Collaborative Signal of Opportunity Doppler Navigation with Inverse Covariance Intersection

Christian D. Moomaw, Scott M. Martin

Abstract:	As the need for robust position, velocity, and timing (PVT) data grows across different industries, signal of opportunity (SOOP) navigation has arisen as a promising source of PVT measurements in addition to traditional GNSSs. The accuracy of PVT estimates delivered by many SOOP navigation schemes, however, is unacceptably poor. Collaborative navigation techniques can improve the performance of such systems by fusing information across multiple navigation receivers. In this work, the inverse covariance intersection (ICI) data fusion algorithm is applied to a group of collaborating unmanned aerial vehicles (UAVs). The UAVs are navigating using only Doppler measurements from SOOPs derived from low earth orbit (LEO) satellites, and are collaborating using peer-to-peer (P2P) ranges derived from two-way time of flight (TW-ToF) measurements. An analysis of the positioning accuracy of the navigators is conducted, which compares a control group of UAVs using no collaborative navigation techniques to a selection of well-known collaborative navigation schemes, as well as a proposed scheme utilizing ICI. This comparison relies on a measurement-level simulation framework, which generates inertial measurements, SOOP range rates, and P2P ranges which can be supplied to each different navigator configuration to create a Monte Carlo test suite. The results of these tests allow for an analysis of the positioning accuracy of each method, as well as the accuracy of each navigator’s own covariance estimate in each case.
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	2883 - 2894
Cite this article:	Moomaw, Christian D., Martin, Scott M., "Collaborative Signal of Opportunity Doppler Navigation with Inverse Covariance Intersection," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 2883-2894. https://doi.org/10.33012/2022.18556
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