CN110673090A - Passive multi-station multi-target positioning method based on DBSCAN - Google Patents
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Abstract
The invention belongs to the technical field of electronic countermeasure, and particularly relates to a passive multi-station multi-target positioning method based on DBSCAN. The method mainly comprises the steps of firstly determining observation sight lines by combining angle information measured by an observation station relative to targets and position information of the observation station, then finding clusters formed by intersections near each target according to the positions of intersections of all sight lines obtained by the observation sight lines through a density-based DBSCAN clustering method, and finally realizing the positioning of a plurality of targets by finding the central positions of the clusters. The method has the advantages that the method can effectively carry out positioning estimation on a plurality of targets through the angle information measured by a plurality of observation stations and the positions of the observation stations, and is simple and good in effect.
Description
Technical Field
The invention belongs to the technical field of electronic countermeasure, and particularly relates to a passive multi-station multi-target positioning method based on DBSCAN.
Background
In modern electronic information war, the most key point is the competition of electromagnetic space between enemies and my and the detection and monitoring of the key targets of the opponent, so as to obtain the information of strategic deployment, platform type and the like of enemy units. Therefore, in the field of electronic countermeasure, passive positioning technology plays an increasingly important role in improving electronic combat capability due to its advantages of long working distance, hidden reception, strong anti-interference capability, and being not easily perceived by the other party. In passive positioning, target positioning using angle information is a widely used positioning method. However, in the case of multiple stations and multiple targets, multiple false target points occur, which makes the positioning method difficult to implement.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a relatively representative Density-Based Clustering algorithm. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of density-connected points, it is possible to partition areas with sufficiently high density into clusters and find clusters of arbitrary shape in a spatial database of noise. Therefore, in passive multi-station multi-target lateral cross positioning, if the target position is regarded as a set of a plurality of observation line intersection points which are intersected pairwise, target positioning can be achieved through a DBSCAN clustering method. At present, the DBSCAN clustering method has less research in passive positioning and is a direction worthy of research.
Disclosure of Invention
Aiming at the problems, the invention provides a passive multi-station multi-target positioning method based on DBSCAN.
The technical scheme adopted by the invention is as follows:
a passive multi-station multi-target positioning method based on DBSCAN mainly determines observation sight lines by combining angle information measured by an observation station relative to a target and position information of the observation station, so that the positions of cross points of all the observation sight lines are obtained, and even under the condition that certain angle measurement errors exist, the density of the cross points near the real position of the target is still the largest. Therefore, the crossing point sets near each target point can be found out by the density clustering-based DBSCAN method, the center of the crossing point position in each set is calculated, and the target positioning is realized.
For convenience of description, taking a two-dimensional plane as an example, assuming that there are M observation stations and N targets in a certain area of the two-dimensional plane, the azimuth angle measurement value measured from the mth observation station to the nth target can be obtained by measuring the azimuth angle of the targets by the observation stations
Wherein M is 1,2, 1. Because the coordinates of the observation stations are known, an observation sight line from the mth observation station to the nth target can be uniquely determined according to the azimuth angle information, and then M multiplied by N observation sight lines can be obtained. For the observation line of sight from any observation station to any target, the observation line of sight from the rest M-1 observation stations to all targets can intersect with the observation line of sight. For any two stations, the intersection point of their observation lines of sight has a total of N2One, M observation stations are shared
In pairs, i.e. common to all lines of sight
And (4) a point of intersection. Assuming that there is no angle measurement error, the point with the highest repetition degree, i.e. the highest density, of the intersection points may be regarded as the specific position of the target. Due to the existence of measurement errors, the sight line intersections at the real target position do not completely coincide but are distributed near the target position, so that a high-density region is obtained. Meanwhile, some false points are distributed at other positions, namely intersection points formed by pairwise intersection of the sight lines at the non-target real positions.
Performing density clustering by using all intersection positions obtained by pairwise intersection of all observation sight lines and adopting a density clustering-based DBSCAN algorithm, wherein the algorithm specifically comprises the following steps:
inputting: sample, i.e., all intersection position information D ═ x1,x2,...,xm) WhereinNeighborhood parameters (epsilon, MinPts) which are epsilon represent a neighborhood distance threshold of a certain intersection point, wherein MinPts describes a threshold of the number of samples in a neighborhood of which the distance of the certain intersection point is epsilon; the sample distance measure is the euclidean distance.
1) Initializing a set of core objectsInitializing cluster number k equal to 0, initializing sample set Γ equal to D, and cluster partitioning
2) For j 1, 2.. times.m, all core objects are found by the following steps:
a) by means of distance measurement, find sample xjE-neighborhood subsample set N∈(xj)。
b) If the number of the sub-sample set samples satisfies | N∈(xj) | ≧ MinPts, sample xjAdding core object sample set of omega-omega ∪ { xj}。
3) If core object set
The algorithm ends, otherwise step 4 is carried out.
4) In a core object set omega, a core object o is randomly selected, and a current cluster core object queue omega is initializedcurInitializing a class index k +1, and initializing a current cluster sample set CkAnd f, updating the unvisited sample set f- (o).
5) If the current cluster core object queue
Then the current cluster C is clusteredkAfter generation, the cluster partition C is updated to { C ═ C1,C2,...,CkAnd updating a core object set omega-CkAnd (5) turning to the step 3.
6) In the current cluster core object queue omegacurTaking out a core object o', finding out all belonged-neighborhood subsample sets N through neighborhood distance threshold belonged∈(o') making Δ ═ N∈(o') ∩ gamma, updating the current cluster sample set Ck＝Ck∪ delta, update the set of unaccessed samples Γ ═ Γ -delta, update Ωcur＝Ωcur∪ (. DELTA. ∩. omega. -o'), proceed to step 5.
7) Deleting abnormal points which do not belong to any cluster, then calculating the diameter of each cluster, wherein the diameter is measured by the maximum distance between two points in the cluster, and deleting the clusters with the diameter larger than a certain threshold value to obtain a final clustering result, namely cluster division C ═ { C ═ C1,C2,...,Ck}. And obtaining the positioning information of each target by obtaining the midpoint positions of all the cross points in each cluster.
The output result is: position information (x) of a plurality of objects1,x2,...,xN)。
The method has the advantages that the method can effectively carry out positioning estimation on a plurality of targets through the angle information measured by a plurality of observation stations and the positions of the observation stations, and is simple and good in effect.
Drawings
FIG. 1 is a flow chart of a positioning algorithm based on DBSCAN;
FIG. 2 is a view of the observation station and the target radiation source;
FIG. 3 is a view of an intersection point distribution diagram of observation lines of sight based on 8 observation stations and with an angle measurement error of 0.1;
FIG. 4 is an original graph of clustering effect based on DBSCAN;
FIG. 5 is a diagram of the final clustering effect based on DBSCAN;
FIG. 6 is a view of an observation sight intersection distribution diagram based on 8 observation stations and with an angle measurement error of 1;
FIG. 7 is an original graph of clustering effect based on DBSCAN;
fig. 8 is a diagram of the final clustering effect based on DBSCAN.
Detailed Description
The present invention is described in detail below with reference to specific examples:
in this embodiment, matlab and pathon are used to verify the above dbss-based passive multi-station multi-target positioning method, and for the sake of simplicity, the following assumptions are made for the algorithm model:
1) all stations and targets are in the XY plane;
2) all engineering errors are superposed into angle errors;
3) assuming that the target is stationary or moving at a very low speed;
4) the sight lines of the same target are respectively from different observation stations, and two or more than two sight lines of the same radiation source cannot be from the same observation station;
5) one measurement value of one observation station corresponds to only one target.
Simulation scene one:
assuming that the radiation source region is a rectangular area of 100km × 80km, data correlation is performed on 4 target radiation sources using 8 observation stations, which have coordinates of (10, 0), (20, 0), (30, 0), (40, 0) (50, 0), (60, 0), (70, 0), (80, 0) in km and coordinates of the radiation sources of (15, 66), (35, 66), (55, 66), (75, 66) in km. The survey station and target profile is shown in fig. 2. Wherein the observation station angle measurement error is 0.1. Based on the above assumptions, intersection points where all the sight lines intersect pairwise are obtained, and simultaneously, miscellaneous points with coordinates exceeding a certain area are removed, so that the intersection point distribution diagram shown in fig. 3 is obtained.
Simulation scene two:
similarly, assuming that the radiation source region is a rectangular region of 100km x 80km, data association is performed on 4 radiation sources by using 8 fixed receiving stations, and the coordinates of an observation station and a target radiation source are the same as the scene. Similarly, a cross point distribution diagram when the angle measurement error is 1 is obtained, as shown in fig. 6.
