Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking

TL;DR

This paper introduces a new method for tracking multiple extended targets using affinity propagation clustering and elliptical gating to improve accuracy and reduce computational complexity.

Contribution

The novel use of affinity propagation clustering with elliptical gating for measurement partitioning in extended target tracking.

Findings

The proposed method improves tracking performance in cluttered environments.

It significantly reduces computational complexity compared to existing methods.

Numerical results validate the effectiveness of the approach.

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.

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