This technical report presents a cardinalized probability hypothesis density (CPHD) lter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) lter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD...

This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown detection profiles clutter rates using measurements from multiple sensors. In particular, we propose combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter Cardinalized Probability Hypothesis Density (CPHD) filters rates. The probability is augmented filtering state space fo...

This paper presents the probability hypothesis density (PHD) filter for sets of trajectories. The resulting filter, which is referred to as trajectory probability density filter (TPHD), is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. As the PHD filter, the TPHD filter is based on recursively obtaining the ...

This paper studies the multitarget tracking problem based on an asynchronous network of sensors with different sampling rates, where each sensor runs a cardinalized probability hypothesis density (CPHD) filter. To fuse filter estimates obtained at conditioned measurements, arithmetic averaging approach is recursively carried out in timely manner according to network-wide time sequence. The inte...

Many multi-object estimation problems require additional estimation of model or sensor parameters that are either common to all objects or related to unknown characterisation of one or more sensors. Important examples of these include registration of multiple sensors, estimating clutter profiles, and robot localisation. Often these parameters are estimated separately to the multi-object estimat...

This paper addresses the problem of multi-target tracking with superpositional sensors, while covariance matrices measurement noise are not known. The proposed method is based on hybrid multi-Bernoulli cardinalized probability hypothesis density (HMB-CPHD) filter, which has been developed for sensors-based known noises. Specifically, we firstly propose Gaussian mixture (GM) implementation HMB-C...