station layout positions;

[0009] Among them, the optimization method of the sensor station layout model includes:

[0010] Construct a positioning scenario where multiple sensors use passive cross - positioning technology to locate multiple targets;

[0011] According to the target danger level, sensor resource constraints, and station - layout area constraints, establish time constraints, energy constraints, and station - layout position constraints;

[0012] Based on the positioning scenario and constraints, use the weighted root - mean - square error in the station - layout formation as the objective function to construct a sensor station - layout model;

[0013] Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station - layout model.

[0014] Furthermore, the sensor parameters include the initial position, moving speed, energy consumption per unit distance, initial carried energy, and additional fixed energy consumption of the sensor; the target information includes: the position, danger level, startup time, and minimum operating distance of the target.

[0015] Furthermore, the time constraint is defined as: in the sensor group assigned to the same target, the arrival time of the sensor that finishes moving and positioning preparation latest does not exceed the startup time to be measured for this target, and its formula is: ; ; where, is the distance from the initial position of the -th sensor to the sensor station - layout position , is the stepping speed of the -th sensor, is the time required for the -th sensor to reach the station - layout position, represents the startup time to be measured for the -th target.

[0016] Furthermore, the energy constraint is defined as: the total energy consumption of the sensor does not exceed its initial carried energy, and its formula is:

[0017] ;

[0018] ;

[0019] where, represents the distance from the initial position to the sensor station - layout position of the -th sensor, is the energy consumption per unit distance for the -th sensor to travel, is the additional fixed energy, is the initial energy carried by the th sensor, represents the total energy consumption of the th sensor.

[0020] Furthermore, the station layout position constraint includes:

[0021] The minimum operating distance constraint between the sensor and the target, the formula is as follows:

[0022] ;

[0023] where, represents the true position of the target, represents the station layout position of the sensor, is the minimum operating distance of the target;

[0024] The minimum spacing constraint between sensors, the formula is as follows:

[0025] ;

[0026] where, = , = represent the station layout positions of two sensors, and both satisfy and , represents the spacing between two sensors, represents the minimum spacing value to be satisfied.

[0027] Furthermore, the objective function is the weighted root mean square error of the multi-objective region, and its formula is:

[0028] ;

[0029] where, is the objective function;

[0030] ;

[0031] where, is the covariance matrix of the cross-location error, ;

[0032] ;

[0033] The covariance matrix of the measurement error is:

[0034] ;

[0035] Among them, let the angular information of each sensor's measurement of the target be , be the variance of the measurement error of the azimuth angle of the th sensor;

[0036] The covariance matrix of the station location error is:

[0037] ;

[0038] ;

[0039] Among them, is the station location error variance, and the angle ;

[0040] The matrix is:

[0041] ;

[0042] ;

[0043] Among them, , is the root mean square error of the positioning of the th target, is the weight coefficient, and satisfies:

[0044] ;

[0045] Among them, is the number of targets.

[0046] Furthermore, the use of the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model includes:

[0047] Randomly generate multiple groups of sensor station layout positions that meet the constraint conditions;

[0048] Gradually optimize the fitness of the sensor station layout positions through selection, crossover, and mutation operations;

[0049] Repeat the iteration until the convergence condition is met, and then output the optimal sensor station layout model.

[0050] In a second aspect, the present invention provides an optimized sensor station layout device for passive positioning sensors, including:

[0051] An optimized station layout module, configured to input the obtained sensor parameters and target information into a pre-optimized sensor station layout model to obtain the optimal sensor station layout position;

[0052] Among them, the optimization method of the sensor station layout model includes:

[0053] Construct a positioning scenario where multiple sensors use passive cross - positioning technology to locate multiple targets;

[0054] According to the target danger level, sensor resource limitations, and station layout area constraints, establish time constraints, energy constraints, and station layout position constraints;

[0055] Based on the positioning scenario and constraints, use the weighted root - mean - square error under the station layout formation as the objective function to construct a sensor station layout model;

[0056] Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model.

[0057] In a third aspect, the present invention provides an electronic device, including:

[0058] A memory for storing computer programs / instructions;

[0059] A processor for executing the computer programs / instructions to implement the steps of the method described in any one of the foregoing.

[0060] In a fourth aspect, the present invention provides a computer - readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method described in any one of the foregoing.

