基于数据压缩的多传感器 PHD 滤波算法

谭顺成; 王国宏; 徐海全; 王娜

doi:10.15892/j.cnki.djzdxb.2011.02.024

2011 , Vol. 31 >Issue 2: 161 - 164

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2011.02.024

摘要

针对多传感器多目标跟踪,提出一种基于数据压缩的多传感器概率假设密度(PHD)滤波算法,解决串行多传感器PHD(SMSPHD)滤波计算量过大的问题。算法首先利用数据压缩将多传感器量测数据转换成等效的单传感器量测数据,然后在此基础上进行PHD滤波。仿真结果表明,该算法可以实现对多目标的有效跟踪;此外,随传感器数目的增加,该算法增加的计算量约为SMSPHD滤波算法增加的4.3%。

关键词： 数据压缩; 概率假设密度; 多传感器; 多目标跟踪

本文引用格式

谭顺成 , 王国宏 , 徐海全 , 王娜 . 基于数据压缩的多传感器 PHD 滤波算法[J]. 弹箭与制导学报, 2011 , 31(2) : 161 -164 . DOI: 10.15892/j.cnki.djzdxb.2011.02.024

Abstract

For multi-sensor multi-target tracking, a novel multi-sensor probability hypothesis density (PHD) filter based on data compression was proposed to solve the computation load of the serial multi-sensor PHD (SMSPHD) filter. With the proposed method, firstly, the multi-sensor measurements were equivalently converted to those from a single sensor by using data compression, then, the PHD filter was executed. The simulation results demonstrate that the proposed method can realize the tracking of multiple targets effectively. Moreover, as the increasing of the number of sensors, the added computational complexity of the proposed method is about 4.3% of that of the SMSPHD.

Key words： data compressing; probability hypothesis density; multi-sensor; multi-target tracking

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