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Joint Target Tracking and Classification via Sequential Monte Carlo Filtering.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

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Abstract

A sequential Monte Carlo algorithm is suggested for joint maneuvering target tracking and classification, based on kinematic measurements. A mixture Kalman filter is designed for two-class identification of air targets: commercial and military aircraft. Speed and acceleration constraints are imposed on the target behaviour models in order to improve the classification process. The class is modeled as an independent random variable, which can take values over the discrete class space with an equal probability. As a result, the multiple-model structure in the class space, required for reliable classification, is achieved. The performance of the proposed algorithm is evaluated by simulation over typical target scenarios.