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PMHT algorithm, as proposed, promises high performance multi target tracking in clutter with (relatively) modest computational resources. However, when applied to practical target tracking situations, a number of problems need to be overcome. PMHT assumes fixed number of tracks, and furthermore it assumes that all tracks are true tracks. No track quality measure is provided within PMHT to enable false...
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...
The hybrid SIR joint particle filter has been developed as an effective approximation of the exact Bayesian filter for maintaining tracks of multiple maneuvering targets from unassociated measurements. This paper further develops this approach for the situation of limited sensor resolution and two maneuvering targets. For this problem the exact Bayesian filter recursion is characterized, and is subsequently...
The probability hypothesis density (PHD) filter, which was derived from finite set statistics is a promising approach to multi-target tracking. An analytical closed-form solution for the PHD, named Gaussian mixture PHD Filter, is given for linear Gaussian target dynamics with Gaussian births by B. Vo and W. Ma. Based on the Gaussian mixture PHD filter, in this paper, without consideration of data...
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...
This paper deals with the probabilistic data association issue in the context of multiple target tracking. In the continuation of the part I framework, we focus here on scenarios where multiple false measurements may occur. In particular, the influence of various critical parameters on the multi-tracking efficiency, i.e. the probability of correct association, is analyzed. Besides, we study the impact...
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