雷达学报2019，Vol.8Issue(3)：355-365,11.
杂波背景下基于概率假设密度的辅助粒子滤波检测前跟踪改进算法
Track-Before-Detect Algorithm Based on Improved Auxiliary Particle PHD Filter under Clutter Background
摘要
Abstract
Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives. In this study, the concept of two-layer particle is introduced. The Auxiliary Particle Filter (APF) based on Parallel Partition (PP) theory is applied to PHD-TBD. The Auxiliary Parallel Partition Particle Filter (which is based on APF and PP) Track-Before-Detect based on the Probability Hypothesis Density filter (APP-PF-PHD-TBD) algorithm is proposed to improve the target number and state estimation accuracy. The simulation results show that, compared with the existing PHD-filtering-based particle filter track-before-detect algorithm, the proposed algorithm has significant performance advantages in target number and state estimation accuracy. These advantages are particularly obvious in dense target scenarios. Finally, the sea clutter background data obtained using the navigation radar prove that the proposed algorithm outperforms the existing PHD-filtering-based particle filter track-before-detect algorithm in application.关键词
平行分割/ 辅助粒子滤波/ 概率假设密度/ 检测前跟踪/ 随机有限集/Key words
Parallel Partition (PP)/ Auxiliary Particle Filter (APF)/ Probability Hypothesis Density (PHD)/ Track-Before-Detect (TBD)/ Random Finite Set (RFS)/分类
信息技术与安全科学引用本文复制引用
裴家正,黄勇,董云龙,何友,陈小龙..杂波背景下基于概率假设密度的辅助粒子滤波检测前跟踪改进算法[J].雷达学报,2019,8(3):355-365,11.基金项目
国家自然科学基金(U1633122, 61871391, 61471382, 61531020, 61671462)，国防科技基金(2102024)，中国科协“青年人才托举工程”专项经费(YESS20160115) （U1633122, 61871391, 61471382, 61531020, 61671462）
