摘要
Abstract
To address the problem of low probability of intercept(LPI)radar evading anti-radiation reconnaissance through carrier frequency agility,random pulse width modulation,and other means,a blind estimation algorithm for waveform parameters based on improved Wigner-Ville distribution(WVD)and adaptive particle filter is proposed.To mitigate the crossterm interference of WVD when processing multicomponent signals,a time-frequency masking window technique is adopted for suppression,enabling the extraction of high-confidence pulse description word(PDW).On this basis,a frequency hopping pattern prediction framework based on hidden Markov model(HMM)is established,achieving online learning and realtime tracking of waveform agility rules.Simulation results show that the proposed algorithm can effectively process frequency hopping radar signals with a frequency agility range exceeding 20%,and the parameter estimation accuracy is improved by about 15%compared with that of conventional autocorrelation-based algorithms,thus effectively enhancing the survivability and attack accuracy of anti-radiation unmanned aerial vehicle(UAV)in electronic jamming environments.关键词
反辐射无人机/低截获概率(LPI)雷达/波形捷变/盲参数估计/改进型维格纳分布(WVD)/隐马尔可夫模型(HMM)Key words
anti-radiation UAV/low probability of intercept(LPI)radar/waveform agility/blind parameter estimation/improved Wigner-Ville distribution(WVD)/hidden Markov model(HMM)分类
信息技术与安全科学
