Adaptive target state estimation using neural networks

Development of an adaptive target state estimation algorithm for use with advanced missile guidance laws is presented. The target state estimator employs a linear neural network as the decision-making element in a nine-state dynamic model of the target. A Kalman filtering algorithm is used to estimate the neural network weights and the target states. The estimator performance is evaluated in a point-mass nonlinear simulation of missile-target engagement for several different engagement scenarios. This simulation incorporates error models of the seeker and the on-board inertial navigation system. Comparison of the neural network target state estimator performance with a conventional target state estimator reveals that the adaptive estimator provides more accurate estimates of the target states with minimal lag