MAXIMUM LIKELIHOOD APPROACH FOR IDENTIFYING HUMAN OPERATOR REMNANT IN A TRACKING TASK.

Abstract

By applying a maximum likelihood approach to identification with empirical data from a tracking task, the output signal uncorrelated with the input signal (a definition of human operator remnant) can be determined. To obtain this remnant signal, a linear, stationary model describing the human is utilized. The innovations signal is computed from the difference in the empirical data and the model's output. The remnant can then be identified using the innovations sequence by computing the component of the output signal which is uncorrelated (or orthogonal) to the input signal. Data from a roll axis tracking simulator is analyzed and remnant is identified to two phases of tracking (with and without motion information).