CN116709240B - Hierarchical sensor deployment method based on whale optimization algorithm - Google Patents
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Abstract
The application provides a layering sensor deployment method based on a whale optimization algorithm, which comprises the steps of initializing parameters of a sensor group, initializing the number of sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information; optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the steps of a whale optimizing algorithm; optimizing and distributing the dynamic sensors in the sensor group with a finer objective function according to the environment and situation of the current battlefield; in the method provided by the application, the sensor network consists of a static sensor and a dynamic sensor. The static sensor is higher in level, all dynamic sensor positions of the detected area can be obtained, and the dynamic sensor has the moving capability and can accurately move to the optimized position. The method can still accurately acquire the target information of the high maneuvering target under the complex battlefield environment.
Description
Technical Field
The invention belongs to the field of sensor network deployment, and particularly relates to a layering sensor deployment method based on a whale optimization algorithm.
Background
In a networked collaborative fire control system, the task of a scout sensing network is to collect and preprocess various information of a battlefield space and fuse the obtained information into a consistent battlefield situation. At present, related researches on a sensor distribution method mostly adopt artificial intelligent optimization algorithms, such as genetic algorithms, ant colony algorithms and the like, which are proposed by maximizing effective coverage rate and efficiency factors of sensor targets as objective functions, and the algorithms are all based on the efficiency maximization to realize intelligent distribution of the sensors.
When a high maneuvering target exists in a battlefield, the battlefield situation changes quickly, and the objective function value also changes greatly. To accommodate such a rapidly changing environment, the sensor position must be constantly optimized. Based on different characteristics and purposes of various sensors, the positions of all sensors are continuously optimized, the efficiency is low, and unnecessary resource waste can be caused.
Disclosure of Invention
The invention aims to solve the technical problems of low sensor deployment optimization efficiency and resource waste caused by continuously changing battlefield situations.
The technical scheme of the invention is that the layering sensor deployment method based on the whale optimization algorithm comprises the following steps:
step 1, initializing parameters of a sensor group, the number of the sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information.
And 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of a whale optimizing algorithm.
And 3, optimizing and distributing the dynamic sensors in the sensor group by using a multi-sensor optimizing and deploying model according to the environment and situation of the current battlefield.
And step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
Optionally, the battlefield network model is determined by the following method:
assuming that the whole sensor detection area is a two-dimensional plane and is digitized into an L×M grid;
the sensor can be deployed in any point in the grid, and the whole detection area is divided into squares;
the cell covered by each sensor is determined from the coverage of the sensor.
In the battlefield station distribution constraint matrix, an area incapable of station distribution is defined as A unable The area where stations can be located is defined as A able Battlefield geographic constraintsExpressed as:
b ij ∈A able ≠A unable 。
optionally, the indexes met by the objective function include core area coverage, anti-stealth capability, co-channel interference avoidance capability, anti-interference capability and resource utilization capability.
Optionally, the index of the objective function is defined as:
(1) Core area coverage: the core area coverage coefficient beta is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: the anti-stealth capability coefficient alpha is defined as a responsibility detection area and a responsibility area A actually obtained by a reconnaissance system r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing the frequency range of sensor i, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the anti-interference capacity coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area and S i The detection area of the stealth target is the i-th radar.
(5) Resource utilization capability: the resource utilization coefficient eta is defined as the coverage area and the core area A of the sensor below 3 parts c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
optionally, optimizing the deployment of the dynamic sensors in the sensor group with a finer objective function according to the environment and situation of the current battlefield includes:
by [ (x) 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n )]Represents whale individuals, wherein (x i ,y i ) The coordinate position of the ith sensor is the coordinate position of the ith sensor, the sensors are positioned in the grid, and n is the number of the sensors;
deploying the sensor according to situation change (for example, a certain area has an important target and needs important reconnaissance, or the environment of a certain area has change, and the sensor cannot be deployed or needs evacuation); the situation change in the battlefield comprises the occurrence of an important target in a target area, and the important reconnaissance is carried out; or the environment of the target area changes, and the sensor cannot be deployed or needs to be evacuated;
the whale optimizing algorithm comprises the following steps:
step 31, initializing parameters of a sensor group and battlefield environmental parameters, wherein the parameters of the sensor group comprise the number of sensors, the maximum detection radius of each sensor, the detection power of each sensor and the detection probability; the battlefield environmental parameters comprise an area of an area to be detected in a battlefield and a battlefield layout constraint matrix;
step 32, initializing parameters of a whale optimization algorithm, wherein the parameters of the whale optimization algorithm comprise whale number and maximum iteration times;
step 33, calculating population fitness according to the objective function, and finding out the current global optimal solution position of the population;
step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following method:
step 35, updating the parameter A, if |A| <1, updating the whale position according to formula (1), otherwise updating according to formula (2).
