Multiple Extended Object Tracking Using PMHT with Extension-Dependent Measurement Numbers

For multiple extended object tracking (MEOT), data association and object extension estimation are key problems, and the number of measurements generated by each object plays a key role in both problems. For data association, probabilistic multiple hypothesis tracking (PMHT) naturally assumes multiple measurements can be assigned to a single object and has a linear computation complexity, and thus it fits MEOT well. In existing PMHT approaches to MEOT, the measurement number is usually used for data association only, not for direct extension estimation. Since the measurement number contains the extension information, e.g., an object with a bigger extension tends to generate more measurements given the sensor resolution, utilizing the measurement number for extension estimation is expected to improve the tracking performance. This paper proposes a PMHT approach combined with a random-matrix model using extension-dependent measurement numbers. The proposed approach derives a new auxiliary function of which the likelihood function part reflects the extension information contained in the measurement number. Then, using Expectation-Maximization, analytical forms of iteration formulae for kinematic states and extensions of multiple objects can be obtained approximately. Since more information is considered, the tracking performance of MEOT is improved, especially in extension estimation. Simulation results demonstrate the effectiveness of the proposed approach.