Asymptotically Optimal Blind Calibration of Acoustic Vector Sensor Uniform Linear Arrays

Amir Weiss, Boaz Nadler, Arie Yeredor

Research output: Contribution to conferencePaperpeer-review

Abstract

We study the blind calibration problem of uniform linear arrays of acoustic vector sensors for narrowband Gaussian signals, and propose an improved, asymptotically optimal blind calibration scheme. Following recent work by Ramamohan et al., we exploit the special (block-Toeplitz) structure of the underlying signals’ spatial covariance matrix. However, we offer a substantial improvement over their ordinary Least Squares (LS)-based approach: Using asymptotic approximations we obtain Optimally-Weighted LS estimates of the sensors’ gains and phases offsets. We show via simulations that our estimates exhibit near-optimal performance, with improvements reaching more than an order of magnitude in the mean squared estimation errors of the calibration parameters, as well as in directions of-arrival estimation.
Original languageEnglish
Pages4677-4681
Number of pages5
DOIs
StatePublished - May 2020
Event45th IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 45th

Conference

Conference45th IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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