Improving contact classification by incorporating an estimate of aspect from track state

Abstract

This paper presents a method for using information from tracking to improve the results of contact classification. An Extended Kalman Filter is used to predict the target's state (position and velocity) at the current time. The predicted state is used to estimate the target's aspect and heading. The estimate is used in tandem with aspect-dependent features (Doppler and target strength) to classify contacts as targets or clutter. Results on three simulated datasets show that using the velocity estimate and the covariance from the track state results in increased classification accuracy.