Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Cooperative spectrum sensing with noisy hard decision transmissions
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Cooperative shared spectrum sensing for dynamic cognitive radio networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
A repeated significance test with applications to sequential detection in sensor networks
IEEE Transactions on Signal Processing
A generalized cauchy distribution framework for problems requiring robust behavior
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Distributed iterative quantization for interference characterization in wireless networks
Digital Signal Processing
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Recently proposed decentralized, distributed estimation and power scheduling methods for wireless sensor networks (WSNs) do not consider errors occurring during the transmission of binary observations from the sensors to fusion center. In this letter, we extend the decentralized estimation model to the case in which imperfect transmission channels are considered. The proposed estimators, which operate on additive channel noise corrupted versions of quantized noisy sensor observations, are approached from a maximum likelihood (ML) perspective. Complicating this approach is the fact that the noise distribution is rarely fully known to the fusion center. Here we assume the distribution is known but not the defining parameters, e.g., variance. The resulting incomplete data estimation problem is approached from a expectation-maximization (EM) perspective. The critical initialization and convergence aspects of the EM algorithm are investigated. Furthermore, the estimation of the source parameter is extended to the blind case where both the channel and sensor noise parameters are unknown. Finally, numerical experiments are provided to show the effectiveness of the proposed estimators.