文章基本信息

标题：Implementing a Standard Gradient Descent Algorithm on FPGA Embedded Processor for Two-Dimensional Field Sensor Calibration
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作者：Herman I. Verinaz ; Edgar M. Vela ; Ronald A. Ponguillo 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：629-634
出版社：Newswood and International Association of Engineers
摘要：To measure a vector field it is needed to use a field sensor. This type of sensors measures both the direction and magnitude of a vector, e.g., magnetometers and accelerometers. These devices divide the physical space in two or three dimensions so that the sensor detects the components of the field in two or three axes respectively. Usually, before using the data acquired from this sort of sensors, it is necessary to do a previous mathematical processing to guarantee that the measured field is truly proportional to the actual vector field. This is called calibration. This work was developed for the self-calibration of a twodimensional magnetometer used in a Robotracer for robotics competitions. However, the calibration can be done for any two-dimensional field sensor. For the proposed process, the sensor must be surrounded by a constant field because the calibration algorithm adjusts the sensor data using the fact that the magnitude of the field is constant. The algorithm uses a linear model for the calibration of the sensor data. Then, to find the optimal values for the calibration constants, a Standard Gradient Descent algorithm was implemented. The results of the implementation for different devices are presented in this work. It is showed the results for three accelerometers, MPU-6050, ADXL345 and FALCON-GX digital IMU. Time of execution is analyzed for each device using the soft-core processor NIOS II. The processor was running at 100 MHz of clock with 50% duty cycle. It is inside the FPGA EP4CE22F17C6N, Altera´s Cyclone IV family. All this work was done using the DE0-Nano development board.
关键词：Angle Estimation; Dual Extended Kalman Filter; Sensor Fusion; Kalman Filter; Tilt Estimation;