Week of Events
Signal of Opportunity Navigation for Small Spacecraft in Deep Space
Signal of Opportunity Navigation for Small Spacecraft in Deep Space
Technical Webinar organized by AESSpacecraft navigation outside of geosynchronous orbit (GEO) presents an ongoing challenge. Current navigational techniques rely on Earth-based tracking, particularly through NASA's Deep Space Network (DSN). Navigation via the DSN is both fundamentally limited in terms of accuracy, as well as practically limited in terms of availability. Navigation via naturally occurring signals of opportunity, such as those produced by pulsars, quasars, and gamma-ray bursts, is proposed as an alternative navigation technique that could augment or eventually replace navigation via the DSN. This technique involves making range measurements based on the time-difference of arrival (TDOA) of a signal at the user and another location, usually either another cooperating user or a fixed reference point. Estimating the value of the TDOA is challenging, particularly because the signals in question are usually extremely weak. In this talk we describe algorithms for generating a 6 degree of freedom of freedom position, navigation and timing solution in deep space by measuring the time and angle of arrival of x-rays from pulsars. We also demonstrate their performance by post-processing data collected by x-ray detectors on the Suzaku and Chandra missions.Co-sponsored by: IEEE AESSSpeaker(s): DEMOZ, Houston, Texas, United States, Virtual: https://events.vtools.ieee.org/m/282348
Autonomous Driving in 2021 – Challenges, Progress, and Recent Advances
Autonomous Driving in 2021 – Challenges, Progress, and Recent Advances
Technical Webinar organized by AESAutonomous driving is seen as one of the pivotal technologies that considerably will shape our society and will influence future transportation modes and quality of life, altering the face of mobility as we experience it by today. Many benefits are expected ranging from reduced accidents, optimized traffic, improved comfort, social inclusion, lower emissions, and better road utilization due to efficient integration of private and public transport. Autonomous driving is a highly complex sensing and control problem. State-of-the-art vehicles include many different compositions of sensors including radar, cameras, and lidar. Each sensor provides specific information about the environment at varying levels and has an inherent uncertainty and accuracy measure. Sensors are the key to the perception of the outside world in an autonomous driving system and whose cooperation performance directly determines the safety of such vehicles. The ability of one isolated sensor to provide accurate reliable data of its environment is extremely limited as the environment is usually not very well defined. Beyond the sensors needed for perception, the control system needs some basic measure of its position in space and its surrounding reality. Real-time capable sensor processing techniques used to integrate this information have to manage the propagation of their inaccuracies, fuse information to reduce the uncertainties and, ultimately, offer levels of confidence in the produced representations that can be then used for safe navigation decisions and actions.Co-sponsored by: IEEE IMSSpeaker(s): Daniel, Houston, Texas, United States, Virtual: https://events.vtools.ieee.org/m/282350