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1 September 1995 Tracking point-source targets in IR noise with neural- network-aided Kalman filter
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);


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