Step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
The invention has the following technical advantages
(1) Compared with a sensor optimal deployment method for uniformly and optimally deploying all sensors, the invention provides a layering sensor deployment method based on a whale optimization algorithm, wherein the sensors are classified into two types of static sensors and dynamic sensors, the static sensors are higher in level and can control the dynamic sensors in a range, and a more reasonable sensor deployment strategy can be provided. The method provided by the invention can save resources and provide faster optimized deployment speed.
(2) The layering sensor deployment method based on the whale optimization algorithm provided by the invention uses the whale optimization algorithm when optimizing the sensor position. Through experimental tests, compared with the common optimization algorithm, the optimization algorithm has higher iteration speed while providing better optimization precision.
Drawings
FIG. 1 is a flowchart of a layering sensor deployment method based on whale optimization algorithm.
FIG. 2 is a sensor optimization deployment iteration process of the PSO algorithm in an embodiment of the present invention.
FIG. 3 is a sensor optimization deployment iteration process of the GA algorithm in an embodiment of the present invention.
FIG. 4 is a sensor optimization deployment iteration process of the GA-PSO algorithm in an embodiment of the present invention.
FIG. 5 is a sensor optimization deployment iteration process of the WOA algorithm in an embodiment of the invention.
Detailed Description
Referring to fig. 1, the layering sensor deployment method based on whale optimization algorithm of the invention comprises the following steps:
step 1, initializing parameters of a sensor group, the number of sensors, the maximum detection radius of each sensor, the detection power and the detection probability of each sensor, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information.
And 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of a whale optimizing algorithm.
And 3, optimizing and distributing the dynamic sensors in the sensor group by using a multi-sensor optimizing and deploying model according to the environment and situation of the current battlefield.
And step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
Further, the battlefield sensor deployment model in step 1 specifically includes: assuming that the whole detection area of the sensor is a two-dimensional plane and is digitized into an L×M grid, the sensor can be deployed in any point in the grid, the whole detection area is divided into squares, and the unit cell covered by each sensor can be calculated according to the coverage area of the sensor.
Assume that the sensors have different perceived radii r s And an indeterminate perception range delta r s (delta < 1), grid center point coordinates T (x i ,y i ) And a certain sensor S j (x sj ,y sj ) The distance between them isIn the binary sensor coverage model, the sensing probability of the sensor node to the grid area T is:
where λ is the sensor sensing capability index. When the sensing probability of the grid area is larger than a certain threshold value, the grid can be sensed by the sensor node, and the area is an effective coverage point; otherwise, the area is considered to be not perceivable by the sensor node. The sensing probability of a single sensor on a certain area is generally smaller than 1, which means that a plurality of sensors need to be adopted for simultaneous detection in the detection process so as to improve the target sensing probability.
