Summary Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles. The $$\alpha$$-sequential method was proposed recently as a potential solution this that limits limited controlled through sequence of stochastic matrices known $$\alpha$$ matrices. We study the ...

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

This paper presents the novel Rao-Blackwellised particle filter (RBPF) for mobile robot simultaneous localization and mapping (SLAM) using monocular vision. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark pos...

Visual detection and target tracking are interdisciplinary tasks oriented to estimate the state of moving objects in an image sequence. There are different techniques focused on this problem. It is worth highlighting particle filters and Kalman filters as two of the most important tracking algorithms in the literature. In this paper, we presented a visual tracking algorithm which combines the p...

Human body tracking has received increasing attention in recent years due to its broad applicability. Among these tracking algorithms, the particle filter is considered an effective approach for human motion tracking. However, it suffers from the degeneracy problem and considerable computational burden. This paper presents a novel 3D model-based tracking algorithm called the progressive particl...

We have developed a new nonlinear filter that is superior to particle filters in five ways: (1) it exploits smoothness; (2) it uses an exact solution of the Fokker-Planck equation in continuous time; (3) it uses a convolution to compute the effect of process noise at discrete times; (4) it uses the adjoint method to compute the optimal density of points in state space to represent the smooth co...

Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details ...

This paper presents a comparison of different fitters namely: Extended Kalman Filter (EKF), Particle Filter (PF) and a proposed Enhanced Particle / Kalman Filter (EPKF) used in robot localization. These filters are implemented in matlab environment and their performances are evaluated in terms of computational time and error from ground truth and the results are reported. The considered robot l...

We consider non-linear state space models in high-dimensional situations, where the two common tools for state space models both have difficulties. The Kalman filter variants are seriously biased due to non-Gaussianity and the particle filter suffers from the “curse of dimensionality”. Inspired by a regression perspective on the Kalman filter, a novel approach is developed by combining the Kalm...