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Sensor fusion for structural tilt estimation using an acceleration-based tilt sensor and a gyroscope

机译：使用基于加速的倾斜传感器和陀螺仪的结构倾斜估计的传感器融合

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摘要

A tilt sensor can provide useful information regarding the health of structural systems. Most existing tilt sensors are gravity/acceleration based and can provide accurate measurements of static responses. However, for dynamic tilt, acceleration can dramatically affect the measured responses due to crosstalk. Thus, dynamic tilt measurement is still a challenging problem. One option is to integrate the output of a gyroscope sensor, which measures the angular velocity, to obtain the ti however, problems arise because the low-frequency sensitivity of the gyroscope is poor. This paper proposes a new approach to dynamic tilt measurements, fusing together information from a MEMS-based gyroscope and an acceleration-based tilt sensor. The gyroscope provides good estimates of the tilt at higher frequencies, whereas the acceleration measurements are used to estimate the tilt at lower frequencies. The Tikhonov regularization approach is employed to fuse these measurements together and overcome the ill-posed nature of the problem. The solution is carried out in the frequency domain and then implemented in the time domain using FIR filters to ensure stability. The proposed method is validated numerically and experimentally to show that it performs well in estimating both the pseudo-static and dynamic tilt measurements.
机译：倾斜传感器可以提供关于结构系统的健康的有用信息。大多数现有的倾斜传感器是基于重力/加速度，可以提供精确测量静态响应。然而，对于动态倾斜，加速度可以显着影响由于串扰引起的测量响应。因此，动态倾斜测量仍然是一个具有挑战性的问题。一种选择是集成陀螺仪传感器的输出，测量角速度，以获得倾斜;然而，出现问题，因为陀螺仪的低频灵敏度差。本文提出了一种新的动态倾斜测量方法，从基于MEMS的陀螺和基于加速的倾斜传感器融合在一起。陀螺仪在较高频率下提供倾斜的良好估计，而加速度测量用于估计较低频率的倾斜度。 Tikhonov规则化方法用于将这些测量融合在一起并克服问题的不良性质。解决方案在频域中执行，然后使用FIR滤波器在时域中实现以确保稳定性。在数字上和实验验证所提出的方法以表明它在估计伪静态和动态倾斜测量时表现良好。

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