The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. Howeve...

Objective(s): The aim of this study is to examine the effect of different smoothing filters on the image quality and SUVmax to achieve the guideline recommended positron emission tomography (PET) image without harmonization. Methods: We used a Biograph mCT PET scanner. A National Electrical Manufacturers Association (NEMA) the International Electrotechnical Commission (IEC) body phantom was fil...

We present a filter design scheme in which we use an all-fiber lattice filter to equalize Gaussian-like spectra. With a two-step design strategy, the designed equalization filter can flatten the Gaussian-like spectra in the maximally flat sense, and the spectrum ripples can be assessed quantitatively. The equalization performance is analyzed theoretically. It is also shown that the equalization...

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable...

Robot localization using odometry and feature measurements is a nonlinear estimation problem. An efficient solution is found using the extended Kalman filter, EKF. The EKF however suffers from divergence and inconsistency when the nonlinearities are significant. We recently developed a new type of filter based on an auxiliary variable Gaussian distribution which we call the antiparticle filter ...

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the local filters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering ...

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise. In general, for the image sequence filtering, motion compensation (MC) method is required in order to obtain good filtering performance both in the still and moving regions of an image sequence. Nevertheless, a heavy computation load is imposed on MC method and MC tends to get mistaken motion v...

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...

Image restoration keeping sharp edges is achieved by bilateral filter. In this paper, an approach to improve edges for the filter is presented. The proposed algorithm relies on clustering by Expectation Maximization that produced clusters of intensive values. A stage is followed where standard deviation of Gaussian filters for scales of the spatial and intensity are adjusted by features of the ...

Image denoising is one of the important image processing techniques to reduce noises from a corrupted image. This paper proposes a new efficient fuzzy based filter for the restoration of images that are corrupted with Gaussian noise in gray and color images. This filter is applied iteratively to reduce heavy noise in images. The proposed scheme is simulated using standard image of lena under di...