Probabilistic modeling of multifunction radars with autoregressive kernel mixture network  

作　　者：Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang 

机构地区：[1]School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

出　　处：《Defence Technology（防务技术）》2024年第5期275-288,共14页Defence Technology

基　　金：supported by the National Natural Science Foundation of China under Grant 62301119。

摘　　要：The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关 键 词：Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection 

分 类 号：TN957.51[电子电信—信号与信息处理]