Graph matching association method and system for multi-radar dense targets

Abstract

The invention provides a graph matching association method and system for multi-radar dense targets. The method comprises the following steps: constructing a vector set formed by radar tracking targets; analyzing the influence of errors on approximate congruency, and forming a vector similarity matrix; giving a single-moment graph matching algorithm calculation process, and determining a triangle with the highest approximate congruency as an alternative triangle in a vector similarity matrix by using a greedy algorithm; the vertexes and the basic edges are screened from other targets to form alternative associated triangles; and designing an association relationship maintaining method under continuous moments, judging successfully associated triangles at historical moments and current moments by using a DS evidence theory, and determining a target corresponding to a final successfully associated triangle at the current moment, so that the association relationship maintaining stability and the association accuracy are improved. The method has the advantages of being low in time complexity, high in convergence speed and high in real-time performance, and applications such as multi-radar target fusion track generation can be effectively supported by establishing the stable and correct target incidence relation.

Description

technical field [0001] The invention belongs to the technical field of radar target tracking, and in particular relates to a method and system for pattern matching and association of multi-radar dense targets. Background technique [0002] The use of radar to monitor dense targets is of great significance to engineering practice, and target clusters have some of the same characteristics because of their concentrated distribution. For example, in the early stage of the orbiting of multiple satellites with one arrow, the distance between satellites is relatively close, and the speed of movement is basically the same, thus forming a dense target group. [0003] Dense target groups are usually distributed within 100 kilometers of space, and the distance between the targets is between 2 kilometers and 10 kilometers. When the phased array radar measures the spatial position of the target, it is greatly affected by systematic errors and random errors, and the spatial position of th...

AI Technical Summary

Problems solved by technology

Therefore, the main problem faced by the neural network method for dense target association is that it is difficult to collect suitable training data

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