Benhong Zhang

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We develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensor-network environments. Under this framework, spatially distributed observations collected at the sensors form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. We derive iterated conditional modes (ICM)(More)
We develop a hierarchical Bayesian approach for estimating defect signals from noisy measurements and apply it to nondestructive evaluation (NDE) of materials. We propose a parametric model for the shape of the defect region and assume that the defect signals within this region are random with unknown mean and variance. Markov chain Monte Carlo (MCMC)(More)
We derive methods for asymptotic maximum likelihood (ML) estimation of Jakes' Doppler power spectrum parameters from complex noisy estimates of the fading channel. We consider both single-input single-output (SISO) and smart-antenna scenarios and utilize the Whittle approximation to the likelihood to estimate the Doppler spread, noise variance, and channel(More)
We develop a nonparametric method for estimating the probability distribution function (pdf) describing the physical phenomenon measured by a sensor network. The measurements are collected by sensor-processor elements (nodes) deployed in the region of interest; the nodes quantize these measurements and transmit only one bit per observation to a fusion(More)
We present a sequential Bayesian method for dynamic estimation and prediction of local mean (shadow) powers from instantaneous signal powers in composite fading-shadowing wireless communication channels. We adopt a Nakagami-m fading model for the instantaneous signal powers and a first-order autoregressive [AR(1)] model for the shadow process in decibels.(More)
We propose a Bayesian method for complex amplitude estimation in low-rank interference. We assume that the received signal follows the generalized multivariate analysis of variance (GMANOVA) patterned-mean structure and is corrupted by low-rank spatially correlated interference and white noise. An iterated conditional modes (ICM) algorithm is developed for(More)
We present methods for dynamic estimation and prediction of local mean (shadow) powers from instantaneous signal powers in composite fading-shadowing wireless communication scenarios. We adopt a Nakagami-m fading model for the instantaneous signal powers and a first-order autoregressive - AR(1) - model for the shadow process in decibels. Sequential Bayesian(More)
We propose distributed methods for estimating and detecting the mean of a correlated Gaussian random field observed by a sensor network. The random-field correlations are assumed to follow a conditional autoregressive (CAR) model. First, a distributed maximum likelihood (ML) estimator of the mean field is derived. We then develop batch and sequential(More)
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