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 decisionmaking 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 onboard 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.