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Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

Defence Technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

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

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Reconstruction of multifunction radar Search Plan Based on Multiple Sequence Alignment

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Chinese Journal of Electronics 2014年第1期23卷 213-218页

作者： MA Shuang LIU Zheng JIANG Wenli College of Electronic Science and Engineering National University of Defense Technology

multifunction radar(MFR) is a sophisticated sensor which could perform several different functions simultaneously. The problems of deciphering the signals of MFR and inferring the information of its capabilities have posed a great challenge to the Electronic intelligence(ELINT) society. In this paper, we propose a method to reconstruct MFR's search plan from its signals by drawing lessons from the bioinformatics approach"Multiple sequence alignment(MSA)", which is used extensively for gene analysis. In the method, the signal of MFR is represented as a structured task sequence, and is segmented into several blocks according to the inherent periodicity of the search plan. Then, these blocks are aligned optimally to distinguish the tasks relevant to the search function from the others. Simulation results show that the proposed method is applicable and effective.

关键词： multifunction radar Search plan Multiple sequence alignment Electronic intelligence

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