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Actual air traffic data and weather information are utilized to evaluate an aircrafts flight-plan route and predict its trajectories for the climb, cruise, and descent phases. The dynamics for heading (the direction the aircraft nose is pointing) and airspeed are also modeled by the FACET software, while performance parameters, such as climb/descent rates and speeds and cruise speeds, can also be obtained from data tables. The resulting trajectories and traffic flow data are presented in a 3-D graphical user interface. The FACET software is modular and is written in the Java and C programming languages. Notable FACET applications include reroute conformance monitoring algorithms that have been implemented in one of the Federal Aviation Administrations nationally deployed, real-time operational systems.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.

As ever larger numbers of spacecraft seek to make use of Earth's limited orbital volume in increasingly dense orbital regimes, greater coordination becomes necessary to ensure these spacecraft are able to operate safely while avoiding physical collisions, radio-frequency interference, and other hazards. While efforts to date have focused on improving Space Situational Awareness (SSA) and enabling operator to operator coordination, there is growing recognition that a broader system for Space Traffic Management (STM) is necessary. The STM architecture forms the framework for an STM ecosystem, which enables the addition of third parties that can identify and fill niches by providing new, useful services. By making the STM functions available as services, the architecture reduces the amount of expertise that must be available internally within a particular organization, thereby reducing the barriers to operating in space and providing participants with the information necessary to behave responsibly. Operational support for collision avoidance, separation, etc., is managed through a decentralized architecture, rather than via a single centralized government-administered system. The STM system is based on the use of standardized Application Programming Interfaces (API) to allow easier interconnection and conceptual definition of roles to more easily allow suppliers with different capabilities to add value to the ecosystem. The architecture handles basic functions including registration, discovery, authentication of participants, and auditable tracking of data provenance and integrity. The technology is able to integrate data from multiple sources.

Heterogeneous Spacecraft Networks address an emerging need, namely, the ability of satellites and other space-based assets to freely communicate with each other. While it appears that there has been no significant effort to date to address the application, emergence of such a solution is inevitable, given the rapidly-growing deployments of small satellites. These assets need to be able to communicate with each other and with global participants. Extending established global wireless network platforms like Wi-Fi and ZigBee to space-based assets will allow different satellite clusters to assist each other. For example, one cluster could provide images of the earths surface when another cluster is with out visibility at the needed time and location. More importantly, use of such common platforms will enable collaboration among individuals, institutions, and countries, each with limited assets of its own. Thus, allowing the incorporation of space-based assets into commercial wireless networks, and extending commercial communications into low Earth orbit satellites, access to satellite data will become ubiquitous.Similarly, some global networks will also benefit from the ability of a variety of nodes of different types to communicate with each other. One instance is in the emerging Internet of Things (IoT), where an enormous number of smart objects work together to provide customized solutions.

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.

A time-varying electrical field E, having a root-mean-square intensity of 2rms, with a non-zero gradient in a direction transverse to the liquid or fluid flow direction, is produced by a nanostructure electrode array with a very high magnitude gradient near exposed electrode tips. A dielectrophoretic force causes the selected particles to accumulate near the electrode tips, if the medium and selected particles have substantially different dielectric constants. An insulating material surrounds most of the nanostructure electrodes, and a region of the insulating material surface is functionalized to promote attachment of the selected particle species to the surface. An electrical property value Z(meas) is measured at the functionalized surface, and is compared with a reference value Z(ref) to determine if the selected species particles are attached to the functionalized surface. An advantage of this innovation is that an array of nanostructure electrodes can provide an electric field intensity gradient that is one or more orders of magnitude greater than the corresponding gradient provided by a conventional microelectrode arrangement. As a result of the high magnitude field intensity gradients, a nanostructure concentrator can trap particles from high-speed microfluidic flows. This is critical for applications where the entire analysis must be performed in a few minutes.

