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On the sensor order in sequential integrated probability data association filter

science china(information sciences) 2012年第3期55卷 491-500页

作者： WANG Yang1 , jing zhongliang1, hu {2.1, zhang hongjian2 & WU jingjing1 1school of Aeronautics and Astronautics, shanghai jiao tong university, shanghai 2.02.0, china 2school of electronic, information and electrical engineering, shanghai jiao tong university, shanghai 2.02.0, china 1. school of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai 200240 China2. School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China

The processing order of sensors with different detection probabilities and in different clutter densities in a multi-sensor system is investigated in this paper. A sequential implementation of the integrated probability data association (IpDA) algorithm under random set framework is derived. Under the assumptions of different detection probabilities and different clutter densities of individual sensor in a multi-sensor system, we reach the conclusion that the sequential IpDA filter depends on the order analyzing the target existence probability of varying sensor orders. Moreover, we obtain the optimal order of sensors for the sequential IpDA filter in terms of maximizing the target existence probability. The conclusions are demonstrated by simulation results.

关键词： sequential integrated probability data association random finite set optimal sensor order

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Tracks Extraction of the probability Hypothesis Density Filter for Survival Targets

Tracks Extraction of the Probability Hypothesis Density Filt...

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第二十七届中国控制会议

作者： zhang hongjian 1,jing zhongliang 2.hu shiqiang 2.1.school of electronic,information and electrical engineering shanghai jiao tong university shanghai 2.02.0,p.r.china 2.institute of aerospace science&technology shanghai jiao tong university shanghai,2.02.0,p.r.china

The outcome of probability Hypothesis Density or Cardinalized probability Hypothesis Density filter is a random finite set at each time ***,the interesting thing in practice is the tracks of survived *** order to get the tracks of survived targets,data association technique should be used to pair existing tracks with the elements of the outcome random finite set at each time *** this paper,a data association approach based on the Wasserstein Distance for performance evaluation of multi-target tracking filter is ***’transportation matrix’of the Wasserstein Distance is obtained through an optimal assignment algorithm,and the data association matrix can be obtained from the’transportation matrix’.Simulation reveals that data association approach is successful.

关键词： random Finite Set Gaussian Mixture probability Hypothesis Density Data Association

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