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In this paper we will consider several algorithms for tracking closely spaced objects. In particular we will concentrate on various particle filter implementations. One particular problem when using a joint multi target particle filter is the so-called mixed labelling problem. This problem amounts to the fact that different particles will have a different labelling w.r.t. target identity. The combination...
In this paper, we derive the updating formula of the cardinalized probability hypothesis density (CPHD) filter recently developed in the works of Mahler et al., (2006) from the non- Poisson multiple-hypothesis tracking (MHT) algorithm developed earlier in the works of Mori et al. (2004). The particular form of the CPHD updating formula developed in this paper is expressed only with the probability...
We seek to establish a quality metric for sensor fused video based on the performance of a multisensor system that actively tracks multiple surface targets over an extended field of regard Such a system must, in real-time, fuse multisensor/spectral imagery, detect targets reliably, track the targets for extended time periods as they move, stop, approach, cross, hide/emerge, over an extended field...
In multi-sensor multi-target bearings-only tracking we often see false intersections of bearings known as ghosts. When the bearing measurements from each sensor have been associated to form sequences termed threads, the problem is to associate pairs of threads to identify the true target intersections. In this paper we present two algorithms: (i) classical bayesian thread association (CBTA) and (ii)...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three issues: (i) we show the equivalence between the...
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