Cramér-Rao analysis of orientation estimation: viewing geometry influences on the information conveyed by target features.

A methodology for analyzing an imaging sensor's ability to assess target properties is developed. By the application of a Cramér-Rao covariance analysis to a statistical model relating the sensor measurements to the target, a lower bound can be calculated on the accuracy with which any unbiased algorithm can form estimates of target properties. Such calculations are important in understanding how a sensor's design influences its performance for a given assessment task and in performing feasibility studies or system architecture design studies between sensor designs and sensing modalities. A novel numerical model relating a sensor's measurements to a target's three-dimensional geometry is developed in order to overcome difficulties in accurately performing the required numerical computations. The accuracy of the computations is verified against simple test cases that can be solved in closed form. Examples are presented in which the approach is used to investigate the influence of viewing perspective on orientation accuracy limits. These examples are also used to examine the potential accuracy improvement that could be gained by fusing multiperspective data.