Particle filters are important approximation methods for solving probabilistic optimal filtering problems on nonlinear non-Gaussian dynamical systems. In this paper, we derive novel moment conditions for importance weights of sequential Monte Carlo based particle filters, which ensure the L convergence of particle filter approximations of unbounded test functions. This paper extends the particl...

A B S T R A C T Data assimilation methods that work in high dimensional systems are crucial to many areas of the geosciences: meteorology, oceanography, climate science etc. The equivalent weights particle filter has been designed, and has recently been shown to, scale to problems that are of use to these communities. This article performs a systematic comparison of the equivalent weights parti...

High-performance bulk acoustic wave (BAW) filters have been widely applied in the advanced radio frequency (RF) wireless communication systems in the past decade. However, the demand for filters with large bandwidth, up to 10%, still puts a significant stress on the typical aluminum nitride (AlN)-based BAW filters. In this work, a modified lattice configuration is proposed to achieve a wideband...

It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, ...

The problem of estimating the number and state of multiple targets using a sensor with limited sensing ability is raised in a variety of applications, including monitoring of endangered species, civilian security, and military surveillance. The particle filter is widely used to solve this problem since Kalman filter’s disadvantage on estimating non-Gaussian distribution. However, the problem be...

In this paper propose a denoising method for reducing noise in digital images. An efficient RaoBlackwellized Particle Filter (RBPF) with maximum likelihood Estimation approach is used for improving the learning stage of the image structural model and guiding the particles to the most appropriate direction. It increases the efficiency of particle transitions. The proposal distribution is compute...

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weigh...

Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorit...