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The NDL uses homodyne detection to obtain changes in signal frequency caused by a target of interest. Frequency associated with each segment of the modulated waveform collected by the instrument is positive or negative, depending on the relative range and direction of motion between the NDL and the target. Homodyne detection offers a direct measurement of signal frequency changes however only the absolute values of the frequencies are measured, therefore additional information is necessary to determine positive or negative sign of the detected frequencies. The three segmented waveform, as opposed to conventional two-segmented ones, allows for resolving the frequency sign ambiguity. In a practical system, there are times when one or more of the three frequencies are not available during a measurement. For these cases, knowledge of the relative positions of the frequency sideband components is used to predict direction of the Doppler shift and sign, and thus make correct range and velocity measurements. This algorithm provides estimates to the sign of the intermediate frequencies. The instrument operates continuously in real time, producing independent range and velocity measurements by each line of sight used to take the measurement. In case of loss of one of the three frequencies, past measurements of range and velocity are used by the algorithm to provide estimates of the expected new range and velocity measurement. These estimates are obtained by applying an estimation filter to past measurements. These estimates are used during signal loss to reduce uncertainty in the sign of the frequencies measured once signals are re-established, and never to replace value of a measurement.

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 technology presents an on-board estimation, navigation and control architecture for multi-rotor drones flying in an urban environment. It consists of adaptive algorithms to estimate the vehicle's aerodynamic drag coefficients with respect to still air and urban wind components along the flight trajectory, with guaranteed fast and reliable convergence to the true values. Navigation algorithms generate feasible trajectories between given way-points that take into account the estimated wind. Control algorithms track the generated trajectories as long as the vehicle retains a sufficient number of functioning rotors that are capable of compensating for the estimated wind. The technology provides a method of measuring wind profiles on a drone using existing motion sensors, like the inertial measurement unit (IMU), rate gyroscope, etc., that are observably necessary for any drone to operate. The algorithms are used to estimate wind around the drone. They can be used for stability or trajectory calculations, and are adaptable for use with any UAV regardless of the knowledge of weight and inertia. They further provide real-time calculations without additional sensors. The estimation method is implemented using onboard computing power. It rapidly converges to true values, is computationally inexpensive, and does not require any specific hardware or specific vehicle maneuvers for the convergence. All components of this on-board system are computationally effective and are intended for a real time implementation. The method's software is developed in a Matlab/Simulink environment, and has executable versions, which are suitable for majority of existing onboard controllers. The algorithms were tested in 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.

The SQRLi system is made up of three major components including the laser assembly, the mirror assembly, and the electronics and data processing equipment (electronics assembly) as shown in the figure below. The three main systems work together to send and receive the lidar signal then translate it into a 3D image for navigation and imaging purposes. The rover sensing instrument makes use of a unique fiber optic laser assembly with high, adjustable output that increases the dynamic range (i.e., contrast) of the lidar system. The commercially available mirror setup used in the SQRLi is small, reliable, and has a wide aperture that improves the field-of-view of the lidar while maintaining a small instrument footprint. Lastly, the data processing is done by an in-house designed processor capable of translating the light signal into a high-resolution (sub-millimeter) 3D map. These components of the SQRLi enable successful hazard detection and navigation in visibility-impaired environments. The SQRLi is applicable to planetary and lunar exploration by unmanned or crewed vehicles and may be adapted for in-space servicing, assembly, and manufacturing purposes. Beyond NASA missions, the new 3D lidar may be used for vehicular navigation in the automotive, defense, or commercial space sectors. The SQRLi is available for patent licensing.

The DLC platform is composed of three key components: a NASA-designed field programmable gate array (FPGA) board, a NASA-designed multiprocessor on-a-chip (MPSoC) board, and a proprietary datapath that links the boards to available inputs and outputs to enable high-bandwidth data collection and processing. The inertial measurement unit (IMU), camera, Navigation Doppler Lidar (NDL), and Hazard Detection Lidar (HDL) navigation sensors (depicted in the diagram below) are connected to the DLC’s FPGA board. The datapath on this board consists of high-speed serial interfaces for each sensor, which accept the sensor data as input and converts the output to an AXI stream format. The sensor streams are multiplexed into an AXI stream which is then formatted for input to a XAUI high speed serial interface. This interface sends the data to the MPSoC Board, where it is converted back from the XAUI format to a combined AXI stream, and demultiplexed back into individual sensor AXI streams. These AXI streams are then inputted into respective DMA interfaces that provide an interface to the DDRAM on the MPSoC board. This architecture enables real-time high-bandwidth data collection and processing by preserving the MPSoC’s full ability. This sensor datapath architecture may have other potential applications in aerospace and defense, transportation (e.g., autonomous driving), medical, research, and automation/control markets where it could serve as a key component in a high-performance computing platform and/or critical embedded system for integrating, processing, and analyzing large volumes of data in real-time.