[0061] In a fifth aspect, the present invention provides a computer program product, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, they implement the steps of the method described in any one of the foregoing.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention:

[0063] The present invention provides an optimized station layout method, device, and storage medium for passive positioning sensors. By constructing a multi - constraint optimization model including time, energy, target priority, and sensor spacing, it solves the problem that the ideal station layout strategy cannot be realized due to resource limitations, and significantly improves the feasibility and robustness of the station layout scheme in the actual complex environment. By introducing the weighted root - mean - square error as the fitness function and dynamically adjusting the weight coefficient, it gives priority to ensuring the positioning accuracy of high - risk targets while taking into account the error balance of the overall area, and realizes the dynamic optimization of multi - target positioning performance. Using binary coding combined with roulette wheel selection, single - point crossover, and mutation operations enhances the global search ability and avoids falling into local optima. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a flowchart of an optimized station layout method for passive positioning sensors provided by an embodiment of the present invention;

[0065] Figure 2 It is the flowchart of the genetic algorithm provided by the embodiment of the present invention;

[0066] Figure 3 It is the schematic diagram of the simulation result (positioning error convergence curve) provided by the embodiment of the present invention;

[0067] Figure 4 It is the distribution map of the optimal station layout positions provided by the embodiment of the present invention. Specific implementation manners

[0068] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and cannot be used to limit the protection scope of the present invention.

[0069] Embodiment 1. This embodiment introduces an optimization method for the station layout of passive positioning sensors, including:

[0070] Input the obtained sensor parameters and target information into the pre-optimized sensor station layout model to obtain the optimal sensor station layout positions;

[0071] Among them, the optimization method of the sensor station layout model includes:

[0072] Construct a positioning scenario where multiple sensors use passive cross-positioning technology to locate multiple targets;

[0073] Establish time constraints, energy constraints, and station layout position constraints according to the target danger level, sensor resource limitations, and station layout area constraints;

[0074] Based on the positioning scenario and constraints, use the weighted root mean square error in the station layout formation as the objective function to construct a sensor station layout model;

[0075] Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model.

[0076] As Figure 1 shown, the application process of the optimization method for the station layout of passive positioning sensors provided in this embodiment specifically involves the following steps:

[0077] S1: Construct sensors to locate targets through passive cross-positioning technology to construct a positioning scenario;

[0078] S2: Establish time constraints, energy constraints, and station layout position constraints according to the target danger level, sensor resource limitations, and station layout area constraints, as follows:

[0079] Time Constraint: Among the sensor groups assigned to the same target, the arrival time of the sensor that finishes moving and positioning preparation last shall not be later than the power-on time of the target to be measured. The formula is as follows:

[0080] ;

[0081] ;

[0082] wherein, is the distance from the initial position of the th sensor to the sensor deployment position . is the stepping speed of the th sensor, is the time required for the th sensor to reach the deployment position, represents the power-on time of the th target to be measured.

[0083] Energy Constraint: The total energy consumption of the sensors shall not exceed their initial carried energy. The formula is as follows:

[0084] ;

[0085] ;

[0086] wherein, represents the distance from the initial position to the sensor deployment position of the th sensor, is the energy consumption per unit distance of the th sensor during travel, is the additional fixed energy, is the initial carried energy of the th sensor, represents the total energy consumption of the th sensor.

[0087] Deployment Position Constraint: The sensor group shall meet the minimum operating distance from the target. The formula is as follows:

[0088] ;

[0089] wherein, represents the true position of the target, represents the deployment position of the sensor, is the minimum operating distance of the target.

[0090] The distance between sensors meets the minimum distance. The formula is as follows:

[0091] ;

[0092] Among them, = , = represent the station layout positions of two sensors, and both satisfy and , represents the distance between the two sensors, represents the minimum distance value to be satisfied.

[0093] S3: Using the weighted root mean square error in the station layout formation as the objective function, follow the steps below:

[0094] ;

[0095] ;

[0096] Among them, is the covariance matrix of the cross - location error, .

[0097] ;

[0098] The covariance matrix of the measurement error is:

[0099] ;

[0100] Among them, assume that the angle information of the target measurement by each sensor is , is the variance of the measurement error of the azimuth angle of the th sensor.

[0101] The covariance matrix of the station location error is:

[0102] ;

[0103] ;

[0104] Among them, is the station location error variance, the angle ;

[0105] The matrix is:

[0106] ;

[0107] ;

[0108] Among them, , is the root mean square error of the positioning of the th target, is the weight coefficient, and satisfies:

[0109] ;

[0110] Among them, is the number of targets. By adjusting the weights, different emphases can be achieved during task allocation.

[0111] S4: According to the constraint conditions and the objective function, obtain the optimization function for solving this optimal model, in the following steps:

[0112] ;

[0113] In the above optimization function, represents summing and minimizing the weighted root mean square errors under the given set of sensor position coordinates, is the weight coefficient, which is used to measure the error degree between the predicted value and the true value of the model, is the set of sensor positions in the model. The constraint condition means that the station setup time of the th sensor cannot exceed the target time to be measured , reflects the energy-related constraint, represents that the distance between the th sensor and the th target should be greater than the minimum operating distance , further limits that the minimum distance between different sensors should be greater than , to avoid sensors being too close to each other and affecting the performance of the sensor network. These constraint conditions and the objective function work together to solve the optimal model for the optimal layout of multi-constraint passive positioning sensors based on the genetic algorithm.

[0114] S5: Use the genetic algorithm to iteratively optimize the objective function. The flowchart of the genetic algorithm is as Figure 3 shown, in the following steps:

[0115] Randomly generate multiple groups of sensor coordinates that meet the constraint conditions, use m-bit binary encoding to convert the sensor position coordinates into chromosome genes, and form an initial population; calculate the fitness value of each chromosome gene, and select the station layout plan with high fitness to enter the next generation. The objective function is the weighted root mean square error. The smaller the error, the higher the fitness; generate a new station layout plan by exchanging the genes in the front part of the parental chromosomes, and the crossover probability is p; randomly change some genes in the chromosome and mutate with a probability of n to increase the diversity of the station layout methods; repeat the selection, crossover, and mutation operations until the set number of iterations k or the convergence condition is reached.

[0116] S5: Change the sensor position to the optimal station layout position for output.

[0117] Use the genetic algorithm for the objective function representing the overall positioning accuracy Iteratively calculate to make the objective function Iterate until the required positioning accuracy is reached, or the iteration reaches the required number of iterations. The finally obtained value is the set of the coordinates of the required sensors.

[0118] Experiment:

[0119] Use the genetic algorithm to solve the optimal station layout. The simulation conditions are set as follows: the number of observation stations is 4, the number of targets to be measured is 3, and the station layout area is: , with the unit of km, and the area of the target radiation source is: , with the unit of km, the population size , adopt 20-bit binary encoding to convert the sensor position coordinates into chromosome genes, the crossover probability , the mutation probability , the number of iterations k is 800, and the convergence result of the positioning error is as shown in Figure 3 . It can be seen from the figure that as the number of iterations increases, the overall positioning error in the target area first decreases continuously and then remains unchanged. When the number of iterations of the algorithm is less than 100, the positioning error drops rapidly. Between 100 and 200 times, the positioning error drops slowly. After 200 times, the positioning error remains unchanged, indicating that the algorithm converges when the number of iterations reaches about 200 times, and the positioning error at convergence is 0.2568 km. The positions of the observation stations at the optimal station layout for passive positioning at convergence are as shown in Figure 4 . It can be seen from the figure that the observation stations are distributed at different positions in space at this time, and their coordinates are (27.76153, 48.55391) / km, (24.06548, 31.93893) / km, (36.54703, 21.01536) / km, (47.99835, 26.10071) / km.

[0120] In this embodiment, a genetic algorithm is innovatively introduced. The genetic algorithm draws on the mechanism of "survival of the fittest and elimination of the unfit" in biological evolution, and searches for the optimal solution in a vast solution space by simulating operations such as gene crossover and mutation. The present invention also combines a multi-objective optimization model, comprehensively considering multiple objectives such as positioning accuracy, time consumption, energy consumption, and other complex constraint conditions. In this way, not only can it effectively cope with multiple constraint conditions in complex scenarios, but also when optimizing the station layout, it can take into account key factors such as time and energy consumption, fundamentally solving the problems of traditional methods and existing intelligent algorithms in sensor station layout, and significantly improving the practicability and reliability of passive positioning technology.

[0121] Embodiment 2. This embodiment provides an optimization device for passive positioning sensors, including:

[0122] An optimization station layout module, configured to input the obtained sensor parameters and target information into a pre-optimized sensor station layout model to obtain the optimal sensor station layout position;

[0123] Among them, the optimization method of the sensor station layout model includes:

[0124] Construct a positioning scenario where multiple sensors use passive cross-positioning technology to locate multiple targets;

[0125] According to the target danger level, sensor resource limitations, and station layout area constraints, establish time constraints, energy constraints, and station layout position constraints;

[0126] Based on the positioning scenario and constraint conditions, use the weighted root mean square error in the station layout formation as the objective function to construct a sensor station layout model;

[0127] Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model.

[0128] For the specific function implementation of the above modules, refer to the relevant content in the method of Embodiment 1, which will not be elaborated here.

[0129] Embodiment 3. This embodiment provides an electronic device, including:

[0130] A memory, configured to store computer programs / instructions;

[0131] A processor, configured to execute the computer programs / instructions to implement the steps of the method described in any one of the foregoing.

