Safe2Ditch Steer-To-Clear Development and Flight Testing

This paper describes a series of small unmanned aerial system (sUAS) flights performed at NASA Langley Research Center in April and May of 2019 to test a newly added Steer-to-Clear feature for the Safe2Ditch (S2D) prototype system. S2D is an autonomous crash management system for sUAS. Its function is to detect the onset of an emergency for an autonomous vehicle, and to enable that vehicle in distress to execute safe landings to avoid injuring people on the ground or damaging property. Flight tests were conducted at the City Environment Range for Testing Autonomous Integrated Navigation (CERTAIN) range at NASA Langley. Prior testing of S2D focused on rerouting to an alternate ditch site when an occupant was detected in the primary ditch site. For Steer-to-Clear testing, S2D was limited to a single ditch site option to force engagement of the Steer-to-Clear mode. The implementation of Steer-to-Clear for the flight prototype used a simple method to divide the target ditch site into four quadrants. An RC car was driven in circles in one quadrant to simulate an occupant in that ditch site. A simple implementation of Steer-to- Clear was programmed to land in the opposite quadrant to maximize distance to the occupants quadrant. A successful mission was tallied when this occurred. Out of nineteen flights, thirteen resulted in successful missions. Data logs from the flight vehicle and the RC car indicated that unsuccessful missions were due to geolocation error between the actual location of the RC car and the derived location of it by the Vision Assisted Landing component of S2D on the flight vehicle. Video data indicated that while the Vision Assisted Landing component reliably identified the location of the ditch site occupant in the image frame, the conversion of the occupants location to earth coordinates was sometimes adversely impacted by errors in sensor data needed to perform the transformation. Logged sensor data was analyzed to attempt to identify the primary error sources and their impact on the geolocation accuracy. Three trends were observed in the data evaluation phase. In one trend, errors in geolocation were relatively large at the flight vehicles cruise altitude, but reduced as the vehicle descended. This was the expected behavior and was attributed to sensor errors of the inertial measurement unit (IMU). The second trend showed distinct sinusoidal error for the entire descent that did not always reduce with altitude. The third trend showed high scatter in the data, which did not correlate well with altitude. Possible sources of observed error and compensation techniques are discussed

Geographic Centroid Routing for Vehicular Networks

A number of geolocation-based Delay Tolerant Networking (DTN) routing protocols have been shown to perform well in selected simulation and mobility scenarios. However, the suitability of these mechanisms for vehicular networks utilizing widely-available inexpensive Global Positioning System (GPS) hardware has not been evaluated. We propose a novel geolocation-based routing primitive (Centroid Routing) that is resilient to the measurement errors commonly present in low-cost GPS devices. Using this notion of Centroids, we construct two novel routing protocols and evaluate their performance with respect to positional errors as well as traditional DTN routing metrics. We show that they outperform existing approaches by a significant margin.Comment: 6 page

Performance Evaluation of Hyperbolic Position Location Technique in Cellular Wireless Networks

This study addresses the wireless geolocation problem that has been an attractive subject for the last few years after Federal Communications Commission (FCC) mandate for wireless service providers to locate emergency 911 users with a high degree of accuracy -within a radius of 125 meters, 67 percent of the time by October 2001. There are a number of different geolocation technologies that have been proposed. These include, Assisted GPS (A-GPS), network-based technologies such as Enhanced Observed Time Difference (E-OTD), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and Cell of Origin (COO). This research focuses on network based techniques, namely the more prominent TDOA which is also called hyperbolic position location technique. The main problem in time-based positioning systems is solving nonlinear hyperbolic equations derived from set of TDOA estimates. Two algorithms are implemented as a solution to this problem: A closed form solution and a Least Squares (LS) algorithm. Accuracy and computational efficiency performances are compared in a wireless system established using DGPS measurements in Dayton, OH area

Improving Image Classification with Location Context

With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them. In addition to research that tries to predict GPS coordinates from visual features, this also opens up the door to problems that are conditioned on the availability of GPS coordinates. In this work, we tackle the problem of performing image classification with location context, in which we are given the GPS coordinates for images in both the train and test phases. We explore different ways of encoding and extracting features from the GPS coordinates, and show how to naturally incorporate these features into a Convolutional Neural Network (CNN), the current state-of-the-art for most image classification and recognition problems. We also show how it is possible to simultaneously learn the optimal pooling radii for a subset of our features within the CNN framework. To evaluate our model and to help promote research in this area, we identify a set of location-sensitive concepts and annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has GPS coordinates with these concepts, which we make publicly available. By leveraging location context, we are able to achieve almost a 7% gain in mean average precision

