Comparison of compensation methods on RLG Temperature error and their application in POS

Abstract

Motion compensation technology based on Ring Laser Gyroscope (RLG) Position and Orientation System (POS) enormously improves the imaging quality and operation efficiency of airborne remote sensing systems. However, bias error of RLG, aroused by temperature variation, severely deteriorates the measurement precision of POS. To solve this problem, several error modeling and compensation techniques have been devised, including Linear Least Squares Fitting (LLSF), RBF Neural Network (RBF NN) and Least Square Support Vector Machine (LS SVM). Theoretical basis of these methods are introduced. Comparison among them with subjects on model complexity, computing speed, precision and generalization performance is drawn, and conclusions are verified via temperature circling experiment of real RLG. Approach based on LLSF acquires the advantages of high computing speed and low hardware resource occupancy, while superiority on precision and generalization performance of LS SVM is obvious. According to the hostile working environment and high precision requirement of POS, methods based on LLSF and LS SVM are adopted to work under online and offline modes of POS, which meet the demands of computing speed and compensation precision respectively. Airborne flight experiment results demonstrate that, six groups' average online inertial navigation error of RLG POS after 4 hours' flight was 9.5775 nmiles, while the average offline inertial navigation error was 4.0661 nmiles. Such result satisfied the application requirement of high resolution InSAR.