Non-Negative Matrix Factorization Based UKF Algorithm for Constant Modulus Signals in Adaptive Beamforming ()
ABSTRACT
Blind adaptive beamforming is getting appreciated for its various applications in
contemporary communication systems where sources are statistically dependent or
independent that are allowed to formulate new algorithms. Qualitative performance
and time complexity are the main issues. In this paper, we propose a technique for
constant modulus signals applying basic non-negative matrix factorization (BNMF)
in blind adaptive beamforming environment. We compared the existing Unscented
Kalman Filter based Constant Modulus Algorithm (UKF-CMA) with proposed
NMF-UKF-CMA algorithm. We see there is a better improvement of sensor array
gain, signal to interference plus noise ratio (SINR) and mean squared deviation
(MSD) as the noise variance and the array size increase with reduced computational
complexity with the UKF-CMA.
Share and Cite:
Vignesh, R. and Narayanankutty, K. (2016) Non-Negative Matrix Factorization Based UKF Algorithm for Constant Modulus Signals in Adaptive Beamforming.
Open Journal of Antennas and Propagation,
4, 119-127. doi:
10.4236/ojapr.2016.43009.