TY - GEN
T1 - Waveform-dependent Bayesian Cramér-Rao angle-estimation bounds and threshold SNR estimates for MIMO radars
AU - Correll, Bill
AU - Kantor, Joshua M.
AU - Bliss, Daniel W.
PY - 2011
Y1 - 2011
N2 - Multiple-input multiple-output (MIMO) radars operate by simultaneously transmitting multiple independent waveforms. This facilitates improved angle-estimation performance by enabling the use of sparse antenna arrays without the ambiguities that occur when sparse arrays are used in conventional radars. Angle-estimation performance can be characterized in terms of the local error-performance bound given by the Cramér-Rao bound and in terms of the threshold point given by the SNR at which the estimator deviates significantly from the Cramér-Rao bound. In this paper, we extend results of Bliss, Forsythe, and Richmond on angle-estimation performance as a function of transmit waveform covariance for a MIMO radar. The analysis described in the above work is dependent upon an estimate or test location of a target position. Here, we provide a framework for a Bayesian extension that incorporates knowledge of the priors on the target position probability density. This information affects both the Cramér-Rao bound and the threshold SNR. Consequently, it affects waveform and system optimization.
AB - Multiple-input multiple-output (MIMO) radars operate by simultaneously transmitting multiple independent waveforms. This facilitates improved angle-estimation performance by enabling the use of sparse antenna arrays without the ambiguities that occur when sparse arrays are used in conventional radars. Angle-estimation performance can be characterized in terms of the local error-performance bound given by the Cramér-Rao bound and in terms of the threshold point given by the SNR at which the estimator deviates significantly from the Cramér-Rao bound. In this paper, we extend results of Bliss, Forsythe, and Richmond on angle-estimation performance as a function of transmit waveform covariance for a MIMO radar. The analysis described in the above work is dependent upon an estimate or test location of a target position. Here, we provide a framework for a Bayesian extension that incorporates knowledge of the priors on the target position probability density. This information affects both the Cramér-Rao bound and the threshold SNR. Consequently, it affects waveform and system optimization.
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U2 - 10.1109/RADAR.2011.5960588
DO - 10.1109/RADAR.2011.5960588
M3 - Conference contribution
AN - SCOPUS:80052457374
SN - 9781424489022
T3 - IEEE National Radar Conference - Proceedings
SP - 503
EP - 508
BT - RadarCon'11 - In the Eye of the Storm
T2 - 2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11
Y2 - 23 May 2011 through 27 May 2011
ER -