Virtual Aircraft Target Generator for ADS-B Testing

NASA's SDR uses Field-Programmable Gate Array (FPGA) technology to enable flexible performance on orbit. A first-generation FM-modulated transceiver is capable of operating at up to 1 Mbps downlink and 50 kbps uplink, full duplex. An FPGA performs Reed-Solomon (255,223) encoding, decoding, and bit synchronization, providing Consultative Committee for Space Data Systems (CCSDS) and Near Earth Network (NEN) telemetry protocol compatibility. The transceiver accepts data from the onboard flight computer via a source synchronous RS422 interface. NASA's second-generation full duplex SDR, known as PULSAR (programmable ultra-lightweight system-adaptable radio, Figures 1 and 2 below) incorporates command receiver and telemetry transmitters, as well as updated processing and power capabilities. An S-band command receiver offers a max uplink data rate of 300 Kbps and built-in QPSK demodulation. X- and S-Band telemetry transmitters offer a max downlink data rate of 150 Mbps and flexible forward-error correction (FEC) using Reed-Solomon encoding (LDPC rate 7/8 and 1/2 convolution in development), and it uses QPSK modulation. The use of FEC adds an order of magnitude increase in telemetry throughput due to an improved coding gain. An onboard FPGA uses high-speed logic for uplink/downlink and encoding/decoding processes. Balloon flight testing has been conducted and is ongoing for PULSAR.

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 system revolves around a Communications Remote Monitoring Panel (CRMP), which interfaces with multiple radios operating under different communication protocols. The CRMP intelligently identifies each radio’s communication specification, such as analog, digital, or proprietary protocols, and dynamically adjusts to ensure the correct protocol is used for proper communication. This capability allows for seamless interaction between otherwise incompatible systems. Once connected, the CRMP transmits queries to assess the status of each connected radio to retrieve metrics including operational status, voltage, power status, and internal temperature. The CRMP can also remotely switch radios on or off, or flag problems for maintenance crews. The system supports real-time data acquisition, cross-protocol translation, and scalable network management. Whether deployed at a single control center or across multiple geographically dispersed facilities, the system improves visibility into the health of communication networks while minimizing the need for on-site technicians. Data trends may also inform replacement equipment acquisitions. Its modular, scalable architecture enables simultaneous monitoring of multiple radios, making it ideal for expanding networks or integrating legacy and next-gen systems.

Increasing demand for smaller UAVs (e.g., sometimes with wingspans on the order of six inches and weighing less than one pound) generated a need for much smaller flight and sensing equipment. NASA Langley's new sensing and flight control system for small UAVs includes both an active flight control board and an avionics sensor board. Together, these compare the status of the UAVs position, heading, and orientation with the pre-programmed data to determine and apply the flight control inputs needed to maintain the desired course. To satisfy the small form-factor system requirements, micro-electro-mechanical systems (MEMS) are used to realize the various flight control sensing devices. MEMS-based devices are commercially available single-chip devices that lend themselves to easy integration onto a circuit board. The system uses less energy than current systems, allowing solar panels planted on the vehicle to generate the systems power. While the lightweight technology was designed for smaller UAVs, the sensors could be distributed throughout larger UAVs, depending on the application.

NASA's Extensible Traffic Management (xTM) system allows for distributed management of the airspace where disparate entities collaborate to maintain a safe and accessible environment. This digital ecosystem relies on a common data generation and transfer framework enabled by well-defined data collection requirements, algorithms, protocols, and Application Programming Interfaces (APIs). The key components in this new paradigm are: Data Standardization: Defines the list of data attributes/variables that are required to inform and safely perform the intended missions and operations. Automated Real Time And/or Post-Flight Data Verification Process: Verifies system criteria, specifications, and data quality requirements using predefined, rule-based, or human-in-the-loop verification. Autonomous Evolving Real Time And/or Post-Flight Data Validation Process: Validates data integrity, quantity, and quality for audit, oversight, and optimization. The verification and validation process determines whether an operation’s performance, conformance, and compliance are within known variation. The technology can verify thousands of flight operations in near-real time or post flight in the span of a few minutes, depending on networking and computing capacity. In contrast, manual processing would have required hours, if not days, for a team of 2-3 experts to review an individual flight.