Generalised labelled multi-Bernoulli forward-backward smoothing
This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB f...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://ieeexplore.ieee.org/abstract/document/7527954/ http://hdl.handle.net/20.500.11937/50665 |
| Summary: | This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections. |
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