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NASAs reverse-ephemeris lunar navigation system is a concept for determining position on the lunar surface based on known orbits of satellites. In conventional GPS navigation systems, the GPS satellite transmits ephemeris data to a receiver on earth for determining position at the receiver location. Whereas for the reverse-ephemeris approach the receiver becomes the transmitter, and the satellite instead serves more as a fixed reference position with a known ephemeris. This simplifies the satellite requirements and also mitigates potential navigational disruptions that can otherwise arise in navigation systems that utilize satellite-based communications, for example from interference, jamming, etc. The design consists of lunar surface S-Band (2,400 2,450 MHz) 10 W transceivers ranging with analog translating transponders on a three-satellite constellation in frozen elliptical orbits to provide continuous coverage with service to 300 simultaneous users over 1.8 MHz of bandwidth at the transponder. Digital bases systems are possible too. As compared to GPS-based navigation requiring four or more satellites costing 100s of millions of dollars, the new NASA concept is based on using only three smallsats.

The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.

The novel Vision-based Approach and Landing System (VALS) provides Advanced Air Mobility (AAM) aircraft with an Alternative Position, Navigation, and Timing (APNT) solution for approach and landing without relying on GPS. VALS operates on multiple images obtained by the aircraft’s video camera as the aircraft performs its descent. In this system, a feature detection technique such as Hough circles and Harris corner detection is used to detect which portions of the image may have landmark features. These image areas are compared with a stored list of known landmarks to determine which features correspond to the known landmarks. The world coordinates of the best matched image landmarks are inputted into a Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT) module to estimate the camera position relative to the landmark points, which yields an estimate of the position and orientation of the aircraft. The estimated aircraft position and orientation are fed into an extended Kalman filter to further refine the estimation of aircraft position, velocity, and orientation. Thus, the aircraft’s position, velocity, and orientation are determined without the use of GPS data or signals. Future work includes feeding the vision-based navigation data into the aircraft’s flight control system to facilitate aircraft landing.