一种新的基于概率理论的概率数据互联滤波器

刘宗香; 谢维信; 黄敬雄

doi:10.3724/SP.J.1146.2008.00796

一种新的基于概率理论的概率数据互联滤波器

doi: 10.3724/SP.J.1146.2008.00796

基金项目:

国家部级基金和深圳大学科研启动基金(200640)资助课题

计量
- 文章访问数: 3393
- HTML全文浏览量: 117
- PDF下载量: 819
- 被引次数: 0
出版历程
- 收稿日期: 2008-06-23
- 修回日期: 2009-03-23
- 刊出日期: 2009-07-19

A New Probabilistic Data Association Filter Based on Probability Theory

摘要

摘要: 该文从理论上分析了用于目标跟踪的概率数据互联滤波器(PDAF)和联合概率数据互联滤波器(JPDAF)存在的不足，提出了一种新的概率数据互联滤波器(NPDAF)。NPDAF在数据关联时基于概率理论：一个测量可能源于目标，也可能源于杂波，但其源于目标的概率与其源于杂波的概率之和应为1。同时，给出了跟踪过程中NPDAF的数据互联模型及滤波器的实现方法。该实现方法首先计算测量与各目标的关联概率，然后用概率对跟踪滤波器的增益加以修正。仿真实验表明，在对多目标进行跟踪时NPDAF的性能优于JPDAF。
- 目标跟踪;概率理论;概率数据互联滤波器;联合概率数据互联滤波器
Abstract: The Probabilistic Data Association Filter (PDAF) and the Joint Probabilistic Data Association Filter (JPDAF) are theoretically analyzed and their shortages in theory are pointed out. Based on the probability theory, a New Probabilistic Data Association Filter (NPDAF) is proposed, in which a measurement may originate from targets or a clutter, but the sum of the probabilities originating from targets and a clutter is equal to 1. Also in the paper, the mathematical model for data association in target tracking and the realization technique for NPDAF are presented. The　correlative probabilities between a measurement and targets are first computed in the realization technique, then the gain of a tracking filter is modified using the correlative probability. Simulation results show that the performance of NPDAF is better than that of JPDAF in multiple target tracking.

HTML全文

参考文献(1)

何友, 王国宏等. 多传感器信息融合及应用[M]. 北京: 电子工业出版社, 2000, 第6 章, 第10 章.[2]Wang Xue-zhi and Musicki D. Low elevation sea-surfacetarget tracking using IPDA type filters[J].IEEE Transactionson Aerospace and Electronic Systems.2007, 43(2):759-774[3]Kirubarajan T, Bar-Shalom Y, and Lerro D. Bearings-onlytracking of maneuvering targetsusing a batch-recursiveestimator[J].IEEE Transactions on Aerospace and ElectronicSystems.2001, 37(3):770-780[4]刘源, 谢维信, 许录平. 多传感器模糊-概率交互作用的数据互联算法[J]. 电子学报, 1999, 27(12): 30-34.Liu Yuan, Xie Wei-xin, and Xu Lu-ping. Multisensor fuzzyprobabilityinteracting data association algorithm[J]. ActaElectronica Sinica, 1999, 27(12): 30-34.[5]张晶炜, 修建娟, 何友, 熊伟. 基于D-S 理论的分布交互式多传感器联合概率数据互联算法[J]. 中国科学E 辑信息科学,2006, 36(2): 182-190.[6]Puranik S and Tugnait J K. Tracking of multiplemaneuvering targets using multiscan JPDA and IMMfiltering[J].IEEE Transactions on Aerospace and ElectronicSystems.2007, 43(1):23-35[7]Blom H A P and Bloem E A. Bayesian tracking of twopossibly unresolved maneuvering targets[J].IEEETransactions on Aerospace and Electronic Systems.2007,43(2):612-627[8]Savage C R and Morran B. Waveform selection ofmaneuvering targets within an IMM framework[J].IEEETransactions on Aerospace and Electronic Systems.2007,43(3):1205-1214[9]Sinha A, Kirubarajan T, and Bar-Shalom Y. Application ofthe Kalman-Levy filter for tracking maneuvering targets[J].IEEE Transactions on Aerospace and Electronic Systems.2007, 43(3):1099-1107[10]李良群, 姬红兵, 罗军辉. 杂波环境下被动多传感器机动目标跟踪新算法[J]. 电子与信息学报, 2007, 29(8): 1838-1840.Li Liang-qun, Ji Hong-bing, and Luo Jun-hui. Maneuveringtarget tracking algorithm with multiple passive sensors inclutter environment. Journal of Electronic InformationTechnology, 2007, 29(8): 1838-1840.

施引文献

资源附件(0)

访问统计