Powerline Geolocation

To enable it to acquire GPS signals very quickly and also track weak signals, the radiation-hardened Navigator receiver utilizes a bank of hardware correlators, a ColdFire microprocessor, and a specialized fast acquisition module (see figure 1). The hardware is implemented in VHSIC Hardware Description Language (VHDL) to target radiation-hardened Field Programmable Gate Arrays (FPGA) rather than Application-Specific Integrated Circuits (ASIC), in order to maintain flexibility for growth and design modifications. The Navigator was designed to operate autonomously to enable the use of GPS for onboard navigation in high altitude space missions. With the exception of GPS signals, Navigator requires no external data (e.g., current time estimate, recent GPS almanac, or converged navigation filter estimate of the receiver dynamics). By double buffering data up front in 1ms blocks, data can be processed as it is acquired. A discrete Fourier transform (DFT) is used to calculate the 1ms correlations, significantly reducing computing time. Computational efficiency is optimized and tradeoffs among sampling rate, data format, and data-path bit rate are carefully weighed in order to increase performance of the algorithm. In addition, the Navigators hardware-independent receiver software includes both a hardware interface to perform low-level functions as well as basic navigation. Onboard orbit determination and accurate state estimation/propagation during periods with no GPS access are accomplished by integration with the GPS Enhanced Onboard Navigation System (GEONS). Exploiting the properties of Fourier transform in a massively parallel search for the GSP signal, the Navigator has been tested and proven capable of acquiring signals at 25dB-Hz and below.

This technology comprises a novel system of detecting, inspecting and analyzing a corona discharge using an ultra-violet camera. It is useful for a number of potential applications, most notably, power line fault detection. The most novel feature is that it uses UV instead of IR which has been problematical for corona discharge detection because there is too much interference from other sources. UV detection offers images that isolate the location of the corona discharge with far greater precision.

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.

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.

The system is designed to work with any internet enabled mobile device that has access to its GPS pseudorange, code phase and potentially carrier phase measurements. The system makes use of GPS measurements or corrections obtained from a stationary base station to refine its positioning estimate using established computational techniques such as those associated with differential GPS, Real-Time Kinematic (RTK) GPS and/or the Local Area Augmentation System (LAAS). The location information from mobile and fixed base signals can be integrated using established such as those used for differential GPS, Such methods remove errors common to the measurements of both devices (the mobile and base station) thereby increasing accuracy of the mobile device position estimates.