Random set type radar radiation source signal parameter high frequency mode mining method based on weighted clustering

Abstract

The invention discloses a random set type radar radiation source signal parameter high frequency mode mining method based on weighted clustering. A similarity distance matrix is constructed for the signal parameter data of the random set type airborne radar radiation source target; the similarity of every two random set type measurement values is compared; the random set type clusters are iteratively merged on the basis of the similarity distance matrix according to a hierarchical clustering method until the cluster meeting the random set distance threshold cannot be found; meanwhile, the support degree of each new cluster and the random set value are iteratively calculated, and finally the corresponding random set value meeting the support degree threshold clustering is mined to act as the high frequency mode to be outputted. The advantages of the method are that (1) the similarity comparison method for the random set type radar radiation source signal parameter is put forward; (2) the high frequency signal parameter mode can be further mined based on the similarity distance matrix; and (3) the calculation cost is low and the implementation method is engineered.

Description

technical field [0001] The invention relates to the field of data analysis and processing of random aggregate radar radiation source signal parameters, in particular to a method for mining high-frequency mode of random aggregate radar radiation source signal parameters based on weighted clustering. Background technique [0002] As we all know, there are more and more signal characteristics of today's radar emitters. In addition to traditional types, airborne radar emitters also have random aggregate signal parameters composed of several random measured values. Most of the traditional signal feature types are continuous and discrete. Nowadays, random aggregate signal features are also an important type of radar emitter data. This is because, as the complexity of radar emitters increases, the types and forms of signal signatures become increasingly diverse. In addition, due to the progress of signal characteristic measurement technology and the influence of measurement envir...

AI Technical Summary

Problems solved by technology

The existing radar emitter signal feature analysis methods and radar emitter type identification methods are almost all for fixed-value signal features, and they cannot describe and analyze random set signal features specifically.

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