Multi-sensor Data Fusion And The Application In The State Estimation Of Maneuvering Target

Multi-sensor data fusion can synthesize the spatio-temporal redundancy and complementary information according to some algorithms and criterions based on exploiting sufficiently multi-sensor measuring information, in the course of this synthesizing process, the accuracy and reliability and survivability of state estimation for maneuvering target are enhanced greatly. Based on above performances the applications of multi-sensor data fusion in state estimation for maneuvering target is studied systemically. The main work includes:Based on the analysis that the extreme value of acceleration presupposed causes influence in the"current"statistical model, a modified model is given, which utilizes the functional relationship between maneuvering status and estimation of the neighboring intersample position vector to carry out the self-adaptive of the process noise variance. Then combining with the recursive characteristic of Kalman filter, an improved self-adaptive filtering algorithm is presented.In some algorithms based on Kalman filter and its extension, the presupposition of measurement variance leads to the descent of state estimation accuracy. A self-adaptive estimation algorithm of measurement variance is presented based on spatio-temporal analysis and LSE, which make full use of redundancy and complementary information from the single sampled data of multi-sensors and the multi-times sampled data of single sensor. Furthermore, utilizing the characteristic that filtering error covariance expresses filtering precision and the principle of information conservation, the dynamic and reasonable distribution of distributed tracks weight coefficient is accomplished.Jerk model and strong tracking filter is organically assembled, and based on spatio-temporal synthetically analysis and LME, a self-learning estimation method of the system measurement variance is given. The method improves obviously the...