Multi-IMU System for Robust Inertial Navigation: Kalman Filters and Differential Evolution-Based Fault Detection and Isolation
Bibliographic record
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${F}1$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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".