The estimation of an unobservable process x from an observed process y is often performed in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the Kalman filter. On the other hand, particle filters provide approximate solutions in more complex situations. In this paper, we propose two successive generalizations of the classica...

Particle filters (PF) and auxiliary particle filters (APF) are widely used sequential Monte Carlo (SMC) techniques. In this paper we comparatively analyse, from a non asymptotical point of view, the Sampling Importance Resampling (SIR) PF with optimal conditional importance distribution (CID) and the fully adapted APF (FA). We compute the (finite samples) conditional second order moments of Mon...

We model a time series fyt; t = 1; :::; ng using a state space framework with the fytj tg being independent and with the state f tg assumed to be Markovian. The task will be to use simulation to estimate f( tjFt), t = 1; :::; n, where Ft is contemporaneously available information. We assume a known `measurement' density f(ytj t) and the ability to simulate from the `transition' density f( t+1j ...

Particle filter has been widely applied in nonlinear target tracking due to the ability carry multiple hypothesis and relaxation of linearity/Gaussian assumption. In this paper, an adaptive double space-resampling particle is proposed increase efficiency robustness filtering by adjusting sample size. The first resampling operation, adopted before prediction samples, generates a larger number eq...

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information....

In this work we address the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution. In the context of mobile robots this problem arises in localization and simultaneous localization and mapping (SLAM) with occupancy grid maps. The lack of a parameterized observation model for these maps forces a sa...