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Nonlinear least squares algorithm with embedded Kalman filter for Bragg wavelength detection in fiber Bragg grating sensors

YU TANG1,* , YUN CHUNG CHU1, CHI CHIU CHAN2, JIAN SUN2

Affiliation

  1. School of Electronic Engineering, Nanyang Technological University, Singapore 639798
  2. School of Chemical and Biological Engineering, Nanyang Technological University, Singapore 637457

Abstract

In a fiber Bragg grating (FBG) sensing system, the accuracy of Bragg wavelength detection is much affected by the presence of unwanted interferometric signal in the system. In this paper, a nonlinear least-square (NLS) algorithm with an embedded Kalman filter is used to remove this unwanted signal to enhance the measurement accuracy. This hybrid approach avoids the disadvantage of a pure NLS estimation, which is rather model sensitive, and the disadvantage of an extended Kalman filter, which might fail to converge. Computer simulations and experimental results are provided to demonstrate the effectiveness of this proposed method. Improvements of the accuracy in Bragg wavelength detection are observed.

Keywords

Fiber Bragg grating, Nonlinear least squares, Kalman filter.

Citation

YU TANG, YUN CHUNG CHU, CHI CHIU CHAN, JIAN SUN, Nonlinear least squares algorithm with embedded Kalman filter for Bragg wavelength detection in fiber Bragg grating sensors, Optoelectronics and Advanced Materials - Rapid Communications, 1, 4, April 2007, pp.145-148 (2007).

Submitted at: Jan. 25, 2007

Accepted at: March 14, 2007