TY - GEN
T1 - Conditions for target recovery in spatial compressive sensing for MIMO radar
AU - Rossi, M.
AU - Haimovich, A. M.
AU - Eldar, Y. C.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - 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.
AB - 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.
KW - Compressive sensing
KW - MIMO radar
KW - direction of arrival estimation
KW - random arrays
UR - http://www.scopus.com/inward/record.url?scp=84890499790&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890499790&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638433
DO - 10.1109/ICASSP.2013.6638433
M3 - Conference contribution
AN - SCOPUS:84890499790
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4115
EP - 4119
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -