TY - JOUR
T1 - Preprocessing for Direction Finding with Minimal Variance Degradation
AU - Weiss, Anthony J.
N1 - Funding Information:
Manuscript received July 26, 1992; revised July 28, 1993. This work was supported by the United States Army Research Office under Contract DAAL03-91 -C-0022 sponsored by U.S. Army Communications Electronics Command, Center for Signals Warfare. The associate editor coordinating the review of this paper and approving it for publication was Prof. Daniel Fuhrmann. A. J. Weiss is with the Electrical Engineering Department, Tel-Aviv University, Tel-Aviv, Israel. B. Friedlander is with the Department of Electrical and Computer Engineering, University of Califomia, Davis, CA 95616 USA. IEEE Log Number 9400390.
PY - 1994/6
Y1 - 1994/6
N2 - Numerous authors have advocated the use of preprocessing in high-resolution direction of arrival (DOA) algorithms. The benefits cited include reduced computation, improved performance in spatially colored noise, and enhanced resolution. We identify the preprocessing matrices that provide minimum variance estimates of DOA for a number of models and algorithms. We examine the Cramer-Rao Bound (CRB) for Gaussian signals, the CRB for deterministic signals, and the asymptotic variance of the MUSIC estimator for preprocessed data. We also study the effect of array manifold errors on the direction estimates. As expected, the optimal preprocessor requires knowledge of the source directions. However, we show that performance that is close to optimal can be obtained with only approximate knowledge of the source directions (with an error not exceeding the array beamwidth) if the design rules outlined in this paper are used.
AB - Numerous authors have advocated the use of preprocessing in high-resolution direction of arrival (DOA) algorithms. The benefits cited include reduced computation, improved performance in spatially colored noise, and enhanced resolution. We identify the preprocessing matrices that provide minimum variance estimates of DOA for a number of models and algorithms. We examine the Cramer-Rao Bound (CRB) for Gaussian signals, the CRB for deterministic signals, and the asymptotic variance of the MUSIC estimator for preprocessed data. We also study the effect of array manifold errors on the direction estimates. As expected, the optimal preprocessor requires knowledge of the source directions. However, we show that performance that is close to optimal can be obtained with only approximate knowledge of the source directions (with an error not exceeding the array beamwidth) if the design rules outlined in this paper are used.
UR - http://www.scopus.com/inward/record.url?scp=0028446070&partnerID=8YFLogxK
U2 - 10.1109/78.286963
DO - 10.1109/78.286963
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AN - SCOPUS:0028446070
SN - 1053-587X
VL - 42
SP - 1478
EP - 1485
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 6
ER -