[0132] Embodiment 4. This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method described in any one of Embodiment 1.

[0133] Example 5. This example provides a computer program product, including computer programs / instructions, which, when executed by a processor, implement the steps of the method described in any one of Example 1.

[0134] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

[0135] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as methods, systems, or computer program products. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0137] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than limit the scope of its protection. Although the present disclosure has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present disclosure, various changes, modifications or equivalent substitutions can still be made to the specific implementation manners of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending for publication.

Claims

1. An optimized station layout method for passive positioning sensors, characterized in that Including: Input the obtained sensor parameters and target information into a pre-optimized sensor station layout model to obtain the optimal sensor station layout positions; Among them, the optimization method of the sensor station layout model includes: Construct a positioning scenario where multiple sensors use passive cross-location technology to locate multiple targets; According to the target danger level, sensor resource limitations, and station layout area constraints, establish time constraints, energy constraints, and station layout position constraints; Based on the positioning scenario and constraints, use the weighted root mean square error in the station layout formation as the objective function to construct a sensor station layout model; Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model.

2. The optimized station layout method for the passive positioning sensor according to claim 1, wherein The sensor parameters include the initial position, moving speed, energy consumption per unit distance, initial carried energy, and additional fixed energy consumption of the sensor; the target information includes: the position, danger level, power-on time, and minimum operating distance of the target.

3. The optimized station layout method for the passive positioning sensor according to claim 2, characterized in that The time constraint is defined as: among the sensor groups assigned to the same target, the arrival time of the sensor that finishes moving and positioning preparation last does not exceed the power-on time to be measured of this target, and its formula is: ； ； Among them, is the distance of the th sensor from the initial position to the sensor deployment position . is the stepping speed of the th sensor, is the time required for the th sensor to reach the deployment position, represents the power-on time of the th target to be measured.

4. The optimized station layout method for the passive positioning sensor according to claim 3, characterized in that The energy constraint is defined as: the total energy consumption of the sensor does not exceed its initial carried energy, and its formula is: ； ； Among them, represents the distance from the initial position of the th sensor to the sensor deployment position, is the energy consumption per unit distance traveled by the th sensor, is the additional fixed energy, is the initial energy carried by the th sensor, represents the total energy consumption of the th sensor.

5. The optimized station layout method for the passive positioning sensor according to claim 4, characterized in that, The station layout position constraints include: The minimum operating distance constraint between the sensor and the target, and the formula is as follows: ； Among them, represents the target true position, represents the sensor layout position, is the minimum action distance of the target; The minimum spacing constraint between sensors, and the formula is as follows: ； Among them, = , = represent the station positions of two sensors, and both satisfy and , represents the distance between the two sensors, represents the minimum distance value to be satisfied.

6. The optimized station layout method for the passive positioning sensor according to claim 5, characterized in that, The objective function is the weighted root mean square error of a multi-target area, and its formula is: ； Among them, is the objective function; ； wherein, is the covariance matrix of the cross-positioning error, ; ； Covariance matrix of measurement error is as follows: ； Among them, let the angle information of each sensor's measurement of the target be , be the variance of the measurement error of the azimuth angle of the th sensor; Covariance matrix of site error is as follows: ； ； Among them, is the variance of the station location error, and the angle ; Matrix is as follows: ； ； Among them, , is the root mean square error of the positioning of the th target, is the weight coefficient, and satisfies: ； Among them, is the target number.

7. The optimized station layout method for the passive positioning sensor according to claim 1, characterized in that The use of the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model includes: Randomly generate multiple groups of sensor station layout positions that meet the constraints; Gradually optimize the fitness of the sensor station layout positions through selection, crossover, and mutation operations; Repeat the iteration until the convergence condition is met, and then output the optimal sensor station layout model.

8. An optimized station layout device for a passive positioning sensor, characterized in that, Including: An optimization station layout module for inputting the obtained sensor parameters and target information into a pre-optimized sensor station layout model to obtain the optimal sensor station layout positions; Among them, the optimization method of the sensor station layout model includes: Construct a positioning scenario where multiple sensors use passive cross-location technology to locate multiple targets; According to the target danger level, sensor resource limitations, and station layout area constraints, establish time constraints, energy constraints, and station layout position constraints; Based on the positioning scenario and constraints, use the weighted root mean square error in the station layout formation as the objective function to construct a sensor station layout model; Use the genetic algorithm to iteratively optimize the objective function to obtain an optimized sensor station layout model.

9. An electronic device, characterized in that, Including: A memory for storing computer programs / instructions; A processor for executing the computer programs / instructions to implement the steps of the method described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the method described in any one of claims 1-7.

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