Low Complexity Sensor Fusion Solution for Accurate Estimation of Gravity and Linear Acceleration

Authored by: Ramasamy Kannan

Multisensor Attitude Estimation

Print publication date:  August  2016
Online publication date:  November  2016

Print ISBN: 9781498745710
eBook ISBN: 9781315368795
Adobe ISBN:

10.1201/9781315368795-15

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Abstract

Motion sensors are used in mobile and wearable applications for improving user experience. They are used in areas of motion detection, gesture recognition for health, fitness, and sports. Any motion is a combination of linear motion and rotational motion in 3D space. Accelerometer sensor measures motion which is a combination of linear motion and limited rotational motion. Accelerometer data is a combination of linear acceleration components which are a measure of the linear motion and gravity components which is a measure of limited rotational motion. The computation of gravity and linear acceleration components depends on an accurate estimation of the device orientation. Device orientation is estimated based on fusion of data from one or more motion sensors such as accelerometer, gyroscope, and magnetometer. The orientation estimation solution introduced is a low complexity linear Kalman filter (LKF) using differential state equation using the same principle of a complementary filter approach. The compensation of drift in the time update system instead of the measurement update system results in a substantial improvement in terms of the solution response and settling times. The solution removes any drift and has good tolerance to any noise introduced from the inputs or the system. The solution also inherits the fast response and small orientation detection capabilities of the gyroscope. Using this estimated orientation we can compute the linear acceleration and gravity observed on a device. A projection-based approach is introduced and it uses the device orientation to separate the linear acceleration and gravity components from the accelerometer data. This separation of components results in a better analysis and recognition of linear and rotational motion using mobile and wearable devices.

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