Abstract
Recent research into multi-object filtering for non-standard targets introduced alternative approaches for target group representation. In these approaches a measurement model (likelihood) was suggested that led to a representation of the measurements as a spatial point process, namely a Poisson point process. In this paper we take a more traditional approach to extended target tracking. We assume a 'standard' measurement model (at most one measurement generated from a target point), but represent the target group (extended targets) as a spatial cluster process, in particular an independent cluster process with a fixed distribution on the component (daughter) process. With this assumption we are able to derive approximate measurement-update equations for the first order moment density of the extended object Bayes filter in a number of scenarios. Such approximations are Bayes optimal and provide estimates for the number of clusters (extended targets) and their locations.
Original language | English |
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Title of host publication | 13th Conference on Information Fusion, Fusion 2010 |
Publication status | Published - 2010 |
Event | 13th Conference on Information Fusion - Edinburgh, United Kingdom Duration: 26 Jul 2010 → 29 Jul 2010 |
Conference
Conference | 13th Conference on Information Fusion |
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Abbreviated title | Fusion 2010 |
Country/Territory | United Kingdom |
City | Edinburgh |
Period | 26/07/10 → 29/07/10 |
Keywords
- Estimation
- Filtering
- Spatial cluster processes
- Tracking