15 September 2005 Poisson models for extended target and group tracking
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Abstract
It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, the measurements are modelled as a Poisson process with a spatially dependent rate parameter. This representation allows us to model extended targets as an intensity distribution rather than a set of points and, for a target formation, it gives the option of modelling part of the group as a spatial distribution of target density. Furthermore, as a direct consequence of the Poisson model, the measurement likelihood may be evaluated without constructing explicit association hypotheses. This considerably simplifies the filter and gives a substantial computational saving in a particle filter implementation. The Poisson target-measurement model will be described and its relationship to other filters will be discussed. Illustrative simulation examples will be presented.
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Kevin Gilholm, Kevin Gilholm, Simon Godsill, Simon Godsill, Simon Maskell, Simon Maskell, David Salmond, David Salmond, } "Poisson models for extended target and group tracking", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59130R (15 September 2005); doi: 10.1117/12.618730; https://doi.org/10.1117/12.618730
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