GPS-Denied Navigation Using SAR Images and Neural Networks

White, Teresa; Wheeler, Jesse; Lindstrom, Colton; Christensen, Randall; Moon, Kevin R.

doi:10.48550/arxiv.2010.12108

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Georegistration matches landmarks or terrain features as scanned or imaged by vehicles to preloaded data [7], and can work based on Synthetic Aperture Radar (SAR) [18,19,20], Light Detection and Ranging (LIDAR) [21,22,23], or camera visual systems, in what is known as image registration [24,25,26,27]. While SAR suffers from being memory and computationally expensive and LIDAR is restricted for aviation purposes by its limited range, image registration is a potentially valid solution for completely removing the position drift in GNSS-Denied scenarios.…”

Section: Objectives and Noveltymentioning

confidence: 99%

Minimization of GNSS-Denied Inertial Navigation Errors for Fixed Wing Autonomous Unmanned Air Vehicles

Gallo,

Barrientos

2021

Preprint

View full text Add to dashboard Cite

This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to minimize the attitude error and reduce the position drift (both horizontal and vertical) with the dual objective of improving the aircraft GNSS-Denied inertial navigation capabilities as well as facilitating the fusion of the inertial filter with visual odometry algorithms. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results, compare the proposed filter with more traditional implementations, and analyze the sensitivity of the results to the quality of the onboard sensors. The author releases the C ++ implementation of both the navigation filter and the high fidelity simulation as open-source software [1].

show abstract