Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models

Abstract

This 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.

Online Adaptive Parameter Estimation for Quadrotors

“…The aim of this paper is to suggest a novel parameter estimation methodology and a novel adaptive law for estimating 1 2 3 4 5 6 , , , , , a a a a a a and 7 a of system (2).…”

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Tekrarlamalı Gauss-Seidel Algoritması ile İşaret Modelleme

“…Parametrik yöntemler ise gerçekleme açısından iteratif yöntemler ve tekrarlamalı olmak üzere ikiye ayrılabilir. İteratif yöntemlerde yeterli miktarda ölçüm verisi biriktirildikten sonra tek bir yöntem ile veya iteratif bir yöntem kullanılarak parametre tahmin işlemi yapılmaktadır [4][5][6].…”

The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise

“…Mathematical models play a great role in adaptive control, online prediction and signal modeling [1][2][3][4]. The system models of physical plants can be constructed from first principle, but this method cannot work well when the physical mechanism of the true plants is not very clear [5,6].…”