A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm
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We propose a high-resolution ranging algorithm for impulse radio (IR) ultra-WideBand (UWB) communication systems in additive white Gaussian noise. We formulate the ranging problem as a maximum- likelihood (ML) estimation problem for the channel delays and ...
Institute of Electrical and Electronics Engineers2009
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...
Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties ...
This paper addresses the problem of distortion modeling for video transmission over burst-loss channels characterized by a finite state Markov chain. A Distortion Trellis model is pro- posed, enabling us to estimate at the frame level the expected mean-squ ...
Practical impulse radio ultra-wideband (IR-UWB) ranging systems always have to work in multi-user and weak non-line-of-sight (NLOS) environments. In this paper, we derive a novel IR-UWB ranging estimator under multi-user and weak NLOS environments. We mode ...
We describe a method for aligning multiple unlabeled configurations simultane- ously. Specifically, we extend the two-configuration matching approach of Green and Mardia (2006) to the multiple configuration setting. Our approach is based on the in- troduct ...
This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 21-23, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting an ...
We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the proba ...
Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization ...