Obtaining Trajectories Using Strapdown INS/KF Framework: Methodological Proposal

Abstract

The state-of-the-art of positioning systems has proven that complex sensor networks and artificial vision are required to accurately locate moving objects in autonomous navigation applications. This document presents the methodology for tracking position of moving objects using Kalman Filter Inertial Navigation Systems (INS/KF), integrating the Zero Velocity Update and Zero Angle Rate Update algorithms. The main contribution of this document is the methodological proposal in the integration of the INSKF-ZUPT/ZARUT o IKZ to the INS Strapdown feedback, providing restrictive properties to the displacement errors, significantly improving the trajectory, with a greater definition to the movement that was exposed the object. The proposed IKZ was tested with raw data from an IMU MPU-9255 in order to analyze the different results between static tests and linear movements on the X, Y and Z axes.

Keywords

• Filtro de Kalman
• Navegación
• MEMS
• INS