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Compressed sensing for DOA estimation with fewer receivers than sensors

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3 Author(s)
Jian-Feng Gu ; Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC, Canada ; Wei-Ping Zhu ; Swamy, M.N.S.

This paper addresses the problem of the direction-of-arrival (DOA) estimation using fewer receivers than sensors. Inspired by the Compressed Sensing (CS) theory developed in recent years, we present a new preprocessing scheme for a large array using a small size receiver. Unlike the traditional ℓ2 -norm-based algorithms by judicious selection of the preprocessing matrix, the proposed scheme uses a random weight generator as a measurement of the compressed sensing to form the output data for each time interval. The formulated CS problem for DOA estimation is then solved based on the convex programming via ℓ1 -norm approximation such as Dantzig Selector. We consider two different scenarios in the CS domain, i.e., the angle domain and the angle-frequency domain. It is shown that the number of receivers can be reduced significantly for a given number of sensors by using the proposed CS-based DOA estimation approach.

Published in:

Circuits and Systems (ISCAS), 2011 IEEE International Symposium on

Date of Conference:

15-18 May 2011