Kelly Patrick, Majji Manoranjan, Guzmán Felipe
Department of Aerospace Engineering, Texas A&M University, 3141 TAMU, College Station, TX 77843-3141, USA.
Sensors (Basel). 2021 Sep 11;21(18):6101. doi: 10.3390/s21186101.
A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.
提出了一种传感器模型和方法,用于估计使用新型光机械惯性传感器测量的强迫加速度,该模型和方法考虑了随机偏差和测量噪声过程。开发了一种用于估计瞬时传感器偏差的卡尔曼滤波器;然后将此校准步骤的输出用于两种不同的方法来估计施加到传感器的外部加速度。使用模拟测量和对应于经过台架测试的3.76 Hz振荡器的代表值来演示系统的性能。结果表明,所开发的方法在短校准步骤中能够准确估计偏差。这些信息使得能够在延长的运行期间精确估计加速度。这些结果证明了结合当前先进的光机械传感技术,使用所提出的方法可靠地精确估计加速度的可行性。