Robust sequential classification of tracks

We present a robust probabilistic method to classify targets based on their tracks. As is customary in supervised learning problems, it is assumed that example tracks from various classes are available to train a classifier. We present an optimal but computationally intensive sequential solution, and show that a computationally feasible naive Bayes approximation works better than ignoring sequential information. We show how to take into account the uncertainty of the track, as quantified by the error covariance matrix from a Kalman tracker, using the recently proposed expected maximum likelihood rule coupled with a robust local Bayesian discriminant analysis classifier. In addition, we propose an expected maximum a posterior rule to take test sample uncertainty into account for classifiers that model the posterior, and use it to define a robust kernel classifier. Simulations with a Kalman tracker show significantly improved performance by taking into account the tracked state covariance.