Kalman Filter for Noise Reducer on Sensor Readings release_qnwkqtdqdbg7pnerin36x5vbme

by Alfian Ma'arif, Iswanto Iswanto, Aninditya Anggari Nuryono, Rio Ikhsan Alfian

Published in Signal and Image Processing Letters by ASCEE Publications.

2019   p11-22


Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.
In application/xml+jats format

Archived Files and Locations

application/pdf  943.2 kB
simple.ascee.org (publisher)
web.archive.org (webarchive)
Read Archived PDF
Type  article-journal
Stage   published
Date   2019-07-19
Journal Metadata
Not in DOAJ
Not in Keepers Registry
ISSN-L:  2714-6669
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 1eafe8e1-26dc-4b71-8f86-6d902a320dad