Dim target detection and clutter rejection using modified high order correlation neural network

The authors present a scheme for detecting dim moving targets in highly cluttered background from infrared (IR) data. A high-order spatiotemporal correlation scheme developed by R.J. Liou et al. (1991) to extract the sequency information carried by a target track and reject the background clutter is modified to incorporate target motion dynamics. More than 97% clutter rejection is achieved without losing the target information. In addition, a scoring process is used as a post processor to assign velocity and curvature dependent scores to all the possible target windows. The clutter rejection rate can further be improved by examining the scores of the windows using a back-propagation decision making network. Simulation results are also presented