A Unified Filter for Fusion of Multiple Inertial Measurement Units

TL;DR

This paper introduces the unified extended Kalman filter (UEKF), a novel approach for fusing multiple inertial measurement units that improves bias estimation and navigation accuracy in autonomous platforms.

Contribution

The paper proposes the UEKF, a new filter structure with bias variance redistribution, enhancing multiple inertial sensor fusion accuracy and robustness over existing methods.

Findings

UEKF outperforms state-of-the-art filters in sea experiments.

Bias estimation for each sensor improves navigation accuracy.

The bias variance redistribution algorithm effectively manages multiple sensor biases.

Abstract

Navigation plays a vital role in the ability of autonomous surface and underwater platforms to complete their tasks. Most navigation systems apply a fusion between inertial sensors and other external sensors, such as global navigation satellite systems, when available, or a Doppler velocity log. In recent years, there has been increased interest in using multiple inertial measurement units to improve navigation accuracy and robustness. State of the art examples include the virtual inertial measurement unit (VIMU) and the federated extended Kalman filter (FEKF). However, each of those approaches has its drawbacks. The VIMU does not improve the sensor biases, which constitute a significant source of error, especially in low-cost inertial sensors. While the FEKF does improve accuracy, it models uncertainty propagation empirically. If not modeled correctly, this can cause the global solution to diverge. To cope with those shortcomings, we propose and derive a new filter structure for multiple inertial sensors data fusion: the unified extended Kalman filter (UEKF). In addition, to cope with the multiple equal bias variance estimation problem we offer the bias variance redistribution algorithm. Our filter design enables bias estimation for each of the inertial sensors in the system, improving its accuracy and allowing the use of a varying number of inertial sensors. We show that our UEKF performs better than other state of the art, multiple inertial sensor filters using real data recorded during sea experiments.