Time-difference-information-based passive multi-station multi-target direction-finding cross-positioning method

Abstract

The invention, which belongs to the technical field of electronic countermeasures, in particular relates to a time-difference-information-based passive multi-station multi-target direction-finding cross-positioning method. According to the method, on the basis of a multi-radiation-source data association algorithm, observation data from multiple observation stations are associated to obtain a costmatrix C(p,q); and then a coordinate of a target is estimated by using an aggregation hierarchy clustering algorithm based on the obtained cost matrix C(p,q). The method has the following beneficialeffects: association of data from multiple stations is completed accurately and the location of the target is estimated finally and accurately. The method is simple and has the good effect.

Description

technical field [0001] The invention belongs to the technical field of electronic countermeasures, and in particular relates to a passive multi-station multi-target direction-finding cross-location method based on time difference information. Background technique [0002] With the rapid development of radar countermeasure technologies such as electronic jamming and anti-radiation missiles, active positioning represented by radar has been seriously threatened. Since the passive positioning technology can determine the position of the radiation source without generating radiation itself, it has the advantages of long range, concealed reception, strong anti-interference ability and is not easy to be detected by the opponent, which is very important for improving the survival of the system in the electronic warfare environment. Capability and combat effectiveness play a very important role. Therefore, the research of passive positioning technology has been paid more and more att...

AI Technical Summary

Problems solved by technology

[0003] Hierarchical clustering and partitioning clustering are two major methods of data clustering. Hierarchical clustering is created to solve some defects in partitioning clustering algorithms. For example, the effect of partitioning clustering depends on predefined parameters, and the parameters in In many practical situations, there is no way to determine, and there is still an initialization problem in the division of clusters. Poor initialization will directly lead to low clustering effects. Hierarchical clustering is to achieve the purpose of making the clustering process independent of initial parameters and more flexible.

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