CN113238185B - Fibonacci scatter search-based non-cooperative target positioning method and system - Google Patents
Fibonacci scatter search-based non-cooperative target positioning method and system Download PDFInfo
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Abstract
The invention discloses a non-cooperative target positioning method and a non-cooperative target positioning system based on Fibonacci scatter search, wherein the method comprises the following steps: the method comprises the following steps that firstly, sensor nodes in a sensor network of a non-cooperative target area are cooperatively positioned to obtain position information of each sensor node; acquiring the sensing state of each sensor node on a non-cooperative target area signal and determining each boundary sensor node in the sensor network; step three, determining a non-cooperative target mapping area according to the position information of each boundary sensor node; fourthly, constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes; fifthly, Fibonacci and scatter iterative search is carried out on the non-cooperative target mapping area to obtain a search point with the minimum fitness value, and position information corresponding to the search point is used as position information of the non-cooperative target. The invention can realize the positioning of the non-cooperative target under the condition of no distance measurement.
Description
Technical Field
The invention belongs to the technical field of network positioning, and particularly relates to a non-cooperative target positioning method and system based on Fibonacci scatter search.
Background
With the continuous development of communication technology, different types of communication equipment are emerging continuously, in order to realize the management and control of related equipment, a non-cooperative signal source needs to be positioned, and the positioning is helpful for managers to make reasonable management schemes, such as the supervision of illegal radio stations and the tracking of illegal aircrafts; in the field of social stability maintenance, the positioning result of a non-cooperative target can assist a dry police to accurately know the state of the riot terrorist molecule. The positioning method is mainly divided into coarse-grained positioning and fine-grained positioning according to the positioning accuracy. The anchor nodes capable of realizing self-positioning are mainly divided into positioning based on the anchor nodes and positioning without the anchor nodes according to whether the anchor nodes are deployed in the network or not. And according to whether centralized operation is needed in the positioning process, the positioning is divided into centralized positioning and distributed positioning. The positioning method is divided into positioning based on ranging and positioning without ranging according to whether distance or angle information between nodes is needed in the positioning process. Among them, the more common classification methods are a location method based on ranging and a location method without ranging. The positioning method based on ranging needs to use absolute distance estimation information or Angle estimation information between nodes in the positioning process, first estimate distance information by measuring some physical characteristics of target node signals, and relevant physical attributes mainly include Received Signal Strength Indicator (RSSI), Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA), and the like, and then achieve target positioning using a relevant technology, typical technologies mainly include geometric technology, Multi-Dimensional Scaling (MDS) analysis, and random distance Embedding (SPE), and the like. The positioning method based on the distance measurement generally puts certain requirements on hardware of the monitoring node, the monitoring node is required to be provided with a related component capable of realizing the distance measurement, and the positioning method is difficult to realize under the condition that the monitoring node is not provided with the related component.
The positioning method without distance measurement can realize positioning only by communication information between nodes, and does not require a monitoring node to be provided with a specific component in the positioning process. Typical algorithms mainly include a Centroid Localization (CL) Algorithm, a Weighted Centroid Localization (WCL) Algorithm, a Minimum Enclosing Rectangle Center (mer) Algorithm, a Minimum Enclosing circle Center (mer) Algorithm, a VFIL (Virtual Force Iterative Localization) Algorithm, a positioning Algorithm based on Gravity Search (GSA), etc., a DV-Hop Algorithm, and an ap (application performance Point-In-triangle Test, ap) Algorithm, etc. Both DV-Hop and APIT require the target node to exchange information with the known coordinate node, so that the method is not suitable for non-cooperative positioning.
In summary, although the existing positioning method without ranging can realize the positioning of the non-cooperative signal source under the condition that the monitoring node is not equipped with a specific physical component, the problems of low positioning accuracy and sensitivity to node density generally exist, and although the positioning method based on Gravity Search (GSA) has a reduced sensitivity to the monitoring node density, the positioning method based on GSA still has a space for improving the positioning accuracy due to the lack of the GSA algorithm in the aspect of global optimization.
Disclosure of Invention
One of the objectives of the present invention is to provide a non-cooperative target positioning method based on fibonacci scatter search, which can position a non-cooperative target without measuring distance.
The second purpose of the invention is to provide a non-cooperative target positioning system based on Fibonacci scatter search.
