Abstract
“Central difference Kalman filtering (CDKF)” is proposed as a new state of the art approach for carrier frequency offset estimation in orthogonal frequency division multiplexing systems. The parameter of interest to be estimated in this problem is a static value rather than a dynamically varying parameter. Therefore, classical approaches (e.g., maximum likelihood method or best linear unbiased estimator) might be more pertinent than Bayesian approaches if it is assumed to be a deterministic value. Nonetheless, it is shown and justified that a recently developed extended Kalman variant, i.e., CDKF, outperforms previously proposed methods in terms of mean squared error with efficient processing speed. Particularly, it is shown that CDKF outperforms recently proposed Gaussian particle filter for this one-dimensional static parameter estimation problem.
Published Version
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