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.<>
Application of neural networks in target tracking data fusion
IEEE Transactions on Aerospace and Electronic Systems ; 30 , 1 ; 281-287
1994-01-01
684181 byte
Article (Journal)
Electronic Resource
English
Application of Neural Networks in Target Tracking Data Fusion
Online Contents | 1994
|Data Fusion of Multi-Target Tracking in Wireless Sensor Networks
British Library Online Contents | 2006
|Target tracking and fusion in vehicular networks
IEEE | 2011
|Target Tracking and Fusion in Vehicular Networks
British Library Conference Proceedings | 2011
|Data fusion for ground moving target tracking
Online Contents | 2007
|