In order to achieve one of the purposes, the invention adopts the following technical scheme:
a non-cooperative target positioning method based on Fibonacci scatter search comprises the following steps:
the method comprises the following steps that firstly, sensor nodes in a sensor network of a non-cooperative target area are cooperatively positioned to obtain position information of each sensor node;
acquiring the sensing state of each sensor node on a non-cooperative target area signal and determining each boundary sensor node in the sensor network;
step three, determining a non-cooperative target mapping area according to the position information of each boundary sensor node;
fourthly, constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes;
fifthly, Fibonacci and scatter iterative search is carried out on the non-cooperative target mapping area to obtain a search point with the minimum fitness value, and position information corresponding to the search point is used as position information of the non-cooperative target.
Further, in the step one, the specific implementation process of the cooperative positioning is as follows:
101, acquiring neighbor sensor nodes of each sensor node in a sensor network of a non-cooperative target area, and constructing a local network corresponding to each sensor node;
step 102, taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
103, selecting a secondary center node from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map;
step 104, stitching the initial local map and the secondary local map to obtain a stitched map;
step 105, judging whether the stitching map covers all the sensor nodes, if so, entering step 106; if not, the combined map is assigned to the initial local map, and the step 103 is returned;
106, calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map;
step 107, obtaining a distance information matrix between sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
108, performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and step 109, determining the position information of each sensor node according to the characteristic vector matrix of each local network.
Further, in step 103, the secondary local map meets the following requirements:
the number of the sensor nodes in the intersection between the secondary local map and the initial local map is not less than 3; the number of sensor nodes in the difference set between the secondary local map and the initial local map is the largest.
Further, in step four, the fitness function is:
therein, fitiA fitness function for the ith search point; dijThe distance between the ith search point and the jth boundary sensor node is calculated;
the average value of the distances between the ith search point and all the boundary sensor nodes is obtained; (x)j，yj) The position coordinates of the jth boundary sensor node are obtained; (x)i，yi) Position coordinates of the ith search point; j is 1, 2, …, NB，NBIs the number of boundary sensor nodes.
Further, in step five, the process of fibonacci scatter iterative search is:
step 501, setting an initial value of a Fibonacci scatter layer sequence number p as 1;
502, calculating the search points of each layer of Fibonacci scatter;
step 503, randomly selecting F from the non-cooperative target mapping areapThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
step 504, pairing each first global search point and each second global search point one by one;
step 505, searching points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
step 506, calculating moderate values of the first global search point, the second global search point and the third global search point;
step 507, selecting a search point corresponding to the minimum fitness value as a first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
step 508, according to the search points FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
509, calculating fitness values of the second local search point and the third local search point and sequencing all the search points according to the fitness values from small to large;
step 510, judging whether p is more than or equal to Fibonacci scatter depth NbIf yes, go to step 511; if not, go to step 513;
step 511, selecting the top F from the sorted search pointsp+1The search points form a new first global search point set; and fromRandomly selecting F from the rest non-cooperative target mapping areasp+1The global search points form a new second global search point set;
step 512, making p equal to p +1, and returning to step 504;
step 513, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target.
Further, in step 502, the search point number of the fibonacci scatters of each layer is calculated according to the following formula:
further, a third global search point and a third local search point are calculated according to the following formulas:
wherein, VCIs a dividing point; vAAnd VBRespectively a first search point and a second search point; fpAnd Fp+1Search points of the p-th layer and the p + 1-th layer of Fibonacci scatter are respectively;
when the first search point and the second search point are respectively a first global search point and a second global search point, the division point is a third global search point; when the first search point and the second search point are the first local search point and the second local search point, respectively, the division point is a third local search point.
In order to achieve the second purpose, the invention adopts the following technical scheme:
a non-cooperative target location system based on fibonacci scatter searching, the non-cooperative target location system comprising:
the cooperative positioning module is used for performing cooperative positioning on the sensor nodes in the sensor network of the non-cooperative target area to obtain the position information of each sensor node;
the acquisition module is used for acquiring the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network;
the determining module is used for determining a non-cooperative target mapping area according to the position information of each boundary sensor node;
the construction module is used for constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes;
and the iterative search module is used for carrying out Fibonacci and scatter iterative search on the non-cooperative target mapping area to obtain a search point with the minimum fitness value and taking the position information corresponding to the search point as the position information of the non-cooperative target.
