Inertial Sensor Arrays, Maximum Likelihood, and Cram\'er-Rao Bound

TL;DR

This paper introduces a maximum likelihood estimator for inertial sensor arrays, deriving the Cramér-Rao bound to evaluate performance, and demonstrates that the method outperforms existing techniques, working effectively for both 2D and 3D arrays.

Contribution

A novel maximum likelihood fusion method for inertial sensor arrays that achieves the Cramér-Rao bound and works for both 2D and 3D configurations.

Findings

The proposed method attains the Cramér-Rao bound in simulations.

It outperforms current state-of-the-art measurement fusion methods.

The method is validated with real-world experiments on a 192-element array.

Abstract

A maximum likelihood estimator for fusing the measurements in an inertial sensor array is presented. The maximum likelihood estimator is concentrated and an iterative solution method is presented for the resulting low-dimensional optimization problem. The Cram\'er-Rao bound for the corresponding measurement fusion problem is derived and used to assess the performance of the proposed method, as well as to analyze how the geometry of the array and sensor errors affect the accuracy of the measurement fusion. The angular velocity information gained from the accelerometers in the array is shown to be proportional to the square of the array dimension and to the square of the angular speed. In our simulations the proposed fusion method attains the Cram\'er-Rao bound and outperforms the current state-of-the-art method for measurement fusion in accelerometer arrays. Further, in contrast to the state-of-the-art method that requires a 3D array to work, the proposed method also works for 2D arrays. The theoretical findings are compared to results from real-world experiments with an in-house developed array that consists of 192 sensing elements.