Target condensation method applied to scene surveillance radar

文档序号:6593 发布日期:2021-09-17 浏览:27次 中文

1. A target condensation method applied to a scene surveillance radar is characterized by comprising the following steps:

s1, scanning through a radar to obtain an initial scanning result; the initial scanning result comprises a plurality of target points;

s2, taking each target point as an initial point cluster; wherein a single point cluster comprises n distance dimensions, each distance dimension comprising a start angle, an end angle and a center point;

s3, traversing the existing initial point cluster by the initial point cluster P, outputting the initial point cluster with the difference between the termination angle and the termination angle of the initial point cluster P larger than the loss threshold, and sending all the initial point clusters which are not output into the step S4;

s4, outputting the initial point cluster with the difference between the initial angle and the initial angle of the initial point cluster P larger than the size threshold, and ending the traversal;

s5, judging whether the distance between the initial point cluster P and any output initial point cluster is greater than a distance threshold, if so, judging that the initial point cluster P does not belong to the output initial point cluster, and entering the step S9; otherwise, go to step S6;

s6, judging whether the initial point cluster P belongs to the existing distance dimension of the output initial point cluster, if so, refreshing the distance dimension information of the output initial point cluster, simultaneously completing the association of the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and entering the step S9; otherwise, go to step S7;

s7, judging whether the initial point cluster P meets the distance dimension requirement of the output initial point cluster; if yes, adding a new distance dimension to the output initial point cluster, completing the association between the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and proceeding to step S9; otherwise, go to step S8;

s8, judging whether the initial point cluster P completes traversing with each output initial point cluster, if so, taking the initial point cluster P as an independent point cluster and outputting the independent point cluster to complete target aggregation of the initial point cluster P, and entering the step S9; otherwise, returning to the step S5;

s9, judging whether to continue the target condensation, if so, entering the step S10, and if not, ending the target condensation;

s10, judging whether a new target point pointed by the beam exists at the next moment, if so, taking each new target point as an initial point cluster, and returning to the step S3; otherwise, the process returns to step S8.

Background

The scene monitoring radar system performs preprocessing such as matched filtering, pulse compression, MTI, MTD, clutter suppression, constant false alarm and the like on echo signals to obtain information such as the distance, the direction and the like of a target. However, due to the limitation of the half-power beam width and the range gate of the antenna designed by the scene surveillance radar, a plurality of point clusters can occur after the pretreatment of a single large physical size target (hereinafter referred to as a large target). If the information of the point clusters is not judged, the situation that a single large target is split into a plurality of targets occurs, and meanwhile, the point clusters cannot be fused into one target simply and directly, so that the situation that a plurality of targets are fused into one target may occur.

Disclosure of Invention

Aiming at the defects in the prior art, the target aggregation method applied to the scene monitoring radar solves the problem that a single large target is split into a plurality of targets or a plurality of targets are fused into one target when the scene monitoring radar monitors the scene.

In order to achieve the purpose of the invention, the invention adopts the technical scheme that:

a target condensing method applied to a scene surveillance radar is provided, which comprises the following steps:

s1, scanning through a radar to obtain an initial scanning result; the initial scanning result comprises a plurality of target points;

s2, taking each target point as an initial point cluster; wherein a single point cluster comprises n distance dimensions, each distance dimension comprising a start angle, an end angle and a center point;

s3, traversing the existing initial point cluster by the initial point cluster P, outputting the initial point cluster with the difference between the termination angle and the termination angle of the initial point cluster P larger than the loss threshold, and sending all the initial point clusters which are not output into the step S4;

s4, outputting the initial point cluster with the difference between the initial angle and the initial angle of the initial point cluster P larger than the size threshold, and ending the traversal;

s5, judging whether the distance between the initial point cluster P and any output initial point cluster is greater than a distance threshold, if so, judging that the initial point cluster P does not belong to the output initial point cluster, and entering the step S9; otherwise, go to step S6;

s6, judging whether the initial point cluster P belongs to the existing distance dimension of the output initial point cluster, if so, refreshing the distance dimension information of the output initial point cluster, simultaneously completing the association of the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and entering the step S9; otherwise, go to step S7;

s7, judging whether the initial point cluster P meets the distance dimension requirement of the output initial point cluster; if yes, adding a new distance dimension to the output initial point cluster, completing the association between the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and proceeding to step S9; otherwise, go to step S8;

s8, judging whether the initial point cluster P completes traversing with each output initial point cluster, if so, taking the initial point cluster P as an independent point cluster and outputting the independent point cluster to complete target aggregation of the initial point cluster P, and entering the step S9; otherwise, returning to the step S5;

s9, judging whether to continue the target condensation, if so, entering the step S10, and if not, ending the target condensation;

s10, judging whether a new target point pointed by the beam exists at the next moment, if so, taking each new target point as an initial point cluster, and returning to the step S3; otherwise, the process returns to step S8.

