A particle filtering (PF) is a sequential Bayesian filtering method suitable for non-linear non-
Gaussian systems, which is widely used to estimate the states of parameters of interest that …

Particle filtering (PF) is a sequential Monte Carlo method that draws sample (particle) values
of state variables of interest to approximate the posterior probability distribution function …

With consideration of low tracking accuracy and even losing target when the track-before-
detect method based on particle filter (PF-TBD) tracks infrared dim small target in the …

Although the cost-reference particle filter (CRPF) has a good advantage in solving the state
estimation problem with unknown noise statistical characteristics, its estimation accuracy is …

Particle filters (PFs) estimate the true parameter state from samples of states, called
particles, that are drawn to construct an approximated posterior probability density function …

The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian
systems. However, this method misleads the results due to sample impoverishment and the …

Particle filtering (PF) is a powerful sequential Monte Carlo (SMC) method used to estimate
hidden states of parameters of interest from noisy measurements obtained from dynamic …

Terrain-aided navigation (TAN) is a promising technology for achieving accurate positioning
of long-range autonomous underwater vehicles (AUVs). In TAN systems, particle filters (PFs) …

Considering the widely existing processing time uncertainty in the real‐world production
process, this paper constructs a fuzzy mathematical model for the silicon single crystal …

This paper aims to present a full state estimation scheme for the triangular tethered satellite
formations (TSF), which have rarely been studied and face some challenges including …