Abstract
In order to extract the peaks of PHD, a novel method STPHD has been proposed recently. This method can provide more accurate target state estimates than the general clustering algorithm such as k-means clustering. This paper presents a version of STPHD for multi-sensor scene and makes two contributions. First, we generalize the STPHD algorithm to a multi-sensor scenario with an existing framework of fusion. The framework includes an association step and a fusion step. This generation can get better performance in accuracy. But the association step is time-consuming. The second contribution is a novel model for computing the cost of two sets of particles with sub-weights in the association step. The numerical simulation results show that the proposed method can significantly reduce the time cost with a very slight loss in accuracy compared with the previous methods.
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhenwei, L., Lingling, Z., Xiaohong, S., Peijun, M. (2012). A STPHD-Based Multi-sensor Fusion Method. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_13
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DOI: https://doi.org/10.1007/978-3-642-34487-9_13
Publisher Name: Springer, Berlin, Heidelberg
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