We develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensornetwork 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) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach AbstractWe develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensornetwork 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) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field (MRF) model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach.

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 covariance parameters. Asymptotic Crame´r-Rao bounds (CRBs) for the unknown parameters are derived. We also discuss the initialization of the proposed methods and their generalization to the Ricean-fading scenario. Numerical simulations demonstrate the performance of the proposed methods. 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 covariance parameters. Asymptotic Cramér-Rao bounds for the unknown parameters are derived. We also discuss the initialization of the proposed methods and their generalization to the Ricean-fading scenario. Numerical simulations demonstrate the performance of the proposed methods.

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) algorithms are derived for simulating from the posterior distributions of the model parameters and defect signals. These algorithms are then utilized to identify potential defect regions and estimate their size and reflectivity parameters. Our approach provides Bayesian confidence regions (credible sets) for the estimated parameters, which are important in NDE applications. We specialize the proposed framework to elliptical defect shape and Gaussian signal and noise models and apply it to experimental ultrasonic C-scan data from an inspection of a cylindrical titanium billet. We also outline a simple classification scheme for separating defects from non-defects using estimated mean signals and areas of the potential defects.

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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 estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (nonBayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods Abstract 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 estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (non-Bayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods.