Radar signal sorting method based on dynamic correction chaos particle swarm optimization

Abstract

The invention relates to a radar signal sorting method based on dynamic correction chaos particle swarm optimization, and belongs to the technical field of population evolution and signal classification. Aiming at the problems of high pulse flow density and serious characteristic parameter overlapping degree of radiation source signals in a complex electromagnetic environment, a radar signal sorting method based on dynamic correction chaos particle swarm optimization is adopted, and the defects that a traditional clustering sorting algorithm is difficult to classify correctly and the optimization capacity of a particle swarm algorithm is insufficient are overcome. Chaos search is adopted to increase diversity of population later iteration; the updating of the particles is changed in real time according to the state of the population by adopting self-adaptive adjustment parameters; and a new fitness function is used and particle positions are dynamically corrected, so that optimization of the population is more accurate. Compared with other optimization methods, the method has great advantages under several common and new sorting indexes, has better sorting effects on convergence speed, stability and robustness, and can better adapt to a complex electromagnetic environment.

Description

technical field [0001] The invention relates to a radar signal sorting method based on dynamically modified chaotic particle swarm optimization, and belongs to the technical field of population evolution and signal classification. Background technique [0002] With the increasingly complex electromagnetic environment, the dense and variable radiation source signals enter the digital reconnaissance receiver and interweave into complex pulse stream sequences. According to the characteristic parameters of the intercepted pulses, arrival time and other information, the pulse stream is sorted, and the signals belonging to the same radiation source are accurately divided, and then the radar model is identified according to the characteristic parameters of different radiation sources. According to the recognition result, the type, attribute and threat degree of each radar are obtained. From the above analysis, it can be seen that radiation source sorting is a key link in radar rec...

AI Technical Summary

Problems solved by technology

But they are not fully adaptive, and the clustering results cannot well meet the requirements of the radar system for signal sorting accuracy and real-time performance

[0005] In order to accurately separate various radiation source signals from dense, complex and changeable intercepted pulse streams, this ap

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