Abstract:

The Allan variance method can be used to determine the characteristics of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of error terms in the inertial-sensor data by performing certain operations on the entire length of data. In this paper, the theoretical basis of the Allan variance for modeling the inertial sensors’ error terms and its implementation in iPhone 6 Plus were presented. The five-day static data from the iPhone 6 Plus were investigated. The results of the data analysis indicated that white noise was the dominant noise for the short cluster times. It also shows that in z-axis accelerometer, rate random walk was the dominant noise. The results from this paper clearly shows that the Allan variance is a powerful technique to investigate the sensor error behaviors on different timescales.