The invention has the beneficial effects that: and processing the preprocessed target information by adopting a target agglomeration algorithm so as to accurately screen the preprocessed target information and obtain the accurate and real position of the target. The point cluster information is established according to certain distance and angle information, and the distance dimension correlation calculation is carried out on the input target point and the point cluster, so that the target splitting phenomenon after the condensation can be avoided. When the target performs steering motion, angle dimension correlation calculation is performed by using the input target point and the point cluster, so that angle flicker of the condensed target in the steering motion is avoided.

Drawings

FIG. 1 is a flow chart of a method of the present invention;

fig. 2 is a schematic diagram of single point cluster information according to the present invention.

Detailed Description

The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.

As shown in fig. 1, the target condensing method applied to the scene surveillance radar includes the steps of:

s1, scanning through a radar to obtain an initial scanning result; the initial scanning result comprises a plurality of target points;

s2, taking each target point as an initial point cluster; wherein a single point cluster comprises n distance dimensions, each distance dimension comprising a start angle, an end angle and a center point;

s3, traversing the existing initial point cluster by the initial point cluster P, outputting the initial point cluster with the difference between the termination angle and the termination angle of the initial point cluster P larger than the loss threshold, and sending all the initial point clusters which are not output into the step S4;

s4, outputting the initial point cluster with the difference between the initial angle and the initial angle of the initial point cluster P larger than the size threshold, and ending the traversal;

s5, judging whether the distance between the initial point cluster P and any output initial point cluster is greater than a distance threshold, if so, judging that the initial point cluster P does not belong to the output initial point cluster, and entering the step S9; otherwise, go to step S6;

s6, judging whether the initial point cluster P belongs to the existing distance dimension of the output initial point cluster, if so, refreshing the distance dimension information of the output initial point cluster, simultaneously completing the association of the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and entering the step S9; otherwise, go to step S7;

s7, judging whether the initial point cluster P meets the distance dimension requirement of the output initial point cluster; if yes, adding a new distance dimension to the output initial point cluster, completing the association between the initial point cluster P and the output initial point cluster, completing the target aggregation of the initial point cluster P, and proceeding to step S9; otherwise, go to step S8;

s8, judging whether the initial point cluster P completes traversing with each output initial point cluster, if so, taking the initial point cluster P as an independent point cluster and outputting the independent point cluster to complete target aggregation of the initial point cluster P, and entering the step S9; otherwise, returning to the step S5;

s9, judging whether to continue the target condensation, if so, entering the step S10, and if not, ending the target condensation;

s10, judging whether a new target point pointed by the beam exists at the next moment, if so, taking each new target point as an initial point cluster, and returning to the step S3; otherwise, the process returns to step S8.

In one embodiment of the invention, a single cluster of points contains information as shown in FIG. 2, where:

the single point cluster comprises n distance dimensions, each distance dimension comprises 3 items of information of a start angle, an end angle and a central point, namely the start angle of the ith distance dimension is Dbi, the end angle is Dei, the distance value of the central point is Xi, and the width of the ith distance dimension is Si; the distance value of the central point is the central position of all points of the distance dimension, and the starting angle and the ending angle are used for judging the size loss condition of the point cluster.

Taking a distance dimension as an example, a dark circle pattern in the figure represents the position of the start angle of the distance dimension, a light circle pattern represents the position of the end angle of the distance dimension, and a dark five-pointed star pattern represents the position of the center point of the distance dimension.

The invention adopts the target agglomeration algorithm to process the preprocessed target information so as to accurately screen the preprocessed target information and obtain the accurate and real position of the target. The point cluster information is established according to certain distance and angle information, and the distance dimension correlation calculation is carried out on the input target point and the point cluster, so that the target splitting phenomenon after the condensation can be avoided. When the target performs steering motion, angle dimension correlation calculation is performed by using the input target point and the point cluster, so that angle flicker of the condensed target in the steering motion is avoided.

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