基于AI-AP-PHD滤波的多目标跟踪方法

谭顺成; 韩芳林; 于洪波

doi:10.15892/j.cnki.djzdxb.2021.04.020

弹箭与制导学报 >

2021 , Vol. 41 >Issue 4: 90 - 94

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2021.04.020

基于AI-AP-PHD滤波的多目标跟踪方法

谭顺成 ¹^,² ,
韩芳林 ¹ ,
于洪波 ¹

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¹ 海军航空大学信息融合研究所, 山东烟台 264001
² 南京电子技术研究所, 南京 210039

谭顺成(1982—),男,湖南湘潭人,讲师,博士,研究方向:信息融合、雷达数据处理。

收稿日期: 2020-11-04

网络出版日期: 2025-02-13

基金资助

国家自然科学基金(61671462)

国家自然科学基金(61731023)

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Multi-target Tracking Based on AI-AP-PHD Filter

TAN Shuncheng ¹^,² ,
HAN Fanglin ¹ ,
YU Hongbo ¹

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¹ Institute of Information Fusion Technology, Naval Aeronautical University, Shandong Yantai 264001, China
² Nanjing Research Institute of Electronic Technology, Nanjing 210039, China

Received date: 2020-11-04

Online published: 2025-02-13

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摘要

目标的幅值信息可提供更多精确的目标和杂波似然函数信息,结合其对多目标进行跟踪可有效改善传感器的检测跟踪性能,构造了一种实际可行的结合目标幅值信息的辅助粒子PHD(AI-AP-PHD)滤波方法。该方法首先对目标状态向量和量测向量进行扩展,然后通过扩展的动态方程和量测方程实现系统的预测和更新,同时给出多目标的数量和状态估计。仿真结果表明,该方法非常适应于目标信噪比未知的情况,且性能优于传统的PHD滤波方法。

关键词： 辅助粒子滤波; 概率假设密度滤波; 幅度信息

本文引用格式

谭顺成 , 韩芳林 , 于洪波 . 基于AI-AP-PHD滤波的多目标跟踪方法[J]. 弹箭与制导学报, 2021 , 41(4) : 90 -94 . DOI: 10.15892/j.cnki.djzdxb.2021.04.020

Abstract

The amplitude information of target can provide more accurate target and false-alarm likelihoods, which can be used to improve the performance of multi-target detection and tracking. In this paper, a practical and feasible auxiliary particle probability hypothesis density filter combined with target amplitude information (AI-AP-PHD) is proposed. Firstly, the target state vector and measurement vector are expanded, and then the extended dynamic equation and measurement equation are used to realize the prediction and update of system, the number and state estimation of multi-target are given simultaneously in the end. The simulation results show that the proposed method is very suitable for the situation of unknown target SNR, and perform well than the traditional PHD filter method.

Key words： auxiliary particle filter; probability hypothesis density filter; amplitude information

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参考文献

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