Admissible model noise upper bound with constraint of stochastic passage characteristics

作     者：Guoqing Qi Yinya Li Li Chen Andong Sheng 

作者机构：Automation School Nanjing University of Science and Technology Nanjing 210094 P. R. China Artillery & Air Defence Forces Institute of Equipment & Technologies Headquarters of the General Equipment of PLA Beijing 100012 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2011年第22卷第4期

页      面：565-571页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 0823[工学-交通运输工程] 

基　　金：supported by the Science and Technology Development Fund of Nanjing University of Science and Technology(NUST)(XKF09020) NUST Research Fund(2010GJPY067,2010ZYTS050) the National Natural Science Foundation of China(60804019) 

主　　题：stochastic passage characteristic target area modelnoise bi-linear matrix inequality (BMI). 

摘      要：In some object tracking systems, the moving object future position is an area （i.e., target area）. It is a successful estimation strategy if the predicted points fall in the target area. If the object makes a sudden maneuvering, the prediction may get out of the target area easily which may make the tracking system lose the object. The aim is to investigate the admissible maximum object maneuvering intensity, which is characterized as model noise variance, for such kind of tracking system. Firstly, the concept of stochastic passage characteristics over the boundary of target area and their relationship with prediction error variance are described. Secondly, the consistency among the indices of regional pole, prediction error variance and stochastic passage characteristics is analyzed. Thirdly, the multi-indices constraints are characterized by a set of bi-linear matrix inequalities （BMIs）. Then, the admissible maximum model noise variance and the satisfactory estimation strategy are presented by iteratively solving linear matrix inequalities （LMIs） to approximate BMIs. Finally, a numerical example is proposed to demonstrate the obtained resuits.