Abstract
As discussed in the previous chapters, important challenges faced by multi-target filtering/tracking include clutter, detection uncertainty, and data association uncertainty. Until now, three mainstream solutions have emerged for the multi-target tracking problem: multiple hypothesis tracking (MHT), joint probabilistic data association (JPDA), and the emerging random finite set (RFS). Thanks to a long time of development, the first two solutions are relatively mature with abundant reference materials. By contrast, materials about the RFS method are relatively lacking. For this reason, the book aims to detail the RFS-based target tracking algorithms.
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Notes
- 1.
Throughout the book, the multi-target density is denoted by \(\pi\), while the single-target density is denoted by \(p\).
- 2.
For simplicity, the same symbol \(\phi_{k|k - 1}\) is used for the multi-target transition function (3.4.7) and the single-target transition function (2.2.2) (other symbols are similar), which will not cause confusion. It is easy to distinguish them according to the parameter in the bracket of the function, because in the single-target case, the parameter is a vector, while in the multi-target case, the parameter is a finite set.
- 3.
Strictly speaking, the “per-target” error is the “per-estimated-target” error when the cardinality is overestimated, and the “per-true-target” error when the cardinality is underestimated.
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© 2023 National Defense Industry Press
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Wu, W., Sun, H., Zheng, M., Huang, W. (2023). Basics of Random Finite Sets. In: Target Tracking with Random Finite Sets. Springer, Singapore. https://doi.org/10.1007/978-981-19-9815-7_3
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DOI: https://doi.org/10.1007/978-981-19-9815-7_3
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9814-0
Online ISBN: 978-981-19-9815-7
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