Title :
Maneuvering target tracking with non-Gaussian noise
Author :
Xiaoquan, Song ; Zhongkang, Sun
Author_Institution :
Inst. of Electron. Eng., Nat. Univ. of Defense Technol., Hunan, China
Abstract :
Tracking a maneuvering target in a nonlinear interference environment has been discussed in some literature, and this paper considers the problems of tracking in the non-Gaussian noise environment and then tracking with multiple passive sensors. The method of target state estimation in this paper is a dynamic programming approach. Unlike the traditional method, the estimating problem is reduced to a multiple hypothesis-testing problem, and then is solved by using the dynamic programming algorithm. The simulation results show the superiority of the new method to EKF
Keywords :
Kalman filters; digital simulation; dynamic programming; noise; signal processing; simulation; state estimation; target tracking; EKF; active sensors; dynamic programming; extended Kalman filter; glint noise; maneuvering target tracking; multiple hypothesis-testing; multiple passive sensors; nonGaussian noise; nonlinear interference environment; passive sensors; target state estimation; Additive noise; Additive white noise; Dynamic programming; Filters; Gaussian noise; Radar tracking; State estimation; Sun; Target tracking; Working environment noise;
Conference_Titel :
Aerospace and Electronics Conference, 1997. NAECON 1997., Proceedings of the IEEE 1997 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-3725-5
DOI :
10.1109/NAECON.1997.622746