Multi-station multi-target positioning effect:
through the intersection position information shown in fig. 3, the density-based DBSCAN algorithm is used to perform intersection clustering, so as to obtain the original clustering effect graph shown in fig. 4, where the clustering result is 5 clusters under the condition of unknown number of targets. According to the actual situation analysis, since the target is a point target, even if an error exists, the diameter of the cluster does not exceed a certain threshold, and therefore, a certain threshold is set, the cluster with the diameter larger than the threshold is deleted, and the abnormal point is removed, the final clustering effect graph can be obtained as shown in fig. 5, and it can be seen that, in the case where the angle measurement error is 0.1, the clustering effect is good, and the final positioning of the target can be effectively realized.
Based on the intersection point position information of fig. 6, the original clustering effect graph shown in fig. 7 can be obtained, 5 clusters are obtained under the condition that the target number is unknown, and clusters which do not meet the conditions and other abnormal points judged by the algorithm are deleted according to the threshold value to obtain the final clustering effect graph shown in fig. 8.
Claims (1)
1. The passive multi-station multi-target positioning method based on DBSCAN is characterized by comprising the following steps of:
s1, obtaining the azimuth angle measurement value of the nth target measured by the mth observation station through the azimuth angle measurement of the targets by the observation stationWherein M1, 2, 1., M, N1, 2,. N;
s2, according to the coordinates of the observation stations, uniquely determining an observation sight line from the mth observation station to the nth target by the azimuth angle measurement value to obtain M multiplied by N observation sight lines;
s3, enabling all observation visual lines to be located on the same plane, namely enabling the observation visual lines from any observation station to any target to be intersected with the observation visual lines from the rest M-1 observation stations to all targets; for any two stations, the intersection point of their observation lines of sight has a total of N2A plurality of observation stations, M in common
In pairs, i.e. common to all lines of sight
A plurality of intersections; due to the measurement error, the intersection points of the lines of sight at the true position of the target do not coincide completely, but rather are divided intoDistributing the image near a target position to obtain a high-density area, and defining an intersection point formed by pairwise intersection of sight lines at a non-target real position as a false point;
s4, obtained by intersecting all observation sight lines pairwise
The method comprises the following steps of carrying out intersection point clustering on the position information of each intersection point by adopting a density clustering-based DBSCAN algorithm, wherein the method comprises the following specific steps:
s41, making all the intersection position information D equal to (x)1,x2,...,xm) As a sample, wherein
Defining neighborhood parameters (epsilon, MinPts) which are epsilon to represent a neighborhood distance threshold of a certain intersection point, wherein MinPts describes a threshold of the number of samples in a neighborhood of which the distance of the certain intersection point is epsilon; the sample distance measurement mode is Euclidean distance;
s42, initializing a core object set
Initializing cluster number k equal to 0, initializing sample set Γ equal to D, and cluster partitioning
S43, for j 1, 2.
a) By means of distance measurement, find sample xjE-neighborhood subsample set N∈(xj)；
b) If the number of the sub-sample set samples satisfies | N∈(xj) | ≧ MinPts, sample xjAdding a core object sample set:
Ω＝Ω∪{xj}；
s44, if the core object setThe algorithm ends and proceeds to step S48, otherwise to step S45;
s45, randomly selecting a core object o in the core object set omega, and initializing the current cluster core object queue omegacurInitializing a class index k +1, and initializing a current cluster sample set CkUpdating the set of unaccessed samples Γ ═ Γ - { o };
s46, if the current cluster core object queue
Then the current cluster C is clusteredkAfter generation, the cluster partition C is updated to { C ═ C1,C2,...,CkAnd updating a core object set omega-CkStep S43 is entered, otherwise step S47 is entered;
s47, queue omega of object in current cluster corecurTaking out a core object o', finding out all belonged-neighborhood subsample sets N through neighborhood distance threshold belonged∈(o') let △ be N∈(o') ∩ gamma, updating the current cluster sample set Ck＝Ck∪△, update the set of unaccessed samples Γ ═ Γ - △, update Ωcur＝Ωcur∪ (△∩ Ω) -o', proceed to step S46;
s48, deleting abnormal points which do not belong to any cluster, then calculating the diameter of each cluster, wherein the diameter is measured by the maximum distance between two points in the cluster, and deleting the clusters with the diameter larger than a certain threshold value to obtain the final clustering result, namely the cluster division C ═ C1,C2,...,Ck}; obtaining the positioning information of each target by obtaining the midpoint positions of all the cross points in each cluster;
s5, outputting a result as follows: position information (x) of a plurality of objects1,x2,...,xN)。
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