Improvement of the global positioning accuracy with miniaturized strap-down INS systems through wavelet filtering of data from MEMS inertial sensors

Abstract

In this chapter, the correlation between the issue of perturbed data acquired from miniaturized inertial sensors and the wavelet filtering technique is investigated. The market growth of micro-electro-mechanical systems (MEMS) has impacted various fields, and its potential application in strap-down inertial navigation systems (INS) could not be overlooked. Despite the apparent benefits of dimension and price reduction, the utilization of miniaturized inertial sensors for manufacturing the inertial measurement unit (IMU) entails certain drawbacks: the output signals are usually corrupted with different types of errors, which distort the real navigation information. The proposed case focuses on the suitability of wavelets for denoising the perturbed IMU signals before being erroneously processed by the navigation algorithm. The applicative part consisted in implementing sensor software models in Simulink and testing various wavelet filters. Furthermore, to fully assess the efficacy of the wavelet denoising technique, the model of a SDINS employing a MEMS-based IMU was established in Simulink. The evaluation involved comparing the attitude, position and speed components obtained before and after the denoising procedure with those of an ideal model linked to constant inputs. The results demonstrated the effectiveness of the proposed association in terms of positioning accuracy, signal characteristics improvement and computation complexity.