2016•Unpublished venueRequires access

Low cost MEMS gyroscope and accelerometer implementation without Kalman Filter for angle estimation

Nur Hazliza Ariffin, Norhana Arsad, Badariah Bais

Open publisher page 26 citations

Abstract

These days, the accuracy of the measured tilt angle is extremely important and it is always a challenge especially for robotics application. MEMS (micro-electro-mechanical system) gyroscope and accelerometer has been widely used to measure the tilt angle along the axis. However, various studies highlighted that MEMS gyroscope and accelerometer has weakness to gain accuracy of orientation. In order to cater the low accuracy, data from the MEMS gyroscope and accelerometer require a filter where the data from both sensors will be combined by using the complementary filter. Thus, digital complementary filter is developed in such a way that the strength of one sensor will be used to overcome the weaknesses of the other sensor which is complementary to each other. The filtering will be demonstrated using MEMS gyroscope and accelerometer in different position throughout the measurement for analysis and angle estimation via complementary filter. From the simulation result, it is evident that the complementary filter method is achieved with responsive and accurate angle of 0 to 90 degrees with same as actual position at factor value of 0.97. In addition, it is not sensitive to horizontal accelerations or to gyroscope drift with microcontroller-friendly filtering system.

About this research paper

What this paper is about

These days, the accuracy of the measured tilt angle is extremely important and it is always a challenge especially for robotics application. MEMS (micro-electro-mechanical system) gyroscope and accelerometer has been widely used to measure the tilt angle along the axis. However, various studies highlighted that MEMS gyroscope and accelerometer has weakness to gain accuracy of orientation. In order to cater the low accuracy, data from the MEMS gyroscope and accelerometer require a filter where the data from both sensors will be combined by using the complementary filter. Thus, digital complementary filter is developed in such a way that the strength of one sensor will be used to overcome the weaknesses of the other sensor which is complementary to each other. The filtering will be demonstrated using MEMS gyroscope and accelerometer in different position throughout the measurement for analysis and angle estimation via complementary filter. From the simulation result, it is evident that the complementary filter method is achieved with responsive and accurate angle of 0 to 90 degrees with same as actual position at factor value of 0.97. In addition, it is not sensitive to horizontal accelerations or to gyroscope drift with microcontroller-friendly filtering system.

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Method / approach

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Available abstract

These days, the accuracy of the measured tilt angle is extremely important and it is always a challenge especially for robotics application. MEMS (micro-electro-mechanical system) gyroscope and accelerometer has been widely used to measure the tilt angle along the axis. However, various studies highlighted that MEMS gyroscope and accelerometer has weakness to gain accuracy of orientation. In order to cater the low accuracy, data from the MEMS gyroscope and accelerometer require a filter where the data from both sensors will be combined by using the complementary filter. Thus, digital complementary filter is developed in such a way that the strength of one sensor will be used to overcome the weaknesses of the other sensor which is complementary to each other. The filtering will be demonstrated using MEMS gyroscope and accelerometer in different position throughout the measurement for analysis and angle estimation via complementary filter. From the simulation result, it is evident that the complementary filter method is achieved with responsive and accurate angle of 0 to 90 degrees with same as actual position at factor value of 0.97. In addition, it is not sensitive to horizontal accelerations or to gyroscope drift with microcontroller-friendly filtering system.

Key concepts: Gyroscope, Accelerometer, Vibrating structure gyroscope, Kalman filter, Tilt (camera), Filter (signal processing), Tilt sensor, Computer science

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