Extended Object Tracking and Classification Using Radar and ESM Sensor Data

Extended object tracking and classification (EOTC) using multisensor kinematic and attribute data is a challenging and highly coupled problem. It is a joint decision and estimation (JDE) problem. A good solution has to handle effectively the coupling between tracking and classification and also make good use of multisensor data. For this purpose and based on the recently proposed JDE framework, this letter proposes a conditional JDE (CJDE) risk, which integrates the tracking error and the classification cost using heterogeneous sensor data. Object classes differ from each other in maneuverability and feature attribute. A suitable model is identified and used to describe object classes differing in maneuverability. Also presented are attribute evolution and measurement models. Then, an EOTC algorithm optimizing the CJDE risk is proposed, which considers the coupling and the information contained in various data. Simulation results demonstrate the superiority of the proposed EOTC algorithm in joint performance.

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