Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Sensor network-based countersniper system
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Signal reconstruction in sensor arrays using sparse representations
Signal Processing - Sparse approximations in signal and image processing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Inference in sensor networks: graphical models and particle methods
Inference in sensor networks: graphical models and particle methods
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach
Digital Signal Processing
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This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal solution. By exploiting the spatial sparsity of the posterior density, we demonstrate that the optimal solution can be computed using fast sparse approximation algorithms. We show that exploiting the signal sparsity can reduce the sensing and computational cost on the sensors, as well as the communication bandwidth. We further illustrate that the sparsity of the source locations can be exploited to decentralize the computation of the source locations and reduce the sensor communications even further. We also discuss how recent results in 1-bit compressive sensing can significantly reduce the amount of inter-sensor communications by transmitting only the intrinsic timing information. Finally, we develop a computationally efficient algorithm for bearing estimation using a network of sensors with provable guarantees.