This paper proposes an auxiliary vector filtering (AVF) algorithm based on a constrained constant modulus (CCM) design for robust adaptive beamforming. This scheme provides an efficient way to deal with filters with a large number of elements. The proposed beamformer decomposes the adaptive filter into a constrained (reference vector filters) and an unconstrained (auxiliary vector filters) comp...

In the present study, a theoretical model was used to examine factors affecting the filtration characteristics of filters used for respiratory protection. This work was designed to support the particulate filter test requirements established in 1996. The major operating parameters examined in this work include face velocity, fiber diameter, packing density, filter thickness, and fiber charge de...

Recursively estimating the likelihood of a set of parameters, given a series of observations, is a common problem in signal processing. The Kalman filter is a The particle filter is now a well-known alternative to the Kalman filter. It represents the likelihood as a set of samples with associated weights and so can approximate any distribution. It can be applied to problems where the process mo...

As an approximation of the optimal stochastic filter, particle filter is a widely used tool for numerical prediction of complex systems when observation data are available. In this paper, we conduct an error analysis from a numerical analysis perspective. That is, we investigate the numerical error, which is defined as the difference between the numerical implementation of particle filter and i...

The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. The nonparametric nature of particle filters makes them ideal for non-linear, non-Gaussian dynamic systems. Particle filtering has many applications: in computer vision, robotics, and econometrics to name just a few. Although superior to Kalman filters, particle filters have higher computational requirements...

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but...

A person following behaviour for a mobile robot with a new vision tracking algorithm is presented in this paper. According to the different characteristics of particle filter and Kalman filter, a novel approach of target tracking based on hybrid particle filters is applied to process the target object’s position and shape component respectively, whose state updating is on the basis of data fusi...

As the normal particle filter has an expensive computation and degeneracy problem, a propagation-prediction particle filter is proposed. In this scheme, particles after transfer are propagated under the distribution of state noise, and then the produced filial particles are used to predict the corresponding parent particle referring to measurement, in which step the newest measure information i...