Maximum likelihood estimation based deinterleaving algorithm of radar signal in non-ideal scenarios
The existing research has elaborated on the problem of deinterleaving of radar pulse signal under ideal conditions,but lacks the model representation under two non-ideal scenarios,spurious pulses and lost pulses.To solve this problem,a deinterleaving algorithm of radar signals in non-ideal scenarios based on maximum likelihood estimation is proposed,which characterizes spurious pulse and lost pulse phenomena by modifying the likelihood factor,so as to improve the deinterleaving accuracy in complex scenarios.When part of the radar prior information is known,the proposed algorithm model has better deinterleaving effect.Simulation experiment results show that,compared with the existing maximum likelihood model and the deep learning algorithm,the proposed algorithm has a significant improvement in the deinterleaving accuracy and has high application value.
deinteleaving of radar signalpulse repetition intervalelectronic reconnaissancemaximum likelihood estimataion
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