Search

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.

Stochastic error in the Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (SINU) is the primary issue causing sensors to be unable to operate as a standalone device. Conventional implementation of MEMS SINU fuses measurement with a global positioning system (GPS) through a Kalman filter in order to achieve long-term accuracy. Such integration is known as a GPS-aided SINU system, and its estimation accuracy relies on how precise the stochastic error prediction is in Kalman filtering operation. In this paper, a comprehensive study on stochastic error modeling and analysis through a Gauss-Markov (GM) model and autoregressive (AR) model are presented. A wavelet denoising technique is introduced prior to error modeling to remove the MEMS SINU's high frequency noise. Without a wavelet denoising technique, neither the GM model nor AR model can be utilized to represent the stochastic error of SINU. Next, details of the Kalman filter implementation to accommodate the AR model are presented. The modeling outcomes are implemented on an unmanned aerial vehicle (UAV) for on-board motion sensing. The experimental results show that AR model implementation, compared to a conventional GM model, significantly reduced the estimated errors while preserving the position, velocity and orientation measurements.

A Strapdown Inertial Navigation Unit (SINU) is a low cost motion measurement device commonly used for navigation solutions. Global Positioning System (GPS) is usually adopted as an external reference source to minimize the SINU's accumulation errors by applying a Kalman filter to obtain best estimations in positions, velocities and orientations. However, due to the low sampling rate of GPS, such a configuration does not provide intensive orientation estimation. In this paper, a new and efficient real-time GPS-aided SINU system with incorporated magnetometers is developed to enhance the orientation estimation. An intensive orientation estimation algorithm is developed by combining the extra sensory inputs from magnetometers with the inputs from accelerometers. The estimated orientation was applied in the Kalman filtering, replacing the GPS-aided orientation estimation. The offline implementation shows promising results in reducing both the accelerometers' and gyroscopes' errors. Finally, the design is successfully implemented in real-time.