Low complex direction of arrival estimation method based on adaptive filtering algorithm
Autor: | Babur Jalal, Xiaopeng Yang, Denis Igambi, Tehseen Ul Hassan, Zeeshan Ahmad |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
eigenvalues and eigenfunctions
adaptive filters covariance matrices least mean squares methods direction-of-arrival estimation conventional subspace decomposition-based direction arrival estimation method eigenvalue decomposition spatial covariance matrix DOA estimation method fixed step size mean square algorithm variable step size LMS algorithm estimated error signal low complex direction adaptive filtering algorithm Engineering (General). Civil engineering (General) TA1-2040 |
Zdroj: | The Journal of Engineering (2019) |
Druh dokumentu: | article |
ISSN: | 2051-3305 |
DOI: | 10.1049/joe.2019.0276 |
Popis: | Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their implementation is difficult in real-time applications. However, a DOA estimation method based on fixed step size least mean square (LMS) algorithm has overcome this deficiency but the suitable selection of step size is very difficult. A DOA estimation method based on the variable step size LMS algorithm is proposed in this article. In the proposed method, the step size is updated by using the estimated error signal. The spatial spectrum is obtained by the reciprocal of array pattern, where the peak values indicate the estimated DOAs of signals. The proposed method can provide better performance with low computational cost. The performance of proposed method is verified by the simulations and compared with some existing methods. |
Databáze: | Directory of Open Access Journals |
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