An overview of a new sensor calibration platform

Abstract

Inertial sensors are increasingly being employed in different types of applications. The reduced cost and the extremely small size makes them the number-one-choice in miniature embedded devices like phones, watches, and small unmanned aerial vehicles. The more complex the application, the more it is necessary to understand the structure of the error signal coming from these sensors. Indeed, their error signals are composed of deterministic and stochastic parts. The deterministic errors or faults can be compensated by proper calibration while the stochastic signal is usually ignored since its modeling is relatively difficult due to computational or statistical reasons, especially due to its complex spectral structure. However, a recently proposed approach called the Generalized Method of Wavelet Moments overcomes these limitations and this paper presents the software platform that implements this method for the analysis of the stochastic errors. As an example throughout the paper we will consider an inertial measurement unit, but the platform can be used for the stochastic calibration of any kind of sensor. The software is developed in the widely used statistical tool R using C++ language. The tools enable the user to study with ease any signal by the means of a vast range of predefined models and tools.