Bounding integrity risk subject to structured time correlation modeling uncertainty

Sequential state estimation of linear dynamical systems with time correlation uncertainty in the measurement and process noise is considered. The presence of random noise introduces a state estimate error that is defined in terms of a probability distribution. For high integrity navigation applications, the probability of the estimate error vector residing outside a specified boundary must be explicitly quantified. This probability, or integrity risk, can only be computed accurately when the measurement and process noise distributions are precisely known. Unfortunately, precise knowledge of the input noise distributions is rarely available; the use of inexact models can lead to optimistic integrity risks and potentially life-threatening situations can ensue. This paper focuses on developing a methodology to compute upper bounds on integrity risk subject to a bounded uncertainty structure on the input noise autocorrelation functions.