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 …

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study
regarding infrared remote sensing. In the low-SNR images, the intensive noise will …

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 …

The jet pipe servo (JPS) valve is one key component, whose dynamic performance directly
influences the aircraft's maneuverability. In this paper, a more accurate mathematical model …

During the growth of silicon single crystals, it is critical to detect the liquid level of the silicon
melt to ensure their high-quality production. Because noise statistics are difficult to …

This article reviews state-of-the-art of the problem of using Artificial Intelligence (AI) for
spacecraft Guidance, Navigation, and Control (GNC). Developing AI algorithms for GNC …

In order to improve the estimation accuracy of the state information and save the computing
time for fractional-order systems containing correlated and uncorrelated process and …

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 filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts
Gaussian in the state and noise of the system. PFs have the problem of sample …