Further, the cooperative positioning module comprises:
the construction submodule is used for acquiring neighbor sensor nodes of each sensor node in the sensor network of the non-cooperative target area and constructing a local network corresponding to each sensor node;
the initial local map sub-module is used for taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
the secondary local map sub-module is used for selecting secondary central nodes from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map;
the stitching submodule is used for stitching the initial local map and the secondary local map to obtain a stitched map;
the first judgment submodule is used for judging whether the stitching map covers all the sensor nodes, if so, the stitching map is transmitted to the first calculation submodule; if not, giving the combined map to the initial local map and transmitting the combined map to the secondary local map sub-module;
the first calculation submodule is used for calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map;
the information matrix submodule is used for obtaining a distance information matrix between the sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
the characteristic decomposition submodule is used for performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and the determining submodule is used for determining the position information of each sensor node according to the characteristic vector matrix of each local network.
Further, the iterative search module comprises:
the initial submodule is used for setting the initial value of the Fibonacci scatter layer sequence number p to be 1;
the second calculation submodule is used for calculating the search points of each layer of Fibonacci scatter;
a first selection submodule for randomly selecting F from the non-cooperative target mapping regionpThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
the matching submodule is used for matching each first global search point with each second global search point one by one;
a first division submodule for dividing the search points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
the third calculation submodule is used for calculating the moderate value of each first global search point, each second global search point and each third global search point;
the second selection submodule is used for selecting the search point corresponding to the minimum fitness value as the first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
a second division submodule for dividing the search point number FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
the sorting submodule is used for calculating the fitness values of the second local search point and the third local search point and sorting all the search points according to the fitness values from small to large;
a second judgment submodule for judging whether p is greater than or equal to the Fibonacci scatter depth NbIf yes, the sorted local search points are sent to a third selection submodule; if not, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target;
a third selection submodule for selecting a front F from the sorted search pointsp+1The search points form a new first global search point set; and randomly selecting F from the rest non-cooperative target mapping areasp+1The global search points form a new second global search point set; let p be p +1 and give the pairing submodule.
The invention has the beneficial effects that:
the method comprises the steps of performing cooperative positioning on sensor nodes in a sensor network of a non-cooperative target area to obtain position information of each sensor node; acquiring the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network; determining a non-cooperative target mapping area according to the position information of each boundary sensor node; fibonacci and scatter iterative search is carried out on the non-cooperative target mapping area through a fitness function, so that non-cooperative target positioning under the condition of no distance measurement is realized; according to the method, Fibonacci and scattered-branch iterative search is adopted, newly generated search points each time comprise a global random point in an optimization interval and a local point in a Fibonacci and scattered-branch structure, the search points of each layer of Fibonacci and scattered-branch grow on the basis of the optimal search points of the previous layer, the optimality of part of the search points is kept, the global randomness is introduced into the search points, and the global optimization and convergence evolution capabilities are greatly improved.
Drawings
FIG. 1 is a schematic diagram of a non-cooperative target positioning process based on Fibonacci scatter search according to the present invention;
FIG. 2 is a schematic diagram of a sensor network connectivity state;
FIG. 3 is a schematic diagram illustrating classification of sensor nodes in a sensor network;
FIGS. 4-1, 4-2, and 4-3 are schematic diagrams of a mapping region construction process;
FIG. 5 is a schematic diagram of a basic structure of Fibonacci scatter search;
FIG. 6 is a schematic diagram of the global search principle of Fibonacci scatter search;
FIG. 7 is a schematic diagram of a local optimization principle of Fibonacci scatter search;
8-1, 8-2, 8-3, and 8-4 are diagrams illustrating a non-cooperative object location process according to an embodiment.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The embodiment provides a non-cooperative target positioning method based on fibonacci scatter search, and referring to fig. 1, the non-cooperative target positioning method includes the following steps:
and S1, performing cooperative positioning on the sensor nodes in the sensor network of the non-cooperative target area to obtain the position information of each sensor node.
In this embodiment, a certain number of sensors may be deployed randomly in a non-cooperative target area by using manual deployment or aircraft broadcast, and as shown in fig. 2, the specific implementation process of cooperative positioning is as follows:
step 101, acquiring neighbor sensor nodes of each sensor node in a sensor network of a non-cooperative target area, and constructing a local network corresponding to each sensor node.
Each sensor in the sensor network and the neighbor nodes around the sensor network jointly form a local network.
