Cooperative localization using angle of arrival measurements in non-line-of-sight environments
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
Bandwidth efficient cooperative TDOA computation for multicarrier signals of opportunity
IEEE Transactions on Signal Processing
Normalized incremental subgradient algorithm and its application
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
Optimum sensor placement for source monitoring under log-normal shadowing in three dimensions
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Robust maximum likelihood acoustic source localization in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
IEEE Transactions on Signal Processing
Distributed multiagent learning with a broadcast adaptive subgradient method
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
IEEE Transactions on Signal Processing
Dual-decomposition approach for distributed optimization in wireless sensor networks
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
Computational Optimization and Applications
Cooperative localization revisited: error bound, scaling, and convergence
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Hi-index | 35.70 |
This correspondence addresses the problem of locating an acoustic source using a sensor network in a distributed manner, i.e., without transmitting the full data set to a central point for processing. This problem has been traditionally addressed through the maximum-likelihood framework or nonlinear least squares. These methods, even though asymptotically optimal under certain conditions, pose a difficult global optimization problem. It is shown that the associated objective function may have multiple local optima and saddle points, and hence any local search method might stagnate at a suboptimal solution. In this correspondence, we formulate the problem as a convex feasibility problem and apply a distributed version of the projection-onto-convex-sets (POCS) method. We give a closed-form expression for the projection phase, which usually constitutes the heaviest computational aspect of POCS. Conditions are given under which, when the number of samples increases to infinity or in the absence of measurement noise, the convex feasibility problem has a unique solution at the true source location. In general, the method converges to a limit point or a limit cycle in the neighborhood of the true location. Simulation results show convergence to the global optimum with extremely fast convergence rates compared to the previous methods