密集杂波环境下数据融合估计算法评估

孙焱; 马存宝; 陈杰

doi:10.15892/j.cnki.djzdxb.2013.03.003

2013 , Vol. 33 >Issue 3: 171 - 173,180

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2013.03.003

摘要

由于观测量获取的不确定性,使得密集杂波环境下的多传感器机动目标跟踪变得复杂,解决这个问题的关键在于跟踪门限和数据联合与融合算法的选取。文中在分析最邻近卡尔曼滤波(NNKF)和概率数据联合滤波(PDAF)两种融合算法的基础上,对其在密集杂波环境下的跟踪误差和算法实时性效果采用当前统计模型进行评估,仿真结果表明PDAF算法跟踪性能明显优于NNKF算法。

关键词： 数据关联; 数据融合; 目标跟踪; 密集杂波环境

本文引用格式

孙焱 , 马存宝 , 陈杰 . 密集杂波环境下数据融合估计算法评估[J]. 弹箭与制导学报, 2013 , 33(3) : 171 -173,180 . DOI: 10.15892/j.cnki.djzdxb.2013.03.003

Abstract

Target tracking in a cluttered environment is complex due to uncertainty of observation acquirement. The key to this problem is to choose appropriate tracking gate and fusion algorithm. Based on the analysis on two data fusion algorithms, e. g. nearest neighbor Kalman filter (NNKF) and probabilistic data association filter (FDAF), the track error and real-time ability of these approaches in cluttered environment were evaluated by current statistical model in this paper. The simulation results show that PDAF performs better than NNKF in the cluttered environment.

Key words： data association; data fusion; target tracking; cluttered environment

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