In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the ...

This paper provides a simulation-based approach to filtering and sequential parameter learning for stochastic volatility models. We develop a fast simulation-based approach using the practical filter of Polson, Stroud and Müller (2002). We compare our approach to sequential parameter learning and filtering with an auxiliary particle filtering algorithm based on Storvik (2002). For simulated dat...

The knowledge of the pose and the orientation of a mobile robot in its operating environment is of utmost importance for an autonomous robot. Techniques solving this problem are referred to as self-localization algorithms. Self-localization is a deeply investigated field in mobile robotics, and many effective solutions have been proposed. In this context, Monte Carlo Localization (MCL) is one o...

Existing methods of improving particle filters mainly focus on two aspects: designing a good proposal distribution before sampling and allocating particles to a high posterior area after sampling. An auxiliary particle filter (APF) is one such simple algorithm belonging to the former aspect, which generates particles from an importance distribution depending on a more recent observation. Its we...

Selecting conveniently the proposal kernel and the adjustment multiplier weights of the auxiliary particle filter may increase significantly the accuracy and computational efficiency of the method. However, in practice the optimal proposal kernel and multiplier weights are seldom known. In this paper we present a simulation-based method for constructing offline an approximation of these quantit...

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable...

Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suf...