In the grid, an area where a station cannot be located is defined as A unable The area where stations can be located is defined as A able Then the battlefield geographic constraint can be expressed as
b ij ∈A able ≠A unable
In addition, in order to realize the functions of information interaction, collaborative detection and mutual blind compensation between the sensors, a certain distance limit is needed between any two sensors, and the sensors cannot be too large or too small. The distance between two adjacent sensors may be constrained by:
|r i -r j |≤d(S i ,S j )≤r i +r j
further, the objective function in step 2 and step 3 is defined as:
in order to realize tactical requirements of anti-interference, anti-stealth and the like of the sensor network, the following principles should be followed when the sensor is deployed:
(1) Core area coverage: in practical application, some areas are important scout areas and should be covered, so the core area coverage coefficient β is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: based on the existing radar resources, the detection range of a typical stealth target is as large as possible, and the anti-stealth capability coefficient alpha is defined as a responsibility detection area and a responsibility area A actually obtained by a reconnaissance system r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference capability: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing sensor iFrequency range, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the proper airspace coverage redundancy can have stronger survivability when suffering from the interference of the environment or enemy, but excessive redundancy can cause the waste of resources, and the anti-interference capability coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area, S i The detection area of the stealth target is the i-th radar.
(5) Resource utilization capability: normally, overlapping of 2 sensor coverage areas is considered reasonable, overlapping of 3 or more sensor coverage areas is considered wasteful, and therefore, the resource utilization coefficient η is defined as the coverage area and core area a of the 3 or less sensors c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
step 3, in the whale optimization algorithm distributed towards the sensor, using [ (x) 1 ,y 1 ),(x 2 ,y 2 ),...,(x n ,y n )]Represents whale individuals, wherein (x i ,y i ) For the ith sensorIs located in the grid, n being the number of sensors. The sensor is deployed according to situation changes in the battlefield (for example, a certain area has an important target, important reconnaissance is needed, or the environment of a certain area has a change, and the sensor cannot be deployed or needs to be evacuated). The following are the specific algorithm steps of the whale optimization algorithm for sensor-oriented distribution:
step 31, initializing parameters of a sensor group and battlefield environmental parameters, wherein the parameters of the sensor group comprise the number of sensors, the maximum detection radius of each sensor, the detection power of each sensor and the detection probability; the battlefield environmental parameters include the area of the area in the battlefield to be reconnaissad and the battlefield deployment constraint matrix.
Step 32, initializing parameters of a whale optimization algorithm, wherein the parameters of the whale optimization algorithm comprise whale number and maximum iteration number.
And step 33, calculating the population fitness according to the objective function, and finding out the current global optimal solution position of the population.
Step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following formula.
Step 35, updating the parameter A, if |A| <1, updating the whale position according to formula (1), otherwise updating according to formula (2).
Step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
Examples
With reference to fig. 1, the hierarchical sensor deployment method for high maneuvering target detection in a complex land battlefield of the present invention comprises the following steps:
(1) Parameters of the sensor group are initialized, including the number of sensors, the maximum detection radius of each sensor, the detection power of each sensor, the detection probability, the area of an area to be detected in a battlefield and a battlefield layout constraint matrix.
(2) Optimizing and deploying the static sensors in the sensor group by taking the maximum effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the steps of the whale optimizing algorithm.
(3) The dynamic sensors in the sensor group are optimally distributed according to the environment and situation of the current battlefield with finer objective functions.
(4) Detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
In this embodiment, the reconnaissance area of the target setting battlefield is 100km×100km, and the core area is 20km×20km. The number of the sensors is limited, 2 static sensors are adopted for station arrangement, the maximum detection radius (km) of the sensors is respectively set to be 20km and 25km, the detection probability is 0.8, and the weight coefficients of the objective functions are set to be 1,0 and 0. The total of 8 parts of the dynamic sensor is provided with maximum detection radiuses (km) of 5km, 4km, 3km and 3.5km, detection frequencies of [5,7.5], [0.3,0.6], [7,8], [0.5,1.5] and detection probability of 0.8.
The number of the whale population is initialized to be 50, the maximum iteration number is 200, in the embodiment, the PSO, GA, GA-PSO algorithm and the WOA algorithm are respectively adopted to perform 10 rounds of simulation and average, simulation results are shown in a table 1, and the WOA algorithm has the highest iteration speed and better coverage rate when having the same PSO algorithm and the GA-PSO algorithm close to the optimizing precision.
