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...

Tracking objects involves the modeling of non-linear nonGaussian systems. On one hand, variants of Kalman filters are limited by their Gaussian assumptions. On the other hand, conventional particle filter, e.g., CONDENSATION, uses transition prior as the proposal distribution. The transition prior does not take into account current observation data, and many particles can therefore be wasted in...

This paper addresses the issue of multi-aspect target tracking where target’s aspect is modeled by a continuous-valued affine model. The affine parameters are assumed to follow first-order Markov models and augmented with target’s kinematic parameters in the state vector. Three particle filtering algorithms, Sequential Importance Re-sampling (SIR), the Auxiliary Particle Filter (APF1), and a mo...

This paper presents a distributed particle filter over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. Through this consensus filter, each sensor node can gradually diffuse its local mean and covariance of weighted particl...

The unscented Kalman filter is a superior alternative to the extended Kalman filter for a variety of estimation and control problems. However, its effectiveness for improving human motion tracking for virtual reality applications in the presence of noisy data has been unexplored. In this paper, we present an empirical study comparing the performance of unscented and extended Kalman filtering fo...

This paper addresses the state-estimation problem for nonlinear systems in a context where prior knowledge, in addition to the model and the measurement data, is available in the form of a nonlinear equality constraint. Then three suboptimal algorithms based on the unscented Kalman filter (UKF) are developed, namely, the equality-constrained unscented Kalman filter (ECUKF), the projected unscen...

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effects and non constant conditional mean and jumps. We are interested in estimating the time invariant parameters and the non-observable dynamics involved in the model. Our idea relies on the auxiliary particle filter algorithm mixed together with Markov Chain Monte Carlo (MCMC) ...