Color noise is a special kind of often occurring in localization systems, and it more suitable than the general Gaussian white to model time dependence due delay or high-frequency sampling. This paper derives nonlinear filtering framework for multi-step colored systems using whitening techniques Bayes rule. Meanwhile, cubature rule used solve Gaussian-weighted integral proposed framework, resul...

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter c...

Medical ultrasound images are widely used for diagnostic purposes. A major problem regarding these images is in their inherent corruption by speckle noise. The presence of speckle noise severely hampers the interpretation and analysis of medical ultrasound images. The objective of the paper is to propose a method for removal of noise in the medical ultrasound images. The image noise content is ...

This paper shows an approach to improve the statistical validity of orbital estimates and uncertainties as well as a method of associating measurements with the correct space objects. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the space object tracking problem. The Fokker-Planck-Kolmogorov equation describe...

ABSTRACT This paper is concerned with improving the attitude estimation accuracy by implementing an adaptive Gaussian sum filter where the a posteriori density function is approximated by a sum of Gaussian density functions. Compared to the traditional Gaussian sum filter, this adaptive approach utilizes the Fokker-Planck-Kolmogorov residual minimization to update the weights associated with di...

This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a Gaussian Mixture Model (GMM) to the prior ensemble. Using the data likelihood func...