Mathematics (Basel), 2022-03, Vol.10 (6), p.923
2022
Details
- Autor(en) / Beteiligte
- Titel
- Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources
- Ist Teil von
- Mathematics (Basel), 2022-03, Vol.10 (6), p.923
- Ort / Verlag
- Basel: MDPI AG
- Erscheinungsjahr
- 2022
- Link zum Volltext
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2227-7390eISSN: 2227-7390DOI: 10.3390/math10060923Titel-ID: cdi_doaj_primary_oai_doaj_org_article_fca17611d9df43ceb164b36bf901c227
- Format
- –
- Schlagworte
- Accuracy, Algorithms, Arrays, Bayesian analysis, Calibration, Data models, DOA estimation, Efficiency, impulsive noise, Machine learning, Mathematics, Methods, Mutual coupling, Noise, non-circular signal, Optimization, Parameter estimation, Robustness, Signal processing, sparse Bayesian learning