The probability hypothesis density (PHD) filter is a recursive algorithm for solving multi-target tracking problems. The method consists of replacing the expectation of a random variable used in the traditional Bayesian filtering equations, by taking generalized expectations using the random sets formalism. In this approach, a set of observations is used to estimate the state of multiple and un...

Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the dimensionality of the problem, the PHD filter s...

This work addresses the multi-target tracking problem in the nonlinear Gaussian system. One probability hypothesis density filtering algorithm based on GaussianHermite numerical integration is proposed. In order to calculate integrations in the Gaussian mixture probability hypothesis density filter, the Gaussian-Hermite numerical integration method is used to approximate the integration. In the...

Based on probability density evolution method (PDEM) and Bayes’ law, a new filter strategy is proposed, in which the prior of system state interest predicted by solving generalized equation (GDEE), posterior then updated terms Bayesian formula. Furthermore, Chebyshev polynomial-based collocation developed to obtain numerical solutions probability. Three illustrative examples are finally present...

This paper addresses distributed multi-target tracking over a network of sensors having different fields-of-view (FoVs). Specifically, cardinalized probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that node has limited FoV, standard fusion methods need be suitably modified. In fact, monitored area multiple nodes consists several parts are either exclusive...