Fiber optic gyroscope temperature drift error compensation method based on optimized least square-support vector machine (LS-SVM)

Abstract

The invention discloses a fiber optic gyroscope temperature drift error compensation method based on an optimized least square-support vector machine (LS-SVM). The method comprises the following specific steps: (1) initializing an artificial fish swarm algorithm (AFSA) optimization algorithm and LS-SVM model parameters; (2) determining sample data for model training and testing according to fiber optic gyroscope output data, and preprocessing the model input data; (3) training a LS-SVM model by using the training data, and continuously iterating the optimized model parameters through the AFSA algorithm; (4) predicting output of the fiber optic gyroscope according to the trained model, and performing temperature drift error compensation. According to the method, the optimized parameters under set conditions can be acquired according to the training sample by adopting the AFSA algorithm with high global optimization capacity, the condition that local optimization is possibly caused in the optimization process is avoided, and the prediction accuracy of the model can be improved to a certain degree.

Description

technical field [0001] The invention relates to the technical field of inertia, in particular to an optical fiber gyroscope temperature drift error compensation method based on optimized LS-SVM. Background technique [0002] Fiber optic gyroscope (Fiber Optic Gyroscope, FOG) is an optical sensor based on the Sagnac effect. Since the components of the device are sensitive to temperature changes, the temperature change of the working environment will affect the output accuracy of the fiber optic gyroscope, thus restricting the Improved accuracy of inertial navigation. In order to suppress this temperature drift error, many researchers have done a lot of work. On the one hand, starting from the internal structure of the gyroscope, the working environment temperature of the coil, light source and optical devices is more stable through structural design improvements; on the other hand, by analyzing the temperature characteristics of the fiber optic gyroscope, a temperature drift...

AI Technical Summary

Problems solved by technology

At present, optimization methods such as gradient descent algorithm, genetic algorithm and particle swarm optimization are generally used, but the above methods will be lacking in terms of convergence speed or global optimization.

Images