  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.
