Abstract
A simple yet effective state-estimation algorithm is presented and demonstrated to have advantages over previous standard clustering techniques used for the particle probability hypothesis density filter. The idea behind the proposed algorithm is that it uses the latest available information (i.e., the measurements) to direct particle clustering. The particle likelihood and target number estimation, computed during probability hypothesis density recursion, are both used to partition particles into clusters, and the center of each cluster gives the state estimation of an individual target. Simulation results indicate that the proposed algorithm outperforms the standard clustering approach using the k-means algorithm, achieving higher accuracy and shorter computational time.
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Lin, L., Xu, H., Sheng, W. et al. Multi-target state-estimation technique for the particle probability hypothesis density filter. Sci. China Inf. Sci. 55, 2318–2328 (2012). https://doi.org/10.1007/s11432-012-4577-8
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DOI: https://doi.org/10.1007/s11432-012-4577-8