The expectation maximisation algorithm (EM) was introduced by Dempster, Laird and Rubin in 1977 [DLR77]. The basic of expextation maximisation is maximum likelihood estimation (MLE). In modern sensor data fusion expectation maximisation becomes a substantial part in several applications, e.g. multi target tracking with probabilistic multi hypothesis tracking (PMHT), target extraction within pro...

Probabilistic approaches to tracking often use single-source Bayesian models; applying these to multi-source tasks is problematic. We apply a principled multi-object tracking implementation, the Gaussian mixture probability hypothesis density filter, to track multiple sources having fixed pitch plus vibrato. We demonstrate high-quality filtering in a synthetic experiment, and find improved trac...

Tracking multiple maneuvering targets for automotive radar is a vital issue. To this end, a novel DS-UKGMPHD algorithm which combines diagraph switching (DS), unscented Kalman (UK) filter and Gaussian mixture probability hypothesis density (GMPHD) filter is proposed in this paper. The algorithm is capable of tracking a varying number of target cars detected by automotive radar with nonlinear me...

The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem. We show that, under reasonable assumptions, a sequential Monte Carlo (SMC) approximation of the PHD filter converges in mean of order p ≥ 1, and hence almost surely, t...

Sequential Monte Carlo (SMC) methods such as particle filters have been used in tracking problems for moving from an intractable distribution to a density that is closer to the actual posterior distribution. These methods makes use of stochastic simulations that can approximate non-linear and non-Gaussian posterior distributions via importance sampling. Since standard SMC methods only allows to...

We analyse the exponential stability properties of a class of measure-valued equations arising in nonlinear multi-target filtering problems. We also prove the uniform convergence properties w.r.t. the time parameter of a rather general class of stochastic filtering algorithms, including sequential Monte Carlo type models and mean field particle interpretation models. We illustrate these results...

The problem of multiple-object tracking consists in the recursive estimation of the state of several targets by using the information coming from an observation process. The objective of this thesis is to study the spatial branching processes and the measure-valued systems arising in multi-object tracking. We focus on a class of filters called Probability Hypothesis Density (PHD) filters by fir...

Ronald Mahler’s Probability Hypothesis Density (PHD) provides a promising framework for the passive coherent location of targets observed via multiple bistatic radar measurements. We apply a particle filter implementation of the Bayesian PHD filter to target tracking using both range and Doppler measurements from a simple non-directional receiver that exploits non-coöperative FM radio transmitt...