Stochastic Error Modeling of MEMS Inertial Sensor with Implementation to GPS-Aided INU System for UAV Motion Sensing

Abstract

The resided stochastic error in Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (INU) had caused the instrument not being able to operate as a standalone device for navigation applications. The conventional Global Positioning System (GPS)-aided strapdown INU system is commonly adopted to tackle such issue. Note that the estimation accuracy of such system depends on how precise the modeling of the stochastic error. In this paper, a comprehensive stochastic error modeling through three distinct approaches, namely the Gauss-Markov (GM) modeling, the Allan Variance (AV) analysis, and the Autoregressive (AR) modeling, are presented. The analysis shows that AR model achieved better modeling accuracy than the other two approaches. Next, the modeled stochastic errors were implemented on a GPS-aided strapdown INU system for UAV airplane's motion sensing, and the results shown that AR model achieved lower RMSE than the GM model, indicating that AR model is more suitable than GM model in representing the stochastic error model of MEMS strapdown INU.