Step 102, taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
in the embodiment, the sensor with the largest value (i.e., the largest number of neighbor sensor nodes) in the whole sensor network is selected as the initial central node, and the local network formed by the initial central node and the neighbor nodes thereof is used as the local map.
103, selecting a secondary center node from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map.
The selection principle of the secondary center node in this embodiment includes two principles: firstly, the number of intersection nodes between a local network formed by the node and neighbor nodes thereof and a formed local map is not less than 3; secondly, the difference set between the local network formed by the node and the neighbor nodes thereof and the local map comprises the most nodes, namely the secondary local map of the embodiment meets the following requirements:
the number of the sensor nodes in the intersection between the secondary local map and the initial local map is not less than 3; the number of sensor nodes in the difference set between the secondary local map and the initial local map is the largest.
Step 104, stitching the initial local map and the secondary local map to obtain a stitched map;
in the embodiment, the coordinates of the sensor in the local network are converted to be under the same coordinate system of the local map through operations such as translation, rotation, mirroring and the like, that is, the local network is stitched into the local map, that is, the coordinates of the sensor node in the secondary local map are converted to be on the coordinate system of the initial local map after translation, rotation and mirroring.
Step 105, judging whether the stitching map covers all the sensor nodes, if so, entering step 106; if not, the combined map is given to the initial local map, and the step 103 is returned.
And 106, calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map.
Step 107, obtaining a distance information matrix between sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
108, performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and step 109, determining the position information of each sensor node according to the characteristic vector matrix of each local network.
S2, obtaining the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network.
And S3, determining a non-cooperative target mapping area according to the position information of each boundary sensor node.
Sensor nodes in a sensor network are classified into four categories: the schematic diagram of the positions of the different types of sensors is shown in fig. 3. Sensing sensor nodes which are positioned in the signal coverage range of the communication equipment, can sense the existence of the communication equipment and form a sensing sensor node set VSAnd (4) showing. And the boundary sensor nodes can sense signals of non-cooperative targets, but are far away from the communication equipment and basically located in the marginal area of the coverage range of the cooperative targets, so that the sensed signal strength is weak. Set of boundary sensor nodes uses VBIt is shown that the results, obviously,
the set of the non-sensing sensor nodes which are out of the coverage range of the communication equipment signal uses VUAnd (4) showing. Mapping sensor nodes, wherein the nodes usually have communication links with boundary sensor nodes but do not sense signals of communication equipment by themselves, and the set formed by V is usedMIt is shown that the results, obviously,
sensing of a non-cooperative target coverage area is achieved through a signal detection module and a mapping module which are equipped with the sensor, a boundary sensor node set is obtained, and a mapping area is further constructed, referring to fig. 3.
As shown in fig. 4-1, after the sensor node senses the signal of the target device, the sensor node will inform the neighboring nodes in a broadcast manner that the neighboring nodes sense the signal of the communication device, and the broadcast information mainly includes the ID number and the location information of the sensor itself. And the neighbor nodes which receive the perception information notification but do not perceive the communication equipment signals are used as mapping nodes to form a mapping group, each mapping node constructs a local group formed by perception sensor nodes, and surrounding perception nodes are added into the local group.
Fig. 4-2 shows a propagation process of a mapping message, where the mapping message includes information of local groups, the information is exchanged between mapping sensor nodes, adjacent local groups are merged, and finally all nodes in the mapping group can obtain an approximate coverage area of a target device signal. Boundary sensor nodes can be determined according to position information of local group nodes, and the boundary sensor nodes form a set VBMeanwhile, a convex hull as shown in fig. 4-3 can be constructed, and the coverage area of the convex hull is the mapping area. When the position of the target device changes, the perception sensor node broadcasts the change to the neighbor nodes around the perception sensor node. The mapping sensor nodes can modify local group information stored by the mapping sensor nodes according to the broadcast information of the perception sensor nodes, and send the updated local group information to other mapping sensor nodes. When one mapping sensor node finds that no node in the surrounding neighbor nodes is still in the coverage range of the target signal, the sensor node exits from the mapping group.
And S4, constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes.
The fitness function of this embodiment is:
therein, fitiA fitness function for the ith search point; dijThe distance between the ith search point and the jth boundary sensor node is calculated;
the average value of the distances between the ith search point and all the boundary sensor nodes is obtained; (x)j，yj) The position coordinates of the jth boundary sensor node are obtained; (x)i，yi) Position coordinates of the ith search point; j is 1, 2, …, NB，NBIs the number of boundary sensor nodes.
