Target tracking by neural network maneuver modeling

A new approach to tracking a maneuvering target using a neural network-based scheme is developed. The neural network models the target manoeuvre and assists a Kalman filter in updating its gains in order to generate correct estimates of target position and velocity. A performance evaluation of the target tracking scheme is conducted under various interesting scenarios. The parallel processing capabilities of trained neural nets are exploited in this application for realistically handling more input features to correct for the bias induced by the target manoeuvre.<>