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Proceedings Paper

Geolocation system estimators: processes for their quality assurance and quality control
Author(s): John Dolloff; Jacqueline Carr
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Paper Abstract

This paper presents recommended principles and processes for the Quality Assurance (QA) and Quality Control (QC) of estimators and their outputs in Geolocation Systems. Relevant estimators include both batch estimators, such as Weighted Least Squares (WLS) estimators, and (near) real-time sequential estimators, such as Kalman filters. The estimators typically solve for (estimate) the value of a state vector X_true containing 3d geolocations and/or corrections to the sensor metadata corresponding to the measurements supplied to the estimator. Along with a best estimate X of X_true, the estimator outputs predicted accuracy, typically an error covariance matrix CovX corresponding to the error in the solution X. It is essential that the estimator output a reliable and near-optimal estimate X as well as a reliable error covariance matrix CovX. This paper presents various procedures, including detailed algorithms, to help ensure that this is the case, and if not, flag the problem along with supporting metrics. The majority of the QA/QC procedures involve data internal to the estimator, such as measurement residuals, and can be built-in to the estimator. Examples include measurement editing, solution convergence detection, and confidence interval tests based on the reference variance.

Paper Details

Date Published: 27 April 2018
PDF: 20 pages
Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450C (27 April 2018); doi: 10.1117/12.2302539
Show Author Affiliations
John Dolloff, National Geospatial-Intelligence Agency (United States)
Jacqueline Carr, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 10645:
Geospatial Informatics, Motion Imagery, and Network Analytics VIII
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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