And S5, carrying out Fibonacci and scatter iterative search on the non-cooperative target mapping area to obtain a search point with the minimum fitness value, and taking the position information corresponding to the search point as the position information of the non-cooperative target.
The basic structure of the fibonacci scatter search of the present embodiment, with reference to fig. 5, assumes VA、VBAnd VCAre all vectors in D-dimensional Euclidean space, VAAnd VB represents the coordinates corresponding to the search end points of the tree structure, which can be generated by specifying an optimization rule, VCRepresenting the coordinates of the segmentation points found according to given calculation criteria.
Assuming that the fitness function fit (V) has a minimum value in a specified optimization interval, the fitness function value can be obtained according to a search endpoint on the basic structure, if fit (V)A)＜fit(VB) Then dividing point VCSatisfies the equation:
in this embodiment, the fibonacci scatter iterative search includes a global search and a local optimization.
Wherein the endpoints of the global search phase may be represented as follows
{vA}＝Vp＝{vq|q＝[1，Fp]}；
Wherein, VpSet of coordinates, V, for all search points during the p-th iterationqIs a VpQ denotes the sequence numbers corresponding to the 1 st to p th fibonacci sequences.
And
respectively representing an upper boundary and a lower boundary corresponding to the search point when the dimension value is f. Endpoint VACan be taken over VpAll search points in (1), end point VBDesirable section
Global random points uniformly distributed on, len ({ V)B})＝Fp. According to the selected end point VAAnd VBTo find a dividing point VS1. Corresponding to the global search stage, the schematic diagram of the fibonacci scatter constructing process is shown in fig. 6, where a white dotted circle represents the search point set of the previous iteration, and a black solid circle represents the endpoint V of the current iterationAThe gray solid circles represent randomly generated endpoints VBWhite solid line circle represents the division point VS1。
In the local optimization phase, assume VbestFor search points for which the fitness value is optimal during the current iteration, i.e. for
Vbest＝BEST(Vp)；
Let endpoint VA＝VbestEnd point VBOther global random points are taken in the optimized space to obtain
fit(vA)＝min{fit(vq)，q＝[1，Fp]}；
vB＝{vq|vq∈Vp∧Vq≠vA}；
Search point V based on fitness value optimizationAAnd a global random point VBTo find the segmentation point set V of the local optimization stageS2. A schematic diagram of the fibonacci scatter construction process for the local optimization stage is shown in fig. 7.
Search points generated in two different optimization stages include an endpoint VA、VBAnd a division point VS1，VS2Adding the search point of the current column, 3F is obtainedp+2 search points. Calculating the fitness values of all the search points, comparing the fitness values, sequencing the search points according to the sequence of the fitness from good to bad, and reserving F which is before the fitness valuep+1One search point forms the search point set of the next iteration and the rest 3F is discardedp+2-Fp+1And searching points.
Based on the above principle, the process of fibonacci scatter iterative search in this embodiment is:
step 501, setting an initial value of a Fibonacci scatter layer sequence number p as 1;
502, calculating the search points of each layer of Fibonacci scatter;
the search point number of each layer of fibonacci scatters in this embodiment is calculated according to the following formula:
step 503, randomly selecting F from the non-cooperative target mapping areapThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
step 504, pairing each first global search point and each second global search point one by one;
step 505, searching points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
step 506, calculating moderate values of the first global search point, the second global search point and the third global search point;
step 507, selecting a search point corresponding to the minimum fitness value as a first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
step 508, according to the search points FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
509, calculating fitness values of the second local search point and the third local search point and sequencing all the search points according to the fitness values from small to large;
step 510, judging whether p is more than or equal to Fibonacci scatter depth NbIf yes, go to step 511; if not, go to step 513;
step 511, selecting the top F from the sorted search pointsp+1The search points form a new first global search point set; and randomly selecting F from the rest non-cooperative target mapping areasp+1The global search points form a new second global search point set;
step 512, making p equal to p +1, and returning to step 504;
step 513, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target.
Calculating a third global search point and a third local search point according to the following formulas:
wherein, VCIs a dividing point; vAAnd VBRespectively a first search point and a second search point; fpAnd Fp+1Search points of the p-th layer and the p + 1-th layer of Fibonacci scatter are respectively;
when the first search point and the second search point are respectively a first global search point and a second global search point, the division point is a third global search point; when the first search point and the second search point are the first local search point and the second local search point, respectively, the division point is a third local search point.
