Adaptive Beamforming Method for MIMO Antenna Array with Constrained Mean Square Error
Mamatha M.C1, H.C. Sateesh Kumar2
1Mamatha MC is currently pursuing PhD in Sapthagiri College of Engineering, Bangalore, under Visvesvaraya Technological University, India.
2Dr Sateesh Kumar H C is currently is a professor and Head, Department of ECE, Sapthagiri College of Engineering, Bangalore, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1095-1099 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3791079220/2020©BEIESP | DOI: 10.35940/ijrte.B3791.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Adaptive beam forming with Multikernel based Bayesian learning method beam forming on Uniform Linear Array (ULA) antennas for better localization. Undetermined source localization problem is solved using the Multikernel Sparse Bayesian Learning framework. Beam forming problem is considered the undetermined source localization problem and solved using the adaptive method. The Degree of Freedom (DOF) is increased using the adaptive nature of the manifold matrix while maintaining the same number of antennas. The response model that adaptively adjusts the manifold matrix in the Sparse Bayesian problem uses the Multikernel framework. MATLAB based implementation thus carried out on the ULA clearly exhibits better results over the single kernel model. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) with Signal to Noise Ratio (SNR) variation is obtained to evaluate the performance of the proposed implementation. The performance obtained is found to be satisfactory and is at par with the recent previous implementation.
Keywords: Direction of Arrival Estimation, Multikernel Sparse Representation, Basis Pursuit Methods.