Self partitioning neural networks for target recognition

Automatic target recognition (ATR) is a domain in which the neural network technology has been applied with limited success. The domain is characterized by large training sets with dissimilar target images carrying conflicting information. This paper presents a novel method for quantifying the degree of non-cooperation that exists among the target members of the training set. Both the network architecture and the training algorithm are considered in the computation of the non-cooperation measures. Based on these measures, the self partitioning neural network (SPNN) approach partitions the target vectors into an appropriate number of groups and trains one subnetwork to recognize the targets in each group. A fusion network combines the outputs of the subnetworks to produce the final response. This method automatically determines the number of subnetworks needed without excessive computation. The subnetworks are simple with only one hidden layer and one unit in the output layer. They are topologically identical to one another. The simulation results indicate that the method is robust and capable of self organization to overcome the ill effects of the non-cooperating targets in the training set. The self partitioning approach improves the classification accuracy and reduces the training time of neural networks significantly. It is also shown that a trained self partitioning neural network is capable of learning new training vectors without retraining on the combined training set (i.e., the training set consisting of the previous and newly acquired training vectors).