although there exist advanced models which predict beach profile for natural beaches, the behavior of the beaches in front of seawalls still suffers from the lack of appropriate theoretical models and sufficient measured data. in this paper, following the results obtained from the measurements, a beach profile evolution model is developed, using the measured probability distribution of the near...

This work develops an algorithm to initialize an Unscented Kalman Filter using a Particle Filter for applications with initial non-Gaussian probability density functions. The method is applied to estimating the position of a road vehicle along a one-mile test track using terrain-based localization where the pitch response of the vehicle is compared to a premeasured pitch map of the test track. ...

A class of filters based on histograms are presented. The signal probability density function is estimated and filtering is performed in the pdf domain. Such filters can be designed to preserve signal features such as sharp edges while suppressing stochastic variations. One particular histogram filter scheme is evaluated and compared to a median filter and a normal gaussian blurring filter.

Oriented textures were produced with the use of probability density functions modulated sinusoidally over orientation. Orientational contrast sensitivity functions (OCSFs) for a task involving the discrimination of these patterns from orientationally-random textures were found for several human observers. An inverse Fourier transform of this OCSF yielded a weighting function, or filter, defined...

Electroencephalography (EEG) and magnetoencephalography (MEG) measurements are used to localize neural activity by solving the electromagnetic inverse problem. In this paper, we propose a new approach based on the particle filter implementation of the probability hypothesis density filter (PF-PHDF) to automatically estimate the unknown number of time-varying neural dipole sources and their para...

Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follow...

Sequential Monte Carlo probability hypothesis density (SMCPHD) filtering has been recently exploited for audio-visual (AV) based tracking of multiple speakers, where audio data are used to inform the particle distribution and propagation in the visual SMC-PHD filter. However, the performance of the AV-SMC-PHD filter can be affected by the mismatch between the proposal and the posterior distribu...

Let p be an unknown and arbitrary probability distribution over [0, 1). We consider the problem of density estimation, in which a learning algorithm is given i.i.d. draws from p and must (with high probability) output a hypothesis distribution that is close to p. The main contribution of this paper is a highly efficient density estimation algorithm for learning using a variable-width histogram,...

Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A ba...