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Proceedings Paper

Tracking point-source targets in IR noise with neural- network-aided Kalman filter
Author(s): Guan Hua; Yun Hu; Zhenkang Shen; Zhongkang Sun
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Paper Abstract

This paper describes a Neural Network (NN) aided Kalman Filter (KF) for tracking Point- Source Target in IR images. To improve the Kalman estimates and tracking accuracy, we introduce multi-layer backpropagation neural networks into the normal Kalman filter. The performance improvement of NNKF estimations with quantization noise presence has been investigated. This NNKF uses the coordinates of the detected targets in every frame as the measurement data, and estimates the targets' motion parameters which are used as the decision statistics for rejecting/maintaining a target. If the parameters related to an individual possible target have gone beyond a given bound, this `target' will be set aside, and related tracking ended. Simulation results have shown that the performance and accuracy of the NNKF tracker have been improved a lot than that without the aid of neural networks.

Paper Details

Date Published: 1 September 1995
PDF: 9 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217692
Show Author Affiliations
Guan Hua, National Univ. of Defense Technology (China)
Yun Hu, National Univ. of Defense Technology (China)
Zhenkang Shen, National Univ. of Defense Technology (China)
Zhongkang Sun, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 2561:
Signal and Data Processing of Small Targets 1995
Oliver E. Drummond, Editor(s)

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