Orbit determination and orbit control for the Earth Observing System (EOS) AM spacecraft

Future NASA Earth Observing System (EOS) Spacecraft will make measurements of the earth's clouds, oceans, atmosphere, land and radiation balance. These EOS Spacecraft will be part of the NASA Mission to Planet Earth. This paper specifically addresses the EOS AM Spacecraft, referred to as 'AM' because it has a sun-synchronous orbit with a 10:30 AM descending node. This paper describes the EOS AM Spacecraft mission orbit requirements, orbit determination, orbit control, and navigation system impact on earth based pointing. The EOS AM Spacecraft will be the first spacecraft to use the TDRSS Onboard Navigation System (TONS) as the primary means of navigation. TONS flight software will process one-way forward Doppler measurements taken during scheduled TDRSS contacts. An extended Kalman filter will estimate spacecraft position, velocity, drag coefficient correction, and ultrastable master oscillator frequency bias and drift. The TONS baseline algorithms, software, and hardware implementation are described in this paper. TONS integration into the EOS AM Spacecraft Guidance, Navigation, and Control (GN&C) System; TONS assisted onboard time maintenance; and the TONS Ground Support System (TGSS) are also addressed

Development and Demonstration of a TDOA-Based GNSS Interference Signal Localization System

Background theory, a reference design, and demonstration results are given for a Global Navigation Satellite System (GNSS) interference localization system comprising a distributed radio-frequency sensor network that simultaneously locates multiple interference sources by measuring their signals’ time difference of arrival (TDOA) between pairs of nodes in the network. The end-to-end solution offered here draws from previous work in single-emitter group delay estimation, very long baseline interferometry, subspace-based estimation, radar, and passive geolocation. Synchronization and automatic localization of sensor nodes is achieved through a tightly-coupled receiver architecture that enables phase-coherent and synchronous sampling of the interference signals and so-called reference signals which carry timing and positioning information. Signal and crosscorrelation models are developed and implemented in a simulator. Multiple-emitter subspace-based TDOA estimation techniques are developed as well as emitter identification and localization algorithms. Simulator performance is compared to the CramérRao lower bound for single-emitter TDOA precision. Results are given for a test exercise in which the system accurately locates emitters broadcasting in the amateur radio band in Austin, TX.Aerospace Engineering and Engineering Mechanic

Adaptive Geolocation of IoT devices for Active and Assisted Living

Recent developments in IoT devices and communication systems, have brought to light new solutions capable of offering advanced sensing of the surrounding environments. On the other hand, during the last decades, the average life expectancy has increased, which translates into a considerable rise in the number of elderly people. Consequently, in view of all these factors, there is currently a constant demand for solutions to support an Active and Assisted Living (AAL) of such people. The presented thesis intends to propose a solution to help to know the location of IoT devices that may be assisting people. The proposed solution should take into consideration the risk factors of the target group at each moment, as well as the technical constraints of the device, such as its available power energy and means of communications. Thus, ultimately, a profile-based decision should autonomously be made by the device or its integrated system, in order to ensure the usage of the best geolocation technology for each situation.Desenvolvimentos recentes em dispositivos IoT e em sistemas de comunicação, trouxeram consigo novas soluções capazes de oferecer uma deteção avançada dos ambientes circundantes. Por outro lado, no decorrer das últimas décadas, a esperança média de vida aumentou, o que se traduz também num considerável aumento do número de pessoas idosas. Por conseguinte, perante o conjunto destes factores, existe actualmente uma procura constante de soluções de suporte a uma Active and Assisted Living desse grupo de pessoas. A presente tese tenciona propor uma solução que ajude a conhecer a localização dos dispositivos IoT que possam estar a ajudar pessoas. A solução proposta deve ter em consideração os fatores de risco do grupo-alvo em cada momento e também as restrições técnicas do dispositivo, como a energia disponível e os meios de comunicação. Deste modo, em última instância, uma decisão baseada num perfil deve ser tomada autonomamente pelo dispositivo ou pelo seu sistema, para garantir a utilização da tecnologia de geolocalização mais adequada em cada situação