Significant contributors to the cost of launching nano- and micro-satellites to orbit are the costs of software, and Guidance, Navigation and Control (GNC) avionics systems that steer, navigate and control the launch vehicles, sequence stage separation, deploy payloads, and pass data to Telemetry. The high costs of these GNC avionics systems are due in part to the current practice of developing unique, custom, single-use hardware and software for each launch, and requiring high-precision measurements of position and attitude states. NASA Ames Research Center has developed and tested a low-cost avionics system prototype called Affordable Vehicle Avionics (AVA). AVA integrates a low-cost moderately-precise sensor suite with an advanced error-correcting software package to provide GNC for space launch vehicles in a package smaller than a multilayer sandwich (100 mm x 120 mm x 69 mm; 4in x 4.7in x 2.7in), and with a mass of less than 0.84kg (2lbs). The invention provides a common suite of avionics components and demonstration software that deliver affordable, capable GNC with flexible I/O which is applicable to a variety of nano/micro-sat launch vehicles at less than 10 percent of the cost to procure current state-of-the-art GNC avionics. Affordable Vehicle Avionics' (AVA's) approach to drastically reduce costs includes: (1) use of low-cost "tactical-grade" Commercial-off-the-Shelf MicroElectroMechanical Systems (MEMS) inertial measurement unit, wherein adequate navigation precision is achieved by fusing outputs from a Global Positioning System receiver, inertial sensors and a magnetic field vector sensor in an extended Kalman filter formulation that corrects inertial sensor biases; (2) a streamlined "cookbook" approach to define an effective process for launch vehicle developers to design, simulate, verify and support assembly, integration and testing of their SLVs, driven by high-fidelity six degrees of freedom SLV simulations and real-time hardware-in-loop tests to validate guidance, navigation and control for early test flights. Development Status: As of spring 2020, AVA has flown twice in its current configuration on a suborbital platform. Its navigation and control functions were successfully demonstrated for roll-rate control within a tight deadband onboard the first flight test, and it successfully issued attitude pointing commands to a failed reaction control subsystem and it issues issued a rocket-motor ignition command on a second flight test. To date, failure of SLV components other than AVA (e.g., electrical power) has precluded demonstration of navigation and control of an orbital or sub-orbital launch system, which remains to be demonstrated. AVA development was accomplished using a single magnetometer-based magnetic field vector sensor to provide attitude observability during free-fall (inter-stage coast periods). Therefore, the current tested AVA configuration is susceptible to magnetic/electric fields produced by other components and payloads onboard the SLV, so care must be exercised to either mount AVA well away from sources of such fields and or to incorporate magnetic/electric field barriers on field emitters if separation from emitters is inadequate. Also, licensees may wish to provide new AVA inputs from a pair of external horizon sensors to provide more accurate attitude navigation during coast phases of the SLV mission.

This invention is an imaging method that requires very simple optics on an airborne vehicle, a camera with an appropriate lens, and an area on the ground that provides visual texture. The complexity with this method is in the image processing and not as much with the hardware or positioning, making Background Oriented Schlieren (BOS) an attractive candidate for obtaining high spatial resolution imaging of shock waves and vortices in flight. First, images are obtained of a visually textured background pattern from an appropriate altitude. Next, a series of images are collected of a vehicle in flight below the observer vehicle and over the same spot on the ground that serves as a background pattern. Shock waves are deduced from distortions of the background pattern resulting from the change in refractive index due to density gradients. The invention requires special software to create the schlieren images. The schlieren image is a contour plot of a two-dimensional data array of measured distortions, in pixel units. The results are used by researchers to help understand the flow phenomenon and compare to computational models. The BOS method also yields measured deflection distances, which can be used to determine the strength of a given density gradient. The system design and flight planning were based on the camera characteristics, airplane coordination, and airspace limitations.

Swarms of large numbers of cooperating satellites will introduce new space mission capabilities and complexities. From a mission operations perspective, swarms pose a planning challenge due to the limited scalability of ground operations. The approach of planning and commanding individual satellites simply does not scale for multi-sat swarms of tens or hundreds. If the current state-of-practice continues to be applied, operation of large swarms (e.g., 100 spacecraft or more) will become intractable and cost prohibitive. To avoid this operations bottleneck, a new approach is required: the swarm must operate as a unit, responding to high level commands and constraints. Swarm Orbital Dynamics Advisor (SODA) enables high level user inputs in a single planning cycle. From one high level command, SODA determines all of the required individual satellite maneuvers over time, relieving ground personnel of the tasks of designing and commanding the placement of the swarm members. SODA provides the orbital maneuvers required to achieve a desired type of relative swarm motion. The purpose of SODA is two-fold. First, it encompasses the algorithms and orbital dynamics model to enable the desired relative motion of the swarm satellites. Second, SODA is compatible with a variety of visualization tools. The purpose of SODAs visualization element is to illustrate this concept clearly with a variety of graphics and animations. After computing the optimal orbital maneuvers to modify the swarm, these results are simulated to demonstrate successful swarm control.