Real-time FPGA-based Kalman filter for constant and non-constant velocity periodic error correction
Real-time FPGA-based Kalman filter for constant and non-constant velocity periodic error correction
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DOI：
10.1016/j.precisioneng.2016.11.013
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发表时间：
2017-04-01
影响因子：
3.6
通讯作者：
Ellis, Jonathan D.
中科院分区：
文献类型：
--
作者：
Wang, Chen;Burnham-Fay, Ethan D.;Ellis, Jonathan D.
位移测量干涉法具有高分辨率、高动态范围等优点，在位移测量和传感器标定中有着广泛的应用。由于干涉仪中的光束泄漏、不理想的偏振分量和鬼影反射，位移测量存在周期性误差，其节距为多普勒频率的倍数次谐波。在动态测量中，周期误差通常在纳米量级，影响动态测量精度。本文提出了一种基于扩展卡尔曼滤波的周期误差实时估计和修正方法，该方法既能处理定速运动又能处理变速运动。该算法在一个FPGA中的专用硬件架构上实现。其在吞吐量和资源使用方面与传统实现方式相比具有优势。测量验证表明，该方法能有效地消除恒速和变速运动的周期误差，残差达到干涉仪的背景噪声水平。(C)2016 Elsevier Inc.保留所有权利。
Displacement measuring interferometry has high resolution and high dynamic range, which is widely used in displacement metrology and sensor calibration. Due to beam leakage in the interferometer, imperfect polarization components, and ghost reflections, the displacement measurement suffers from periodic error, whose pitch is multiple harmonics of the Doppler frequency. In dynamic measurements, periodic error is usually on the order of nanometers, which impacts the dynamic measurement accuracy. This paper presents an approach to estimate and correct periodic error in real time based on an extended Kalman filter, which has the capability to deal with both constant and non-constant velocity motions. This algorithm is implemented on an application-specific hardware architecture in an FPGA. which has advantages in throughput and resource usage compared with conventional implementations. The measurement validation shows that this approach can effectively eliminate the periodic error for both constant and non-constant velocity motion, and the residual error reaches to the level of the background noise of the interferometer. (C) 2016 Elsevier Inc. All rights reserved.