  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.
