Modified maximum likelihood registration based on information fusion  

作　　者：祁永庆 敬忠良 胡士强 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200030 [2]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200030 Institute of Aerospace Science and Technology,Shanghai Jiao Tong University,Shanghai 200030

出　　处：《Chinese Optics Letters》2007年第11期639-641,共3页中国光学快报（英文版）

基　　金：This work was jointly supported by the Basic Research Project of China (No.A1420060161);the National Natural Science Foundation of China (No.60674107);the Natural Science Foundation of Hebei Province (No.F2006000343);the National Aviation Cooperation Research Foundation of China (No.10577012).

摘　　要：The bias estimation of passive sensors is considered based on information fusion in multi-platform multisensor tracking system. The unobservable problem of bearing-only tracking in blind spot is analyzed. A modified maximum likelihood method, which uses the redundant information of multi-sensor system to calculate the target position, is investigated to estimate the biases. Monte Carlo simulation results show that the modified method eliminates the effect of unobservable problem in the blind spot and can estimate the biases more rapidly and accurately than maximum likelihood method. It is statistically efficient since the standard deviation of bias estimation errors meets the theoretical lower bounds.The bias estimation of passive sensors is considered based on information fusion in multi-platform multisensor tracking system. The unobservable problem of bearing-only tracking in blind spot is analyzed. A modified maximum likelihood method, which uses the redundant information of multi-sensor system to calculate the target position, is investigated to estimate the biases. Monte Carlo simulation results show that the modified method eliminates the effect of unobservable problem in the blind spot and can estimate the biases more rapidly and accurately than maximum likelihood method. It is statistically efficient since the standard deviation of bias estimation errors meets the theoretical lower bounds.

关 键 词：Computer simulation Image sensors Maximum likelihood Monte Carlo methods 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]