NOAA / NASA Drone. NASA Image
Blog Editor’s Note: We were interested to see this navigation sensor fusion effort sponsored by NOAA.
According to Jack Elston at Black Swift, the $400K contract will result in a prototype navigation receiver that will probably fly first on one of the company’s drones.
Integrating other sources with GPS as a way of increasing resilience will likely be a burgeoning field.
The US Department of Defense is pursuing the holy grail of this with it’s modular open systems approach (MOSA). The idea is to be able to integrate, prioritize, and use virtually all legacy and future P, N, and T sources with a simple “plug and play” box or algorithm.
Rather than re-describe Black Swift Technologies’ project, we have reproduced their press release below.
18 Jan NOAA Awards Black Swift Technologies Contract to Develop GPS-Denied Navigation
Enabling Beyond Visual Line of Sight (BVLOS) Operations for UAS in GNSS-Denied Environments
Boulder, CO – (January 18, 2021) – With nearly 95,000 miles of domestic coastline, NOAA’s National Geodetic Survey faces a daunting task. Under current FAA regulations, unmanned aircraft system (UAS) operators are required to keep their aircraft within visual line of sight, making coastline surveys time-consuming and arduous, having to tediously map 2-mile segments at a time, and then relocate to the next operating area. To address this challenge NOAA has selected Black Swift Technologies (BST) to develop commercially viable technology enabling GPS-denied navigation of UAS—technology critical in enabling long distance, beyond visual line of sight (BVLOS) flights.
“Enabling BVLOS operations in the National Airspace System is largely an issue of improved sensing,” states Jack Elston, Ph.D., CEO of Black Swift Technologies. “Operators, both government and commercial, are looking to perform missions over longer distances. These users represent a number of currently under-utilized applications for UAS due to capability and regulations—applications such as aerial imaging, environmental observation, precision agriculture, and infrastructure monitoring and inspection.”
Accurate aircraft position information is essential for safe UAS operations within the Unmanned Aircraft Systems Traffic Management (UTM) system. Unfortunately, the primary system that provides position information, GPS, is a single point of failure in the system, subject to jamming and spoofing, along with on-board failure or poor signal quality in locations such as urban canyons, at high latitudes or high altitudes.
BST aims to provide a robust, secondary navigation option through their diverse-source global positioning system (DS-GPS) that will provide accurate position updates to UAS in GPS denied areas through augmented sensor suites and advanced machine learning capabilities. This will be performed through the careful weighted fusion of advanced machine vision algorithms with the localization of the vehicle using triangulated signals of opportunity—essentially everything emitted within a set of frequencies from a non-moving source.
How it Works
DS-GPS utilizes a standard GPS receiver augmented through the use of additional sensing capabilities to estimate inertial velocity and absolute position. This information allows for the vehicle to determine when the GPS signal is degraded either through spoofing or environmental effects. Other sensors, including cameras and a software-defined-radio, can then be used to replace the GPS as the primary navigational sensor providing position and velocity estimates from diverse sources.
“Black Swift Technologies has worked with NOAA in particular in the past to supply systems that can be used for both coastline work and atmospheric science,” Elston emphasizes. “The work in this SBIR is a natural extension of the machine learning based techniques BST has been developing for unmanned aircraft. One of the key technologies of this work is to use machine vision (both optical flow and SLAM) to allow continued safe flight of UAS in the event of loss of GPS. Work associated with coastline inspection and mapping would greatly benefit from BVLOS operations since this would significantly reduce the amount of time an operator has to spend in the field.”