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 network s. A high order correlation method is developed which recursively comp utes the spatio-temporal cross-correlations between data of several co nsecutive scans. The implementation of this scheme using a connectioni st network is also presented. Several important properties of the high order correlation method are established which indicate that the resu ltant filtered images capture all the target information. The simulati on results using this approach show at least 93% clutter rejection. Fu rther improvement in the clutter rejection rate is achieved by modifyi ng the high order correlation method to incorporate the target motion dynamics. The implementation of this modified high order correlation u sing a high order neural network architecture is demonstrated. The sim ulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here a nd the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are provided.