@conference {12555,
	title = {Compressive wireless arrays for bearing estimation},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {2497 - 2500},
	abstract = {Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, low dimensional random projections of the microphone signals can be used to determine multiple source bearings by solving an <i>l</i> <sub>1</sub>-norm minimization problem. Field data results are shown where only 10 bits of information is passed from each microphone to estimate multiple target bearings.},
	keywords = {acoustic, algorithms;sparse, arrays;acoustic, arrays;hypothesis, bandwidth;compressive, bearing, Estimation, estimation;signal, estimation;sparse, matrices;vectors;wireless, minimization, networks;, problem;communication, problem;microphone, problems;joint, PROCESSING, processing;array, processing;direction-of-arrival, processing;l<sub>1</sub>-norm, sensor, signal, signals;parameter, Testing, vector;wireless, wireless},
	doi = {10.1109/ICASSP.2008.4518155},
	author = {Cevher, V. and Gurbuz, A.C. and McClellan, J.H. and Chellapa, Rama}
}
@article {13995,
	title = {Flexible and Optimal Design of Spherical Microphone Arrays for Beamforming},
	journal = {Audio, Speech, and Language Processing, IEEE Transactions on},
	volume = {15},
	year = {2007},
	month = {2007/02//},
	pages = {702 - 714},
	abstract = {This paper describes a methodology for designing a flexible and optimal spherical microphone array for beamforming. Using the approach presented, a spherical microphone array can have very flexible layouts of microphones on the spherical surface, yet optimally approximate a desired beampattern of higher order within a specified robustness constraint. Depending on the specified beampattern order, our approach automatically achieves optimal performances in two cases: when the specified beampattern order is reachable within the robustness constraint we achieve a beamformer with optimal approximation of the desired beampattern; otherwise we achieve a beamformer with maximum directivity, both robustly. For efficient implementation, we also developed an adaptive algorithm for computing the beamformer weights. It converges to the optimal performance quickly while exactly satisfying the specified frequency response and robustness constraint in each step. One application of the method is to allow the building of a real-world system, where microphones may not be placeable on regions, such as near cable outlets and/or a mounting base, while having a minimal effect on the performance. Simulation results are presented},
	keywords = {adaptive, algorithm;beamforming;beampattern;frequency, arrays;, arrays;acoustic, directivity;optimal, microphone, processing;array, processing;microphone, response;maximum, signal, spherical},
	isbn = {1558-7916},
	doi = {10.1109/TASL.2006.876764},
	author = {Li,Z. and Duraiswami, Ramani}
}
