Publication Type:

Conference Paper

Source:

2016 IEEE Annual India Conference, INDICON 2016, Institute of Electrical and Electronics Engineers Inc. (2016)

ISBN:

9781509036462

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015234220&doi=10.1109%2fINDICON.2016.7838889&partnerID=40&md5=26f95852ab938f5fd2729586eb19ec2f

Keywords:

Adaptive filters, Bandpass filters, Beamforming, Blind adaptive beamforming, Hybrid RLS-LMS-CMA, Kalman filters, MSD and SINR, Nonlinear filtering, Performance comparison, UKF-CMA

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.

Notes:

cited By 0; Conference of 2016 IEEE Annual India Conference, INDICON 2016 ; Conference Date: 16 December 2016 Through 18 December 2016; Conference Code:126283

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.

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