Abstract
More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity.
Highlights
IntroductionMulti-target tracking involves estimating the current state (e.g., position, speed, etc.) of targets using the measurements from multiple targets
Multi-target tracking involves estimating the current state of targets using the measurements from multiple targets
In most target tracking cases, it is assumed that at most one measurement is produced by the target per scan, but this is not true in some cases, e.g., more measurements per scan are potentially generated by the target when it is detected by a high resolution sensor, or there are more than one measurement source on the target surface
Summary
Multi-target tracking involves estimating the current state (e.g., position, speed, etc.) of targets using the measurements from multiple targets. The problem of how to accurately and effectively partition the measurements of the extended target with clutter remains unsolved. A novel measurement partitioning algorithm based on affinity propagation clustering for multiple extended target tracking is proposed in this paper. The elliptical gating technique is introduced to remove the clutter measurements, and the affinity propagation clustering algorithm is used to partition the measurements, and the ET-GM-PHD filter is implemented for tracking multiple extended targets. The reminder of this paper is organized as follows: in Section 2, the measurement partitioning problem and the conventional methods are described.
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