Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Least Square Solutions of Energy Based Acoustic Source Localization Problems
ICPPW '04 Proceedings of the 2004 International Conference on Parallel Processing Workshops
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Energy-based collaborative source localization using acoustic microsensor array
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On Energy-Based Acoustic Source Localization for Sensor Networks
IEEE Transactions on Signal Processing
Energy-based sensor network source localization via projection onto convex sets
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Distributed computation of averages over ad hoc networks
IEEE Journal on Selected Areas in Communications
Directional acoustic source orientation estimation using only two microphones
Digital Signal Processing
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This work addresses the problem of estimating the locations of multiple acoustic sources by a network of distributed energy measuring sensors. The maximum likelihood (ML) solution to this problem is related to the optimization of a non-convex function of, usually, many variables. Thus, search-based methods of high complexity are required in order to yield an accurate solution. Considerable reduction of the complexity can be achieved by means of an alternating projection (AP) algorithm that decomposes the original problem into a number of simpler, yet also non-convex, optimization steps. The particular form of the derived cost functions of each such optimization step indicates that, in some cases, an approximate form of these cost functions can be used. These approximate cost functions can be evaluated using considerably lower computational complexity. Thus, a low-complexity version of the AP algorithm is proposed. Extensive simulation results demonstrate that the proposed algorithm offers a performance close to that of the exact AP implementation, and in some cases, similar performance to that of the ML estimator.