Computation of scalar accuracy metrics LE, CE, and SE as both predictive and sample- based statistics John Dolloff [C] and Jacqueline Carr [C] National Geospatial-Intelligence Agency (NGA) 7500 GEOINT Drive, Springfield, VA Government POC: Christopher O’Neill (
[email protected]); PA case #: 16-277 ABSTRACT Scalar accuracy metrics often used to describe geospatial data quality are Linear Error (LE), Circular Error (CE), and Spherical Error (SE), which correspond to vertical, 2d horizontal, and 3d radial errors, respectively, in a local tangent plane coordinate system (e.g. East-North-Up). In addition, they also correspond to specifiable levels of probability; for example, CE_50 is Circular Error probable, or CE at the 50% probability level. Typical probability levels of interest are 50, 90, and 95%. Scalar accuracy metrics are very important for both near-real time accuracy predictions corresponding to derived location information, and for post-analysis verification and validation of system performance (location accuracy) requirements. The former normally correspond to predictive scalar accuracy metrics and the latter usually correspond to sample-based scalar accuracy metrics. Predictive metrics are generated from a priori error covariance matrices and mean-values, if non-zero, corresponding to the underlying (up to 3) error components. Sample-based metrics are generated from independent and identically distributed (i.i.d.) samples of error relative to “ground truth”. This paper details the proper but practical computation of both types of scalar accuracy metrics, including corresponding confidence intervals – techniques with non-trivial computational approximation errors are not included nor needed. Recommended sample-based metrics utilize order statistics which require no assumptions regarding the underlying probability distributions of errors.