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Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information

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This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a priori information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the process of classification. The two designed estimators are compared and evaluated over a rather complex target trajectory. The results are demonstrating the usefulness of the proposed scheme for the incorporation of an additional speed information. Both filters illustrate the opportunity of the particle filtering and MKF to incorporate constraints in a natural way, providing reliable tracking and correct classification.