One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model

The Unscented Particle Filter

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standar...

Acoustic Source Localization Based on Iterative Unscented Particle Filter

To solve the problem of tracking an acoustic source in noise and reverberation environment, a new method is proposed in pursuit of higher accuracy. First, this paper improves the unscented particle filter, which can add the latest measurement information to optimize the proposal distribution. Then, the likelihood function is constructed by calculating the microphone arrays’ output energy in the...

Robust Adaptive Unscented Particle Filter

An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscentedKalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evalua...

An Improved FastSLAM Framework Based on Particle Swarm Optimization and Unscented Particle Filter ⋆

FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. To solve this problem, a FastSLAM method based on particle swarm optimization and unscented particle filter is proposed. The number of pa...