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A plethora of problems related to signal estimation and direction can be effectively solved by using a sensor array. To this end, uniform estimation for all direction-of-arrivals (DOAs) with low computational complexity is an indispensable step for numerous applications. In this article, we present the DOA estimation method exploiting the recently proposed fast variable step size least mean square (FVSSLMS) method (Jalal et al., 2020). In most of the DOA estimation works, a uniform linear array (ULA) was used. Nonetheless, a ULA does not perform well for the DOA estimation when the signal sources are impinging close to the array endfire. To circumvent this issue, we modify the structure of ULA by displacing its four sensors to the top and bottom of the first and the last sensor. Computer simulations demonstrate that the proposed method provides uniform estimation performance for all DOAs with reduced computation complexity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Conventional direction-of-arrival (DOA) estimation methods based on subspace decomposition are computationally complex and difficult to implement in practical applications. Although a fixed-step-size least mean square (FSS-LMS) algorithm for DOA estimation has overcome the complexity problem; however, the manual selection of suitable step size is difficult. In this letter, a variable-step-size LMS algorithm is proposed for DOA estimation. In the proposed method, the step size is updated by using the estimated signal power and the cumulative sum of squared instantaneous error, and 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 choose step size adaptively and provide better performance with low computational complexity. The performance of proposed method is verified by the numerical simulations and compared with some existing methods. [ABSTRACT FROM AUTHOR]

When a small number of snapshots are used, the performance of the direction-of-arrival (DOA) estimation method based on the least squares (LS) degrades severely because of inadequate estimation of the covariance matrix. Although the subspace-based DOA estimation methods were proposed to improve the performance of DOA estimation method based on the LS; however these methods are computationally complex, especially for a large number of array elements. In this Letter, the DOA estimation method based on the LS is improved by reconstructing the covariance matrix with diagonal loading, where the diagonal loading factor is computed automatically by estimating the signal power. The reciprocal of the array pattern is taken to calculate the spatial spectrum, where the peak values correspond to the estimated DOAs of signals. The proposed method can achieve better performance with few snapshots and low computational complexity. The effectiveness of the proposed method is verified by the numerical simulations. [ABSTRACT FROM AUTHOR]