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Journal of Applied Mathematics
Volume 2014, Article ID 901539, 11 pages
http://dx.doi.org/10.1155/2014/901539
Research Article

A Nonlinear Multiparameters Temperature Error Modeling and Compensation of POS Applied in Airborne Remote Sensing System

1School of Instrument Science and Opto-Electronic Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2Science & Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument & Navigation System Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
3Xi’an Institute of Optics and Precision Mechanics (XIOPM), CAS, Xi’an 710119, China

Received 10 March 2014; Accepted 13 May 2014; Published 9 June 2014

Academic Editor: Guiming Luo

Copyright © 2014 Jianli Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The position and orientation system (POS) is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.