In the embodiment, the position information of each sensor node is obtained by cooperatively positioning the sensor nodes in the sensor network of the non-cooperative target area; acquiring the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network; determining a non-cooperative target mapping area according to the position information of each boundary sensor node; fibonacci and scatter iterative search is carried out on the non-cooperative target mapping area through a fitness function, so that non-cooperative target positioning under the condition of no distance measurement is realized; in the embodiment, Fibonacci and scattered-branch iterative search is adopted, newly generated search points each time comprise a global random point in an optimization interval and a local point in a Fibonacci and scattered-branch structure, the search points of each layer of Fibonacci and scattered-branch grow on the basis of the optimal search point of the previous layer, the optimality of part of the search points is reserved, the global randomness is introduced into the search points, and the global optimization and convergence evolution capabilities are greatly improved.
The following example illustrates a specific process of fibonacci scatter iterative search:
1. randomly selecting a search point (node 1) as a search end point in the mapping region, then randomly selecting a node (node 2) as a search end point, solving a division point (node 3), calculating and sequencing the fitness values of the node 1, the node 2 and the node 3, wherein the fitness value of the node 2 is optimal, and referring to fig. 8-1.
2. Local optimization is performed by taking the node 2 as an end point, as shown in fig. 8-2, a search point (node 4) is randomly selected as the end point, a segmentation point (node 5) is obtained, the fitness values of all nodes are ranked, and the optimal two nodes (node 4 and node 5) are selected as the end points of the 2 nd iteration.
3. 2 search points (node 6 and node 7) are randomly selected, a division point (node 8) between the node 4 and the node 6 and a division point (node 9) between the node 5 and the node 7 are obtained, and fitness values of the node 4, the node 5, the node 6, the node 7, the node 8 and the node 9 are calculated and sorted, referring to fig. 8-3.
4. Taking the node 6 with the optimal fitness value (the minimum fitness value) as an end point, randomly selecting a search point (node 10) as another search point, solving a division point (node 11) between the node 6 and the node 10, calculating and sequencing the fitness values of the node 4, the node 5, the node 6, the node 7, the node 8, the node 9, the node 10 and the node 11, and selecting the node 6, the node 10 and the node 11 as the search end point of the 3 rd iteration, referring to fig. 8-4.
Another embodiment provides a non-cooperative target positioning system based on fibonacci scatter search, the non-cooperative target positioning system comprising:
and the cooperative positioning module is used for performing cooperative positioning on the sensor nodes in the sensor network of the non-cooperative target area to obtain the position information of each sensor node. The cooperative positioning module comprises:
the construction submodule is used for acquiring neighbor sensor nodes of each sensor node in the sensor network of the non-cooperative target area and constructing a local network corresponding to each sensor node;
the initial local map sub-module is used for taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
the secondary local map sub-module is used for selecting secondary central nodes from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map;
the stitching submodule is used for stitching the initial local map and the secondary local map to obtain a stitched map;
the first judgment submodule is used for judging whether the stitching map covers all the sensor nodes, if so, the stitching map is transmitted to the first calculation submodule; if not, giving the combined map to the initial local map and transmitting the combined map to the secondary local map sub-module;
the first calculation submodule is used for calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map;
the information matrix submodule is used for obtaining a distance information matrix between the sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
the characteristic decomposition submodule is used for performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and the determining submodule is used for determining the position information of each sensor node according to the characteristic vector matrix of each local network.
The acquisition module is used for acquiring the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network;
the determining module is used for determining a non-cooperative target mapping area according to the position information of each boundary sensor node;
the construction module is used for constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes;
and the iterative search module is used for carrying out Fibonacci and scatter iterative search on the non-cooperative target mapping area to obtain a search point with the minimum fitness value and taking the position information corresponding to the search point as the position information of the non-cooperative target. The iterative search module comprises:
the initial submodule is used for setting the initial value of the Fibonacci scatter layer sequence number p to be 1;
the second calculation submodule is used for calculating the search points of each layer of Fibonacci scatter;
a first selection submodule for randomly selecting F from the non-cooperative target mapping regionpThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
the matching submodule is used for matching each first global search point with each second global search point one by one;
a first division submodule for dividing the search points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
the third calculation submodule is used for calculating the moderate value of each first global search point, each second global search point and each third global search point;
the second selection submodule is used for selecting the search point corresponding to the minimum fitness value as the first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
a second division submodule for dividing the search point number FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
the sorting submodule is used for calculating the fitness values of the second local search point and the third local search point and sorting all the search points according to the fitness values from small to large;
a second judgment submodule for judging whether p is greater than or equal to the Fibonacci scatter depth NbIf yes, the sorted local search points are sent to a third selection submodule; if not, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target;
a third selection submodule for selecting a front F from the sorted search pointsp+1The search points form a new first global search point set; and randomly selecting from the rest non-cooperative target mapping areasFp+1The global search points form a new second global search point set; let p be p +1 and give the pairing submodule.
