Application of neural networks in target tracking data fusion

Author

Chin, L.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ.

Volume

30

Issue

1

fYear

1994

fDate

1/1/1994 12:00:00 AM

Firstpage

281

Lastpage

287

Abstract

Kalman filtering is a fundamental building block of most multiple-target tracking (MTT) algorithms. The other building block usually involves some type of data association schemes. Here it is proposed to incorporate a neural network into the normal Kalman filter configuration such that the neural network provides the adaptive capability the filter needs. As such the estimation error of the Kalman filter would be reduced, hence improving the MTT solution. Simulation results have shown that this claim is valid

Keywords

Kalman filters; adaptive filters; digital simulation; learning (artificial intelligence); military systems; neural nets; probability; sensor fusion; tracking; Kalman filtering; adaptive filters; data association; estimation error; military surveillance; multiple-target tracking algorithms; neural networks; simulation; target tracking data fusion; Arithmetic; Background noise; Filtering; Filtering algorithms; Infrared sensors; Kalman filters; Neural networks; Noise measurement; Particle tracking; Radar tracking; Sensor phenomena and characterization; Signal processing algorithms; Target tracking;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.250437

Filename

250437