Stochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic Conditions

Abstract

This paper aims at investigating and analyzing the behavior of Micro-Electromechanical Systems (MEMS) inertial sensors stochastic errors in both static and varying dynamic conditions using two MEMSbased Inertial Measurement Units (IMUs) of two different smartphones. The corresponding stochastic error processes were estimated using two different methods, the Allan Variance (AV) and the Generalized Method of Wavelets Moments (GMWM). The developed model parameters related to laboratory dynamic environment are compared to those obtained under static conditions. A contamination test was applied to all data sets to distinguish between clean and corrupted ones using a Confidence Interval (CI) investigation approach. A detailed analysis is presented to define the link between the error model parameters and the augmented dynamics of the tested smartphone platform. The paper proposes a new dynamically dependent integrated navigation algorithm which is capable of switching between different stochastic error parameters values according to the platform dynamics to eliminate dynamics-dependent effects. Finally, the performance of different stochastic models based on AV and GMWM were analyzed using simulated Inertial Navigation System (INS)/Global Positioning System (GPS) data with induced GPS signal outages through the new proposed dynamically dependent algorithm. The results showed that the obtained position accuracy is improved when using dynamic-dependent stochastic error models, through the adaptive integrated algorithm, instead of the commonly used static one, through the non-adaptive integrated one. The results also show that the stochastic error models from GMWM-based model structure offer better performance than those estimated from the AV-based model.