Although the embodiments of the present invention have been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the embodiments of the present invention.
Claims (10)
1. A non-cooperative target positioning method based on Fibonacci scatter search is characterized by comprising the following steps:
the method comprises the following steps that firstly, sensor nodes in a sensor network of a non-cooperative target area are cooperatively positioned to obtain position information of each sensor node;
acquiring the sensing state of each sensor node on a non-cooperative target area signal and determining each boundary sensor node in the sensor network;
step three, determining a non-cooperative target mapping area according to the position information of each boundary sensor node;
fourthly, constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes;
fifthly, Fibonacci and scatter iterative search is carried out on the non-cooperative target mapping area to obtain a search point with the minimum fitness value, and position information corresponding to the search point is used as position information of the non-cooperative target.
2. The method for locating a non-cooperative target according to claim 1, wherein in step one, the specific implementation process of cooperative positioning is as follows:
101, acquiring neighbor sensor nodes of each sensor node in a sensor network of a non-cooperative target area, and constructing a local network corresponding to each sensor node;
step 102, taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
103, selecting a secondary center node from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map;
step 104, stitching the initial local map and the secondary local map to obtain a stitched map;
step 105, judging whether the stitching map covers all the sensor nodes, if so, entering step 106; if not, the combined map is assigned to the initial local map, and the step 103 is returned;
106, calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map;
step 107, obtaining a distance information matrix between sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
108, performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and step 109, determining the position information of each sensor node according to the characteristic vector matrix of each local network.
3. The method according to claim 2, wherein in step 103, the secondary local map satisfies the following requirements:
the number of the sensor nodes in the intersection between the secondary local map and the initial local map is not less than 3; the number of sensor nodes in the difference set between the secondary local map and the initial local map is the largest.
4. The method according to any of claims 1 to 3, wherein in step four, the fitness function is:
therein, fitiA fitness function for the ith search point; dijThe distance between the ith search point and the jth boundary sensor node is calculated;
the average value of the distances between the ith search point and all the boundary sensor nodes is obtained; (x)j，yj) The position coordinates of the jth boundary sensor node are obtained; (x)i，yi) Position coordinates of the ith search point; j is 1, 2, …, NB，NBIs the number of boundary sensor nodes.
5. The method according to claim 4, wherein in step five, the process of fibonacci scatter iterative search is:
step 501, setting an initial value of a Fibonacci scatter layer sequence number p as 1;
502, calculating the search points of each layer of Fibonacci scatter;
step 503, randomly selecting F from the non-cooperative target mapping areapThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
step 504, pairing each first global search point and each second global search point one by one;
step 505, searching points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
step 506, calculating moderate values of the first global search point, the second global search point and the third global search point;
step 507, selecting a search point corresponding to the minimum fitness value as a first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
step 508, according to the search points FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
509, calculating fitness values of the second local search point and the third local search point and sequencing all the search points according to the fitness values from small to large;
step 510, judging whether p is more than or equal to Fibonacci scatter depth NbIf yes, go to step 511; if not, go to step 513;
step 511, selecting the top F from the sorted search pointsp+1The search points form a new first global search point set; and randomly selecting F from the rest non-cooperative target mapping areasp+1The global search points form a new second global search point set;
step 512, making p equal to p +1, and returning to step 504;
step 513, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target.
6. The method according to claim 5, wherein in step 502, the search points of each layer of Fibonacci scatter are calculated according to the following formula:
7. the method of claim 5, wherein the third global search point and the third local search point are calculated according to the following formula:
wherein, VCIs a dividing point; vAAnd VBRespectively a first search point and a second search point; fpAnd Fp+1Search points of the p-th layer and the p + 1-th layer of Fibonacci scatter are respectively;
when the first search point and the second search point are respectively a first global search point and a second global search point, the division point is a third global search point; when the first search point and the second search point are the first local search point and the second local search point, respectively, the division point is a third local search point.
