Tracking point-source targets in IR noise with neural- network-aided Kalman filter

Guan Hua; Yun Hu; Zhenkang Shen; Zhongkang Sun

doi:10.1117/12.217692

1 September 1995 Tracking point-source targets in IR noise with neural- network-aided Kalman filter

Guan Hua, Yun Hu, Zhenkang Shen, Zhongkang Sun

Proceedings Volume 2561, Signal and Data Processing of Small Targets 1995; (1995) https://doi.org/10.1117/12.217692
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

Guan Hua, Yun Hu, Zhenkang Shen, and Zhongkang Sun "Tracking point-source targets in IR noise with neural- network-aided Kalman filter", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); https://doi.org/10.1117/12.217692

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