Inertial Navigation Using an Inertial Sensor Array

TL;DR

This paper introduces a new inertial navigation framework that fuses data from multiple accelerometers and gyroscopes, improving orientation accuracy and navigation performance through advanced modeling and filtering techniques.

Contribution

It presents a comprehensive fusion framework using a Lie Group EKF, with novel orientation modeling and bias handling methods for inertial sensor arrays.

Findings

Second-order accuracy in orientation integration improves navigation precision.

Multiple sensor array configurations are evaluated with real-world experiments.

Biases in accelerometers are effectively modeled as a fixed-dimensional term.

Abstract

We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and real-world experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers.