COMPRESSIVE WIRELESS ARRAYS FOR BEARING ESTIMATION OF SPARSE SOURCES IN ANGLE DOMAIN

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 d eployment 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. W e focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, functions of the low dimensional random projections of the microphone signals can be used to determine multiple source bearings as a solution of anl1-norm minimization problem. Field data results are shown where only 10bits of information is passed from each microphone to estimate multiple target bearings.

[1]  Don H. Johnson,et al.  Array Signal Processing: Concepts and Techniques , 1993 .

[2]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[3]  Deborah Estrin,et al.  Coherent acoustic array processing and localization on wireless sensor networks , 2003, Proc. IEEE.

[4]  John W. Fisher,et al.  Nonparametric belief propagation for sensor self-calibration , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  A.S. Willsky,et al.  Nonparametric belief propagation for self-calibration in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[6]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[7]  J. Haupt,et al.  Compressive wireless sensing , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[8]  S. Kirolos,et al.  Random Sampling for Analog-to-Information Conversion of Wideband Signals , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.

[9]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[10]  J.H. McClellan,et al.  A Multi Target Bearing Tracking System using Random Sampling Consensus , 2007, 2007 IEEE Aerospace Conference.

[11]  Volkan Cevher,et al.  A compressive beamforming method , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[13]  Volkan Cevher,et al.  Acoustic sensor network design for position estimation , 2009, TOSN.