Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial

TL;DR

This tutorial analyzes the use of multiple stationary MEMS-IMUs to improve inertial navigation accuracy, highlighting error relationships, robustness, and the benefits of sensor redundancy through both analytical and experimental comparisons.

Contribution

It provides a new perspective on error analysis in multi-sensor inertial systems and demonstrates the robustness and improvements achievable with multiple stationary MEMS-IMUs.

Findings

Multiple sensors improve signal accuracy and noise rejection.

Analytical model aligns well with experimental results.

Sensor redundancy enhances navigation state estimation.

Abstract

Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, accuracy degrades over time. To mitigate the resulting drift in state estimates, different approaches of parametric and state estimation are proposed to compensate for undesirable errors, using frequency-domain filtering or external information fusion. Another approach uses multiple inertial sensors, a field with rapid growth potential and applications. The increased sampling of the observed phenomenon results in the improvement of several key factors such as signal accuracy, frequency resolution, noise rejection, and higher redundancy. This study offers an analysis tutorial of basic multiple inertial operation, with a new perspective on the error relationship to time, and number of sensors. To that end, a stationary and levelled sensors array is taken, and its robustness against the instrumental errors is analyzed. Subsequently, the hypothesized analytical model is compared with the experimental results, and the level of agreement between them is thoroughly discussed. Ultimately, our results showcase the vast potential of employing multiple sensors, as we observe improvements spanning from the signal level to the navigation states. This tutorial is suitable for both newcomers and people experienced with multiple inertial sensors.