SMC-PHD based multi-target track-before-detect with nonstandard point observations model  

作　　者：占荣辉 高彦钊 胡杰民 张军 

机构地区：[1]Science and Technology on Automatic Target Recognition Laboratory,National University of Defense Technology

出　　处：《Journal of Central South University》2015年第1期232-240,共9页中南大学学报（英文版）

基　　金：Projects(61002022,61471370)supported by the National Natural Science Foundation of China

摘　　要：Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio（SNR）. A modified multi-target track-before-detect（TBD） method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo（SMC）-based probability hypothesis density（PHD） filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

关 键 词：adaptive particle sampling multi-target track-before-detect probability hypothesis density（PHD） filter sequential Monte Carlo（SMC） method 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]