Abstract
Aiming at the multi-target tracking problem under clutter environment, a multi-dimensional feature-assisted multi-target tracking algorithm is proposed. The algorithm makes full use of the differences between the target and clutter measurements in multi-dimensional observations such as distance distribution, kinetic characteristics, and amplitude characteristics, accurately extracts multi-dimensional features, constructs comprehensive feature factor, and classifies and identifies the measurements. Minimize clutter and false alarm measurements, and achieve multi-target tracking in strong clutter scenarios. The performance of the algorithm is verified by the real radar measurement data, and the verification results show that the proposed algorithm can achieve accurate multi-target tracking in strong clutter environment, and has better tracking performance than other traditional feature-assisted tracking algorithms.
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