信号处理2025，Vol.41Issue(12)：1908-1916,9.DOI:10.12466/xhcl.2025.12.004
基于改进SHAEKF算法的外辐射源雷达机动目标跟踪
Tracking Maneuvering Targets with Passive Radar Based on An Improved SHAEKF Algorithm
摘要
Abstract
Given the increasing proliferation of drone systems,monitoring low-altitude,slow,small(LSS)targets in urban environments is an increasingly urgent requirement.However,in complex urban settings,factors such as occlu-sion by buildings and multipath propagation prevent passive radar systems from characterizing prior information on mea-surement errors accurately.This leads to a degradation in tracking precision for maneuvering targets and can even cause filter divergence.To address these challenges,we propose a joint tracking strategy that integrates an improved adaptive extended Kalman filter with the interacting multiple model(IMM)algorithm.First,numerical stabilization is applied to ensure the positive definiteness of the immediately estimated measurement covariance matrix.Second,a multidimen-sional vector is constructed as a control factor by comparing the theoretical estimates and the actual computed values of the filter innovation covariance,which enables dynamic perception of changes in measurement noise statistics.Suitable weighting coefficients are then assigned to each dimension for refined online correction of the measurement covariance matrix.Finally,the entire adaptive filtering module is deeply integrated with the IMM framework.This allows the algo-rithm to handle the dual uncertainties that arise from target maneuvers and environmental variations.The results of a simulation indicate that the proposed SHAEKF-IMM algorithm was able to capture and respond to nonstationary changes in measurement noise covariance more promptly and accurately compared to conventional algorithms while tracking maneuvering targets using radars with an external radiation source.The proposed approach significantly sup-pressed filtering errors caused by model mismatch through an effective online estimation and compensation mechanism.Thus,it demonstrated superior robustness and tracking accuracy across different maneuvering scenarios.关键词
自适应卡尔曼滤波算法/交互式多模型/机动目标跟踪/外辐射源雷达/控制因子Key words
adaptive Kalman filtering algorithm/interacting multiple model/tracking maneuvering targets/passive radar/control factors分类
信息技术与安全科学引用本文复制引用
程瑞,易建新,万显荣..基于改进SHAEKF算法的外辐射源雷达机动目标跟踪[J].信号处理,2025,41(12):1908-1916,9.基金项目
国家自然科学基金(61931015,62071335) （61931015,62071335）
中央高校自主研究项目(2042022dx0001) The National Natural Science Foundation of China(61931015,62071335) （2042022dx0001）
Fundamental Research Funds for the Central Universities(2042022dx0001) （2042022dx0001）