8. A non-cooperative target location system based on fibonacci scatter search, the non-cooperative target location system comprising:
the cooperative positioning module is used for performing cooperative positioning on the sensor nodes in the sensor network of the non-cooperative target area to obtain the position information of each sensor node;
the acquisition module is used for acquiring the sensing state of each sensor node on the non-cooperative target area signal and determining each boundary sensor node in the sensor network;
the determining module is used for determining a non-cooperative target mapping area according to the position information of each boundary sensor node;
the construction module is used for constructing a fitness function according to the positions between the search points in the non-cooperative target mapping area and the boundary sensor nodes;
and the iterative search module is used for carrying out Fibonacci and scatter iterative search on the non-cooperative target mapping area to obtain a search point with the minimum fitness value and taking the position information corresponding to the search point as the position information of the non-cooperative target.
9. The non-cooperative object localization system according to claim 8, wherein the cooperative localization module comprises:
the construction submodule is used for acquiring neighbor sensor nodes of each sensor node in the sensor network of the non-cooperative target area and constructing a local network corresponding to each sensor node;
the initial local map sub-module is used for taking the sensor node with the largest number of neighbor sensor nodes as an initial central node; taking a local network corresponding to the initial central node as an initial local map;
the secondary local map sub-module is used for selecting secondary central nodes from local networks corresponding to the rest sensor nodes; and the local network corresponding to the secondary central node is used as a secondary local map;
the stitching submodule is used for stitching the initial local map and the secondary local map to obtain a stitched map;
the first judgment submodule is used for judging whether the stitching map covers all the sensor nodes, if so, the stitching map is transmitted to the first calculation submodule; if not, giving the combined map to the initial local map and transmitting the combined map to the secondary local map sub-module;
the first calculation submodule is used for calculating the Euclidean distance between each sensor node and each neighbor sensor node according to the signal arrival time of each sensor node and each corresponding neighbor sensor node in the stitching map;
the information matrix submodule is used for obtaining a distance information matrix between the sensor nodes of each local network according to the Euclidean distance between each sensor node and each corresponding neighbor sensor node;
the characteristic decomposition submodule is used for performing characteristic decomposition on the distance information matrix between the sensor nodes of each local network to obtain a characteristic vector matrix of each local network;
and the determining submodule is used for determining the position information of each sensor node according to the characteristic vector matrix of each local network.
10. The non-cooperative object locating system of claim 8, wherein the iterative search module comprises:
the initial submodule is used for setting the initial value of the Fibonacci scatter layer sequence number p to be 1;
the second calculation submodule is used for calculating the search points of each layer of Fibonacci scatter;
a first selection submodule for randomly selecting F from the non-cooperative target mapping regionpThe first global search points and the second global search points respectively form a first global search point set and a second global search point set;
the matching submodule is used for matching each first global search point with each second global search point one by one;
a first division submodule for dividing the search points F according to the p-th and p + 1-th layers of Fibonacci scatterpAnd Fp+1Dividing a connecting line between each first global search point and the corresponding second global search point to obtain a corresponding third global search point;
the third calculation submodule is used for calculating the moderate value of each first global search point, each second global search point and each third global search point;
the second selection submodule is used for selecting the search point corresponding to the minimum fitness value as the first local search point; randomly selecting a second local search point from the rest non-cooperative target mapping areas;
a second division submodule for dividing the search point number FpAnd Fp+1Dividing a connecting line between the first local search point and the second local search point to obtain a corresponding third local search point;
the sorting submodule is used for calculating the fitness values of the second local search point and the third local search point and sorting all the search points according to the fitness values from small to large;
a second judgment submodule for judging whether p is greater than or equal to the Fibonacci scatter depth NbIf yes, the sorted local search points are sent to a third selection submodule; if not, the position information of the search point corresponding to the minimum fitness value is used as the position information of the non-cooperative target;
a third selection submodule for selecting a front F from the sorted search pointsp+1The search points form a new first global search point set; and randomly selecting F from the rest non-cooperative target mapping areasp+1The global search points form a new second global search point set; let p be p +1 and give the pairing submodule.
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