DIM TARGET DETECTION USING HIGH-ORDER CORRELATION METHOD

This paper presents a new method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high order correlation method is developed which recursively computes the spatio-temporal cross-correlations between data of several consecutive scans. The implementation of this scheme using a connectionist network is also presented. Several important properties of the high order correlation method are established which indicate that the resultant filtered images capture all the target information. The simulation results using this approach show at least 93% clutter rejection. Further improvement in the clutter rejection rate is achieved by modifying the high order correlation method to incorporate the target motion dynamics. The implementation of this modified high order correlation using a high order neural network architecture is demonstrated. The simulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here and the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are provided.