Mixture Modeling based Multikernel Sparse Learning for Directional of Arrival Estimation
Awab Habib Fakih1, S M Shashidhar2
1Awab Habib Fakih, Ph.D Reserach Scholar, Visvesvaraya Technological University, Belagavi (Karnataka), India.
2Dr. S M Shashidhar, Principal, Proudhadeveraya Institute of Technology, [PDIT], Hospe.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 670-675 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11320886S19/19©BEIESP | DOI: 10.35940/ijeat.F1132.0886S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Direction of Arrival (DOA) estimation problem is defined as the problem of Sparse Signal Recovery (SSR) in researches published on the Uniform or Non Uniform array based implementations. This Paper attempts a Multikernel Sparse learning (MSL) approach with mixture modeling for the SSR problem to improve the performance parameters including the PSNR and the RMSE of the estimated sparse signal in the underdetermined condition. The Expectation Maximization algorithm is exploited to obtain the convergence in the mixture modeling MSL method. The virtual array response problem thus developed uses the mixture modeling MSL to estimate the DOA. Matlab based implementation is carried out and the results are found to be satisfactory.
Keywords: About Four Key Words Or Phrases in Alphabetical Order, Separated By Commas.
Scope of the Article: Artificial Intelligence and Machine Learning