Integrity Determination for a Vision Based Precision Relative Navigation System

Sean M. Calhoun, John Raquet

Abstract: This paper describes the development of an approach for determining uncertainty and integrity for a vision based, precision relative navigation system. Integrity ultimately relies on the ability to determine rigorous knowledge of the probability density function (pdf) for the estimated relative state or state error; which is based on a known set of models or assumptions and conditioned upon a set of observed measurements. The research is based on the concept of using a vision system, such as a electro-optical (EO) or infrared imaging (IR) sensor, to compute run-time confidence intervals or protection levels for a high precision, safety-critical relative navigation system. Previous work demonstrated the ability to provide an integrity monitor using vision based techniques, this paper presents research taking those approaches further to provide dynamic runtime protection levels for the navigation solution. The research utilizes a generalized Bayesian inference approach, in which a full pdf determination of the state estimate is realized. The paper describes the development a flexible approach for vision based integrity, applicable to a variety of image feature approaches. Results demonstrate how utilization of additional pixel measurements have significant impact on estimation uncertainty.
Published in: Proceedings of IEEE/ION PLANS 2016
April 11 - 14, 2016
Hyatt Regency Hotel
Savannah, GA
Pages: 294 - 304
Cite this article: Calhoun, Sean M., Raquet, John, "Integrity Determination for a Vision Based Precision Relative Navigation System," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, April 2016, pp. 294-304.
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