Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

Abstract

On-board state estimation is a persistent challenge to fielding unmanned aerial systems (UAS), particularly when global positioning system (GPS) measurements are not available. The dominant estimation tool remains the extended Kalman filter (EKF) applied to an inertial measurement unit (IMU). The growing availability of low-cost commercial-grade IMUs raises questions about how to best improve sensor readings into an estimate when measurements are available from multiple IMUs. This paper evaluates four different approaches to attitude estimation from multiple IMU measurements and their application in high dynamic motion. The four approaches are fusion of measurements (virtual IMU), fusion of state estimates (Federated KF), feedback fusion state estimate (Feedback Federated KF), and an EKF design incorporating the additional measurements (Augmented KF); these correspond to fusion before, within, or after state estimation. The performance of the approaches are quantified for varying IMU number theoretically and experimentally. The experiments use on-board autopilot hardware implementations of the estimators during maneuvers in a motion capture volume and flights on-board an Unmanned Aerial Systems (UAS) implementation, with the peak and root-mean-square (RMS) errors used to quantify accuracy. The RMS error results indicate that the feedback federated Kalman Filter using five IMUs returns 38% compared to general federated Kalman Filter using 37% accuracy improvement over a single IMU. This improvement compares to a 19% improvement for virtual IMU and 9% improvement for the Augmented KF respectively. These results indicate that both the Federated KF approach achieves the lowest RMS error relative to the virtual IMU and augmented KF approaches and inform the design of multi-IMU UAS pose estimators.

Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems

“…Algorithms using this hyper-complex number system tend to be more computationally efficient, making them particularly suitable for embedded firmware within CPSs. Patel et al [89], for example, use quaternions to deploy and evaluate IMU fusion algorithms for UAS navigation.…”

Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model

“…Xu Yuan et al, on the other hand, solved the problem of poor viewability of heading information in the traditional foot-based heading reference system by designing an indoor personal navigation system combining a foot-based heading reference system and a shoulder-based electronic compass [28,29]. Patel et al conducted experimental analysis on four different fusion methods of multi-IMU, proving that the navigation accuracy of using five IMU fusion filters has been improved [30]. Niu et al [31,32], Li et al [33], and Prateek et al [34] also studied pedestrian navigation algorithms based on dual foot-mounted IMU.…”