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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measureme...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630985/ https://www.ncbi.nlm.nih.gov/pubmed/31200450 http://dx.doi.org/10.3390/s19122665 |