Information fusion and tracking of maneuvering targets with artificial neural network

Author

Zhongliang, Jing ; Hong, Xu ; Xueqin, Zhou

Author_Institution

Dept. of Autom. Control, Northwestern Polytech. Univ., Xian, China

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3403

Abstract

A novel algorithm for tracking manoeuvring targets is presented in this paper. This algorithm is implemented with a pair of parallel adaptive filters by the information fusion technique together with the current statistical model (CSM) and backpropagation (BP) neural network. In order to adapt to different cases of movement, BP network fuses all state information of both filters and adjusts the system variance for one of the filters according to the trained sample set. Computer simulation results show that this algorithm can successfully tracks manoeuvring targets over a wide range of conditions, and has a higher tracking precision

Keywords

adaptive filters; backpropagation; filtering theory; neural nets; sensor fusion; target tracking; tracking; tracking filters; backpropagation; information fusion; maneuvering target tracking; neural network; parallel adaptive filters; state information; statistical model; system variance; Acceleration; Adaptive filters; Artificial neural networks; Computer simulation; Filtering algorithms; Information filtering; Information filters; Neural networks; Partial response channels; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374783

Filename

374783