Geolocation system estimators: processes for their quality assurance and quality control

John Dolloff; Jacqueline Carr

doi:10.1117/12.2302539

27 April 2018 Geolocation system estimators: processes for their quality assurance and quality control

John Dolloff, Jacqueline Carr

Proceedings Volume 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII; 106450C (2018) https://doi.org/10.1117/12.2302539
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Conference Presentation

Citation Download Citation

John Dolloff and Jacqueline Carr "Geolocation system estimators: processes for their quality assurance and quality control", Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450C (27 April 2018); https://doi.org/10.1117/12.2302539

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