TABLE 1 simulation results
The station situation diagrams drawn based on the optimal values are shown in fig. 2 to 5. The optimal positions of the 4 sensors obtained by the station distribution optimization based on the WOA algorithm are obviously improved compared with the station distribution positions obtained by other 3 algorithms, so that the sensors are better prevented from being arranged in the area where the station cannot be distributed, and the station distribution principle is better followed. When the situation changes, the key area needs to be covered completely, and the coverage rate of the key area can almost reach one hundred percent; when interference occurs, the re-station will make appropriate redundancy of detection ranges for the interfering region.
The invention has the following technical advantages
(1) Compared with a sensor optimal deployment method for uniformly and optimally deploying all sensors, the invention provides a layering sensor deployment method based on a whale optimization algorithm, wherein the sensors are classified into two types of static sensors and dynamic sensors, the static sensors are higher in level and can control the dynamic sensors in a range, and a more reasonable sensor deployment strategy can be provided. The method provided by the invention can save resources and provide faster optimized deployment speed.
(2) The layering sensor deployment method based on the whale optimization algorithm provided by the invention uses the whale optimization algorithm when optimizing the sensor position. Through experimental tests, compared with the common optimization algorithm, the optimization algorithm has higher iteration speed while providing better optimization precision.
Claims (2)
1. The layering sensor deployment method based on the whale optimization algorithm is characterized by comprising the following steps of:
step 1, initializing parameters of a sensor group, the number of the sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information;
step 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of whale optimizing algorithm;
step 3, optimizing and distributing the dynamic sensors in the sensor group by using a multi-sensor optimizing and deploying model according to the environment and situation of the current battlefield;
step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, executing the step 3;
the battlefield network model is determined by the following method:
assuming that the whole sensor detection area is a two-dimensional plane and is digitized into an L×M grid;
the sensor can be deployed in any point in the grid, and the whole detection area is divided into squares;
the cell covered by each sensor is determined according to the coverage of the sensor;
in the battlefield station distribution constraint matrix, an area incapable of station distribution is defined as A unable The area where stations can be located is defined as A able The battlefield geographic constraint is expressed as:
b ij ∈A able ≠A unable
the indexes met by the objective function comprise core area coverage, anti-stealth capability, co-channel interference avoidance capability, anti-interference capability and resource utilization capability;
the index of the objective function is defined as:
(1) Core area coverage: the core area coverage coefficient beta is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: the anti-stealth capability coefficient alpha is defined as a responsibility detection area and a responsibility area A actually obtained by a reconnaissance system r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing the frequency range of sensor i, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the anti-interference capacity coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area and S i The detection area of the stealth target is the i-th radar;
(5) Resource utilization capability: the resource utilization coefficient eta is defined as the coverage area and the core area A of the sensor below 3 parts c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
2. the method of claim 1, wherein optimizing the deployment of the dynamic sensors in the sensor group with a finer objective function based on the environment and situation of the current battlefield, comprises:
by [ (x) 1 ,y 1 ),(x 2 ,y 2 ),…,(x n ,y n ) Represents whale individuals, wherein (x i ,y i ) The coordinate position of the ith sensor is the coordinate position of the ith sensor, the sensors are positioned in the grid, and n is the number of the sensors;
deploying the sensor according to situation changes in the battlefield; the situation change in the battlefield comprises the occurrence of an important target in a target area, and the important reconnaissance is carried out; or the environment of the target area changes, and the sensor cannot be deployed or needs to be evacuated;
the whale optimizing algorithm comprises the following steps:
step 31, initializing parameters of a sensor group and battlefield environmental parameters, wherein the parameters of the sensor group comprise the number of sensors, the maximum detection radius of each sensor, the detection power of each sensor and the detection probability; the battlefield environmental parameters comprise an area of an area to be detected in a battlefield and a battlefield layout constraint matrix;
step 32, initializing parameters of a whale optimization algorithm, wherein the parameters of the whale optimization algorithm comprise whale number and maximum iteration times;
step 33, calculating population fitness according to the objective function, and finding out the current global optimal solution position of the population;
step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following method:
step 35, updating the parameter A, if the value of the parameter A is <1, updating the whale position according to the formula (1), otherwise updating the whale position according to the formula (2);
step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
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