The particle filter is a class of sequential Monte Carlo method, which can be applied to any
nonlinear and non-Gaussian random system that can be represented by the state space …

The aim of this work is to analyze the robustness and computational effort of particle filter
when applied to the attitude and gyro bias estimation problem. The particle filter is based on …

In recent years, the theory of particle filter has been developed and widely used for state and
parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance …

We present a modified bootstrap filter (MBF) to draw particles in the particle filter (PF). The
proposal distribution for each particle involves sampling from the state-space model a …

There has been significant recent interest of particle filters for nonlinear state estimation.
Particle filters evaluate a posterior probability distribution of the state variable based on …

We propose a new realization method of the sequential importance sampling (SIS) algorithm
to derive a new particle filter. The new filter constructs the importance distribution by the …

We propose a new particle filter for nonlinear continuous-discrete models. The proposed
filter is based on the multiple distribution estimation with a bank of extended Kalman-Bucy …

A novel algorithm is presented for the estimation of spacecraft attitude quaternion from
vector observations in gyro-equipped spacecraft. The new estimator is a particle filter that …

This paper proposes a novel particle filter, namely, the auxiliary iterated extended Kalman
particle filter (AIEKPF). To generate the importance density, based on the auxiliary particle …

The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the
target distribution's high-probability regions. This study is motivated by the need to enhance …