Non-iterative blind calibration of nested arrays with asymptotically optimal weighting

Amir Weiss, Arie Yeredor

Research output: Contribution to journalConference articlepeer-review


Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical"array structures, such as uniform linear arrays, not as many are found in the context of the more "modern"sparse arrays. In this paper, we present a novel blind calibration method for 2-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimallyweighted LS joint estimation of the sensors' gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to K-level arrays (K > 2), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation.

Original languageEnglish
Pages (from-to)4630-4634
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021


  • Blind calibration
  • Maximum likelihood
  • Nested arrays
  • Optimally-weighted least squares
  • Sparse arrays


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