Multi-IMU System for Robust Inertial Navigation: Kalman Filters and Differential Evolution-Based Fault Detection and Isolation

Abstract

The safety and reliability of various navigation applications are critically dependent on the integrity of sensor measurements. The inertial measurement unit (IMU) is a primary sensor in many navigation systems, yet is susceptible to diverse errors and faults, particularly with micro-electromechanical systems (MEMS). To address these challenges, we propose a Kalman filter (KF)-based framework incorporating multiple redundant IMUs offering robust fault detection and isolation (FDI) capabilities in the context of inertial navigation. The primary contributions of this work include effective multi-IMU calibration and integration, a comprehensive FDI enabled by a bank of auxiliary KFs, and an optimal dual-objective function combined with the differential evolution (DE) algorithm for fault detection parameters optimization. The effectiveness of the introduced method was validated using real-world data from homogeneous MEMS IMUs during urban road tests. Through data augmentation, fault simulation, and parameter optimization experiments, an exceptional fault detection performance was demonstrated, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}1$ </tex-math></inline-formula> score of 99.9%. Furthermore, our approach significantly enhanced inertial navigation accuracy, with position improvements of up to 78.4% in fault-free conditions compared to a single IMU and 64.5% in fault conditions compared to the standalone IMU fusion. These results confirm that the system can maintain an accurate and reliable navigation solution even in the presence of IMU sensor faults.