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Research of Fuzzy Inference based on Simplified UKF for large alignment errors in SINS alignment on a swaying base

机译:基于简化UKF的模糊推断在摇曳基础上对循环对准的大对准误差研究

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In the condition of large alignment angles which brought about nonlinear problem in SINS, a precise SINS error model was established on the concept of Euler platform error angles. To reduce the computation of initial alignment in SINS, a Simplified UKF (SUKF) could be used since its state equation was nonlinear while the measurement equation was linear. A novel method combined Fuzzy Inference System (FIS) and system noise's online estimation together on the basis of SUKF was proposed to adjust the system noise covariance, and online improve the performance of the SINS initial alignment. In the SINS alignment for large misalignment angles simulation conditions, the SUKF via FIS showed higher accuracy, better stability and also better real-time performance compared with conventional UKF.
机译:在循环中带来非线性问题的大对准角的条件下,在欧拉平台误差角的概念上建立了精确的SINS错误模型。 为了减少SINS中的初始对准的计算,可以使用简化的UKF(SUKF),因为其状态方程是非线性的,而测量方程是线性的。 提出了一种新的方法组合模糊推理系统(FIS)和系统噪声在SUKF的基础上将在一起进行调整系统噪声协方差,并在线提高SINS初始对齐的性能。 在索斯对准的大量未对准角度模拟条件下,与传统UKF相比,SUKF通过FIS显示出更高的精度,更好的稳定性以及更好的实时性能。

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