Robust estimation of the number of coherent radar signal sources using deep learning

Autor: John Rogers, John E. Ball, Ali C. Gurbuz
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IET Radar, Sonar & Navigation, Vol 15, Iss 5, Pp 431-440 (2021)
Druh dokumentu: article
ISSN: 1751-8792
1751-8784
DOI: 10.1049/rsn2.12047
Popis: Abstract A deep‐learning‐based approach to estimating the number of coherent sources in radar is presented. A proper estimate of the number of sources in a signal enables improved angle‐of‐arrival (AoA) estimation common in applications such as radar, sonar, and communication systems. Many AoA estimators utilised in these areas require accurate estimates of the number of sources for enhanced performance. Herein, a robust method that performs well under the existence of coherent sources is developed. The proposed method is based on deep learning and it is shown to perform better than state‐of‐the‐art versions of the Akaike Information Criteria (AIC), Minimum Description Length (MDL), and Exponentially Embedded Families (EEF) estimators, which employ spatial smoothing of the covariance matrix to handle coherent signals.
Databáze: Directory of Open Access Journals
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