Browsing by Author "Jalal, Babur"

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    Low Complex Direction of arrival Estimation method based on Adaptive Filtering Algorithm
    (The Journal of Engineering, 2019) Jalal, Babur; Yang, Xiaopeng; Igambi, Denis; Hassan, Tehseen Ul; Ahmad, Zeeshan
    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.
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    Robust Adaptive Beamforming Based on Desired Signal Power Reduction and Output Power of Spatial Matched Filter
    (IEEE Access, 2018) Igambi, Denis; Yang, Xiaopeng; Jalal, Babur
    The performance of the conventional beamformers degrades in the presence of desired signal in the data samples and array steering vector (ASV) mismatch. Many beamformers have been proposed to improve the performance of standard Capon beamformer. However, the performance of these beamformers is affected by a number of factors, such as a number of data samples or sensors and signal-to-noise ratio. Moreover, the existing beamformers are also sensitive to the ASV mismatch of desired signal. In this paper, two robust adaptive beamformers are proposed to overcome the problems associated with these beamformers. The proposed beamformers have two pre-processing steps. First, the ASV of desired signal is estimated by computing the correlation between the nominal ASV and the eigenvectors corresponding to the dominant eigenvalues. Second, the power of desired signal in the sample covariance matrix is reduced by estimating the desired signal covariance matrix from the output power of spatial matched filter and noise covariance matrix. Subsequently, the matrix regularization is used to estimate the desired sample covariance matrix. In the first beamformer, the desired sample covariance matrix is constructed from the sample covariance matrix with the reduced desired signal power and the diagonal loading based on the output power of spatial matched filter, whereas in the second beamformer, the desired sample covariance matrix is constructed from the sample covariance matrix with the reduced desired signal power and the reconstructed interference-plus-noise matrix loaded with the output power of spatial matched filter. The proposed beamformers can provide a good performance in the presence of desired signal in the data samples and ASV mismatch as shown in the simulation results.