Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
AbstractThis paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models. View Full-Text
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Liu, S.; Xu, L.; Ding, F. Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models. Algorithms 2017, 10, 118.
Liu S, Xu L, Ding F. Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models. Algorithms. 2017; 10(4):118.Chicago/Turabian Style
Liu, Siyu; Xu, Ling; Ding, Feng. 2017. "Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models." Algorithms 10, no. 4: 118.
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