Programs
- B. Sc. Clinical Nutrition, Dietetics, and Food Science - Undergraduate
- Post Doctoral Fellowship in EEG & Clinical Epileptology - Fellowship
Publication Type : Conference Proceedings
Publisher : 2016 IEEE Annual India Conference, INDICON 2016, Institute of Electrical and Electronics Engineers Inc.
Source : 2016 IEEE Annual India Conference, INDICON 2016, Institute of Electrical and Electronics Engineers Inc. (2016)
Keywords : Adaptive filters, Bandpass filters, Beamforming, Blind adaptive beamforming, Hybrid RLS-LMS-CMA, Kalman filters, MSD and SINR, Nonlinear filtering, Performance comparison, UKF-CMA
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Verified : No
Year : 2016
Abstract : In this paper, we propose an adaptive filtering algorithm, Hybrid Recursive and Least Mean Square-based Constant Modulus Algorithm (RLS-LMS-CMA) for optimized blind beamforming for a Uniform Linear Array (ULA). We consider that Recursive Least Square-based Constant Modulus Algorithm (RLS-CMA) and Least Mean Square-based Constant Modulus Algorithm (LMS-CMA) algorithms are time tested. Therefore, we investigated a combination of RLS-LMS-CMA algorithm. We achieve similar tracking performance when compared to Unscented Kalman Filter-based Constant Modulus Algorithm (UKF-CMA) with minimal computational complexity. Simulations are carried out to compare the performance of RLS-LMS-CMA with other state-of-the-art algorithms. Results obtained indicate that proposed algorithm leads to an equivalent tracking ability and convergence rate of UKF-CMA algorithm. © 2016 IEEE.
Cite this Research Publication : V. Ranganathan, Prabha G, and Narayanankutty Karuppath, “Constant modulus hybrid recursive and least mean squared algorithm performance comparable to unscented Kalman filter for blind beamforming”, in 2016 IEEE Annual India Conference, INDICON 2016, 2016