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Statistics on noise covariance matrix for covariance fitting-based compressive sensing direction-of-arrival estimation algorithm: For use with optimization via regularization.
Paik, Ji Woong; Hong, Wooyoung; Ahn, Jae-Kyun; Lee, Joon-Ho.
  • Paik JW; Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.
  • Hong W; Department of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
  • Ahn JK; Agency for Defense Development, Jinhae 51678, Republic of Korea.
  • Lee JH; Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.
J Acoust Soc Am ; 143(6): 3883, 2018 Jun.
Article en En | MEDLINE | ID: mdl-29960432
A covariance fitting algorithm for the estimation of direction-of-arrivals of multiple incident signals is addressed in this paper. The scheme takes advantage of the fact that the incident signals are spatially sparse. A previous study has presented the regularization parameters of the covariance fitting for a very large number of snapshots. In this paper, a strategy on how to determine the regularization constant of the covariance fitting for a general number of snapshots is presented. The strategy essentially exploits the norm of the noise covariance matrix. The proposed algorithm has been validated via numerical simulations.

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2018 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2018 Tipo del documento: Article