This paper proposes a novel merging algorithm in Gaussian mixture probability hypothesis density filter to track close proximity targets. The proposed algorithm is added after GM-PHD recursion, in a condition that more than one target has the same state. The weights of Gaussian components decide whether the components can be utilized to extract states, and the means and covariances of Gaussian ...

The Probability Hypothesis Density (PHD) filter has been recently received a lot of attention by the estimation and data fusion community for its ability to provide a useful solution to the Bayesian filter problem (i.e., implementation issue). Its core foundation to other parallel directions, such as the Sequential Monte Carlo PHD, the Gaussian Mixture PHD and others, offers a viable path to pr...

Compared to the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters, trajectory (TPHD) CPHD (TCPHD) filters are for sets of trajectories, thus able produce estimates with better performance. In this paper, we develop TPHD TCPHD which can adaptively learn history unknown target detection probability, therefore they perform more robustly in scenarios where targets time-varyin...

In this thesis, the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter using a pair of stereo vision system to overcome label discontinuity and robust tracking in an Intelligent Vision Agent System (IVAS) is evaluated. This filter is widely used in multiple-target tracking applications such as surveillance, human tracking, radar, and etc. A pair of cameras is use...

The convergence of the Gaussian mixture extended-target probability hypothesis density GMEPHD filter and its extended Kalman EK filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to pr...

The paper proposes Gaussian mixture particle probability hypothesis density filter(PHD) algorithm ,which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter. This algorithm calculates the object number and state by recursive procedure, avoiding the uncertainty of target state estimation caused by particle sampling and clustering. Gaussia...

This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle fi...