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This paper addresses the problem of cooperative localization (CL) under severe communication constraints. Specifically, we present minimum mean square error (MMSE) and maximum a posteriori (MAP) estimators that can process measurements quantized with as little as one bit per measurement. During CL, each robot quantizes and broadcasts its measurements and receives the quantized observations of its...
Mean-square error (MSE) performance analysis is conducted for a novel distributed least-mean square (D-LMS) algorithm, which is based on consensus, in-network, adaptive estimation using wireless sensor networks (WSNs). For sensor observations that are linearly related to the time-invariant parameter of interest and independent Gaussian data, exact closed-form expressions are derived for the global...
Automatic modulation classification (AMC) is a critical prerequisite for demodulation of communication signals in tactical scenarios. Depending on the number of unknown parameters involved, the complexity of AMC can be prohibitive. Existing maximum-likelihood and feature-based approaches rely on centralized processing. The present paper develops AMC algorithms using spatially distributed sensors,...
This paper deals with demodulation of space-time transmissions from a multi-antenna access point to a network of spatially distributed wireless sensors. Distributed demodulation algorithms are developed by achieving network-wide consensus on the average of (cross-) covariances of locally available per sensor received data vectors with the channel matrix, which constitute sufficient statistics for...
This paper considers the problem of distributed decoding of a coded message from a mobile access point (AP) sent to a sensor network. The distributed decoding problem is solved using a consensus algorithm on the log-likelihood ratio averages, which ensures that all sensors attain the same decision as if all received signals were available at a centralized location. Unlike existing distributed hypothesis...
Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum mean-square error (LMMSE) schemes, well appreciated for centralized estimation, are shown possible to reformulate...
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