Performance bounds of extended target tracking in cluttered environments  

作　　者：MENG HuaDong ZHONG ZhiWen WANG XiQin 

机构地区：[1]Department of Electronic Engineering, Tsinghua University

出　　处：《Science China(Information Sciences)》2013年第7期114-125,共12页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China (Grant No. 60901057);in part by National Basic Research Program of China (Grant No. 2010CB731901)

摘　　要：In this paper, we consider the problem of the performance bound of a nonlinear filtering problem corresponding to tracking an extended target in cluttered environments （i.e., with false alarms and missed detections）. The high resolution sensor obtains the measurements of the position and the extent of the extended target whose shape is modeled by an ellipse. The posterior Cramer-Rao lower bound （PCRLB） provides a useful tool to evaluate the best achievable performance of the nonlinear filtering problem. The bounds of the traditional kinematic state estimation are calculated using the point and the extended target model. It is shown in this paper that the bound of extended target tracking is smaller than that of point model because more information is utilized. The bounds are calculated to examine the influence of the measuring accuracy, the geometry between the sensor and the target, the prior knowledge of the target, and the environmental circumstance. In a cluttered environment, the PCRLB is calculated by IRF （information reduction factor）, MSC （measurement sequence conditioning）, and MESC （measurement existence sequence conditioning） approaches. The simulation results also illustrate the relationship of the three methods.In this paper, we consider the problem of the performance bound of a nonlinear filtering problem corresponding to tracking an extended target in cluttered environments （i.e., with false alarms and missed detections）. The high resolution sensor obtains the measurements of the position and the extent of the extended target whose shape is modeled by an ellipse. The posterior Cramer-Rao lower bound （PCRLB） provides a useful tool to evaluate the best achievable performance of the nonlinear filtering problem. The bounds of the traditional kinematic state estimation are calculated using the point and the extended target model. It is shown in this paper that the bound of extended target tracking is smaller than that of point model because more information is utilized. The bounds are calculated to examine the influence of the measuring accuracy, the geometry between the sensor and the target, the prior knowledge of the target, and the environmental circumstance. In a cluttered environment, the PCRLB is calculated by IRF （information reduction factor）, MSC （measurement sequence conditioning）, and MESC （measurement existence sequence conditioning） approaches. The simulation results also illustrate the relationship of the three methods.

关 键 词：PCRLB extended target extended target tracking tracking performance bound nonlinear filtering elliptical target model 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TN911.7[自动化与计算机技术—控制科学与工程]