NDLI: Conditions for target recovery in spatial compressive sensing for MIMO radar

Content Provider	IEEE Xplore Digital Library
Author	Rossi, M. Haimovich, A.M. Eldar, Y.C.
Copyright Year	2013
Description	Author affiliation: CWCSPR, NJIT, Newark, NJ, USA (Rossi, M.; Haimovich, A.M.) \|\| Dept. of EE, Technion - Israel Inst. of Technol., Haifa, Israel (Eldar, Y.C.)
Abstract	We study compressive sensing in the spatial domain for target localization in terms of direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit/receive elements are placed at random. This allows to dramatically reduce the number of elements, while still attaining performance comparable to that of a filled (Nyquist) array. Leveraging properties of a (structured) random measurement matrix, we develop a novel bound on the coherence of the measurement matrix, and we obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish respectively uniform and non-uniform recovery guarantees for target localization using spatial compressive sensing. In particular, nonuniform recovery is guaranteed if the number of degrees of freedom (the product of the number of transmit and receive elements MN) scales with K(log $G)^{2},$ where K is the number of targets, and G is proportional to the array aperture and determines the angle resolution. The significance of the logarithmic dependence in G is that the proposed framework enables high resolution with a small number of MIMO radar elements. This is in contrast with a filled virtualMIMO array where the product MN scales linearly with G.
Starting Page	4115
Ending Page	4119
File Size	324261
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Arrays MIMO radar Compressed sensing Coherence Sensors MIMO Vectors direction of arrival estimation Compressive sensing random arrays
Content Type	Text
Resource Type	Article

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