Abstract
Traditional compensation methods based on temperature-related parameters are not effective for complex total reflection prism laser gyro (TRPLG) bias variation. Because the high frequency oscillator voltage (UHFO) fundamentally affects the TRPLG bias, and the UHFO has a stronger correlation with the TRPLG bias when compared with the temperature, an introduction of UHFO into the TRPLG bias compensation can be evaluated. In consideration of the limitations of least squares (LS) regression and multivariate stepwise regression, we proposed a compensation method for TRPLG bias based on iterative re-weighted least squares support vector machine (IR-LSSVM) and compared with LS regression, stepwise regression, and LSSVM algorithm in large temperature cycling experiments. When temperature, slope of temperature variation, and UHFO were selected as inputs, the IR-LSSVM based on myriad weight function improved the TRPLG bias stability by 61.19% to reach the maximum and eliminated TRPLG bias drift. In addition, the UHFO proved to be the most important parameter in the process of TRPLG bias compensation; accordingly, it can alleviate the shortcomings of traditional compensation based on temperature-related parameters and can greatly improve the TRPLG bias stability.
Highlights
The total reflection prism laser gyros (TRPLGs) are applied in several systems [1]
Slope of temperature variation, and UHFO were selected as inputs, the IR-least squares support vector machine (LSSVM) based on myriad weight function improved the TRPLG bias stability by
Temperature, slope of temperature variation, UHFO, and TRPLG output were inputs to the microprocessor through the serial port, and the micro-processor compensated for the TRPLG bias using the TRPLG bias compensation algorithm
Summary
The total reflection prism laser gyros (TRPLGs) are applied in several systems [1]. For example, the I42-1-C strapdown navigation system of civilian aircrafts IL-96-300 and TU-204 is based on TRPLG. Some techniques utilize non-convex loss functions to improve robustness [28,29,30,31], which is known as robust LSSVM (R-LSSVM) To overcome the latter disadvantage, Chen et al proposed a sparse R-LSSVM (SR-LSSVM) to achieve a sparse solution of the primal R-LSSVM after obtaining a low-rank approximation of the kernel matrix [32]. These LSSVM-based improved algorithms have not been applied to the compensation of RLG bias.
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