BIASED BEARINGS-ONLY PARAMETER ESTIMATION FOR BISTATIC SYSTEM  

作　　者：Xu Benlian Wang Zhiquan 

机构地区：[1]Department of Information and Control Engineering, Changshu Institute of Technology,Changshu 215500, China [2]School of Automation, Nanjing University of Science ＆ Technology, Nanjing 2100Y4, China

出　　处：《Journal of Electronics(China)》2007年第3期326-331,共6页电子科学学刊（英文版）

基　　金：the Natural Science Foundation of Jiangsu Province, China (BK2004132).

摘　　要：According to the biased angles provided by the bistatic sensors, the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally, Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true, and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore, the estimated results can achieve their Cramer-Rao tow bounds.According to the biased angles provided by the bistatic sensors,the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed,respectively. Additionally,a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally,Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true,and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore,the estimated results can achieve their Cramer-Rao low bounds.

关 键 词：BIAS BEARINGS-ONLY Bistatic system Kalman filter Parameter estimation 

分 类 号：O231.2[理学—运筹学与控制论]