Joint tracking and classification of non-ellipsoidal extended object using random matrix

Many practical extended objects have non-ellipsoidal extensions. Within the random-matrix framework, a non-ellipsoidal extended object (NEO) can be approximated by multiple ellipsoidal sub-objects, each described by a random matrix. NEOs of different classes have different structures determining the relationship among the sub-objects. For effective classification of NEOs, this structural information should be incorporated into the NEO models in different classes for modelbased classifiers. For joint tracking and classification of a NEO using a random matrix, we propose a Bayesian framework that jointly estimates the sub-object states and extensions and obtains the probability mass function of the object class. Utilizing the structural information, the kinematic states and extensions of the sub-objects of a NEO are related to the kinematic state and extension of one reference ellipsoidal object. As such, the dynamics of a NEO can be described by a single model. Furthermore, NEOs of different classes are characterized by such models. Both the derived estimator for tracking and the classifier have a simple form. Simulation results demonstrating the effectiveness of the proposed approach are given.