Name
Abstract | ||
|---|---|---|
An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2014.2362932 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
kalman filters,array signal processing,covariance matrices,least mean squares methods,measurement errors,nonlinear filters,kalman filter-style state space model,lms-cma algorithm,rls-cma algorithm,ukf-cma,auxiliary parameter,blind uniform linear beamforming,constant modulus blind adaptive beamforming,measurement noise covariance matrices,recursive least mean square-based cm,unscented kalman filter-based constant modulus adaptation algorithm,blind beamforming,constant modulus,state space model,unscented kalman filter,noise,vectors | Beamforming,Extended Kalman filter,Fast Kalman filter,Control theory,Covariance intersection,Unscented transform,Kalman filter,Invariant extended Kalman filter,Recursive least squares filter,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 4 | 1070-9908 |
Citations | PageRank | References |
5 | 0.46 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md. Zulfiquar Ali Bhotto | 1 | 77 | 6.42 |
| Ivan V. Bajic | 2 | 101 | 9.70 |