基于标签多伯努利滤波的衍生目标跟踪算法

doi:10.7527/S1000-6893.2015.0103

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基于标签多伯努利滤波的衍生目标跟踪算法

邱昊, 黄高明, 左炜, 高俊

海军工程大学电子工程学院, 武汉 430033

收稿日期:2015-01-04 修回日期:2015-04-12 出版日期:2015-09-15 发布日期:2015-05-04
通讯作者: 黄高明男, 博士, 教授。主要研究方向: 雷达/电子战信号处理、盲信号处理、无源探测、电子战系统仿真与效能评估。 Tel: 027-65461241 E-mail: hgaom_paper@163.com E-mail:hgaom_paper@163.com
作者简介:邱昊男, 博士研究生。主要研究方向: 多目标跟踪技术。 Tel: 027-65461870 E-mail: qhcs01@163.com;左炜男, 博士, 讲师。主要研究方向: 雷达信号处理、电子对抗。 Tel: 027-65461870 E-mail: zwei_wh@163.com;高俊男, 博士, 教授。主要研究方向: 数字信号处理、数字通信、短波无线通信。 Tel: 027-65461226 E-mail: gaojunnj@163.com
基金资助:
国家"863"计划 (2014AAXXX4061)

Spawned target tracking algorithm based on labeled multi-Bernoulli filtering

QIU Hao, HUANG Gaoming, ZUO Wei, GAO Jun

College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Received:2015-01-04 Revised:2015-04-12 Online:2015-09-15 Published:2015-05-04
Supported by:
National High-tech Research and Development Program of China (2014AAXXX4061)

摘要/Abstract

摘要：

针对现有随机有限集(RFS)滤波器在低信噪比环境下对衍生目标跟踪性能严重下降的问题,提出了一种基于Delta扩展标签多伯努利(δ-GLMB)滤波器的改进算法。基于随机集理论和伯努利衍生模型,推导了新的预测方程,并采用了假设裁剪及分组手段和多伯努利近似技术以降低算法的计算量。针对假设增多引起的虚警问题,将多帧平滑思想和算法相结合,利用标签信息对新目标进行回溯处理。仿真结果表明,所提算法能对目标数目进行无偏估计,在低探测概率和强杂波环境下性能明显优于概率假设密度(PHD)算法,计算开销在衍生初始阶段增长快于PHD,目标较分散时低于PHD。

关键词: 目标跟踪, 随机有限集, 标签多伯努利, 衍生目标, 序贯蒙特卡罗

Abstract:

For the problem that the performance of existing random finite set (RFS) based filters serious degrades when tracking spawned targets in low signal-to-noise ratio condition, an improved algorithm based on δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. According to RFS theory and Bernoulli spawning model, a new predicted formulation is derived. Hypotheses pruning and grouping method along with Bernoulli approximation are adopted to cut down the computational load. To reduce the false alarms introduced by extra hypotheses, the multi-frame smooth method is employed through a new target confirmation step with label information. Simulations show that the proposed method can provide unbiased estimation of cardinality, and significant outperforms the probability hypothesis density (PHD) filter in the low detection probability and dense clutter environment. The computational cost of proposed algorithm grows faster than PHD in the early stage of spawning, while is lower than PHD when targets separate well.

Key words: target tracking, random finite set, labeled multi-Bernoulli, spawned target, sequential Monte Carlo

中图分类号:

V243
TP391

邱昊, 黄高明, 左炜, 高俊. 基于标签多伯努利滤波的衍生目标跟踪算法[J]. 航空学报, 2015, 36(9): 3012-3019.

QIU Hao, HUANG Gaoming, ZUO Wei, GAO Jun. Spawned target tracking algorithm based on labeled multi-Bernoulli filtering[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(9): 3012-3019.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于标签多伯努利滤波的衍生目标跟踪算法

Spawned target tracking algorithm based on labeled multi-Bernoulli filtering

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