Asymptotic performance of optimal gain-and-phase estimators of sensor arrays

Qi Cheng; Yingbo Hua; Petre Stoica

doi:10.1109/78.887058

Asymptotic performance of optimal gain-and-phase estimators of sensor arrays

Qi Cheng, Yingbo Hua, Petre Stoica

School of Engineering, Design & Built Environment

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

Abstract

For estimating angles of arrival, there are three well known algorithms: weighted noise subspace fitting (WNSF), unconditional maximum likelihood (UML), and conditional maximum likelihood (CML). These algorithms can also be used for estimating/calibrating the gains-and-phases of sensor arrays, assuming the angles of arrival are known. We show that the WNSF algorithm with an optimal weight has the same statistical efficiency as the UML algorithm but more efficient than the CML algorithm. This conclusion was known for angles of arrival estimation and is now confirmed for gains-and-phases calibration. Computationally, the WNSF algorithm is shown to be more attractive than the other two as it can be implemented via a quadratic minimization procedure for arbitrarily shaped arrays.

Original language	English
Pages (from-to)	3587-3590
Number of pages	4
Journal	IEEE Transactions on Signal Processing
Volume	48
Issue number	12
DOIs	https://doi.org/10.1109/78.887058
Publication status	Published - Dec 2000

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Asymptotic performance of optimal gain-and-phase estimators of sensor arrays

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