Wahyu Sukestyastama Putra
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引用次数: 2

摘要

加速度计在技术发展中是一种有用的传感器。目前,智能手机、导航设备和可穿戴设备上都有这种加速度计。然而,将传感器输出信号处理成可解释的数据并不容易。这是因为加速度计传感器的输出具有显著的噪声。在这项研究中,作者感兴趣的是开发一种使用卡尔曼滤波器的估计方法。卡尔曼滤波是一种估计器,因此期望传感器数据更能抵抗噪声干扰。在本研究中,作者对2步卡尔曼滤波器进行了创新。由于使用1步对估计结果仍然存在噪声,因此进行了研究。通过对算法仿真结果的分析,可以得出卡尔曼滤波两步算法在估计加速度计传感器输出方面具有良好的性能。与卡尔曼滤波1步算法相比,卡尔曼滤波2步算法具有更小的平均误差估计,并且能够比卡尔曼滤波1步方法更快地达到恒定/稳定状态
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementasi Algoritma 2 Step Kalman Filter Untuk Mengurangi Noise Pada Estimasi Data Accelerometer
An accelerometer is a useful sensor in technological development. Currently, the accelerometer is found on smartphone devices, navigation devices, and wearable devices. However, processing the sensor output signal into data that can be interpreted is not easy. This is because the output of an accelerometer sensor has significant noise. In this study, the authors are interested in developing an estimation method using a Kalman Filter. Kalman filter is an estimator so it is expected that the sensor data are more resistant to noise interference. In this study, the author innovated the 2 step Kalman filter. The study was conducted because the use of 1 step still has noise on the estimation results. Based on the analysis of the algorithm simulation results, it can be concluded that the Kalman filter 2-step algorithm has good performance in estimating the accelerometer sensor output. When compared with the Kalman filter 1 step algorithm, the Kalman filter 2 step algorithm has a smaller average error estimation and is able to achieve a constant/stable condition faster than the Kalman filter 1 step method
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