Co-array Music under Angle-Independent Nonidealities

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The difference co-array is crucial in determining the number of resolvable sources in direction-of-arrival (DoA) estimation. This virtual array of pairwise sensor position differences enables sparse arrays to identify vastly more sources than sensors. However, the idealized assumptions giving rise to the co-array, such as isolated omnidirectional gain patterns, may not hold in practice. Consequently, the applicability of the co-array model to real-world arrays needs to be investigated thoroughly. In this work, we consider a general class of angle-independent departures from the ideal model caused by nonideal sensors or compression of the array measurements. We study the impact of these nonidealities on DoA estimation using co-array MUSIC, assuming that the array is calibrated and that an infinite number of snapshots is available. We establish that proper use of the calibration data enables unbiased DoA estimation of more sources than sensors. Nonidealities may nevertheless cause subspace swap at low SNR.

Original languageEnglish
Title of host publicationProceedings of 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
Number of pages6
ISBN (Electronic)978-0-7381-3126-9
Publication statusPublished - 3 Jun 2021
MoE publication typeA4 Conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pacific Grove, United States
Duration: 1 Nov 20205 Nov 2020
Conference number: 54

Publication series

NameAsilomar Conference on Signals, Systems, and Computers proceedings
PublisherIEEE Computer Society Press
ISSN (Print)1058-6393
ISSN (Electronic)2576-2303


ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleACSSC
Country/TerritoryUnited States
CityPacific Grove


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