Give Me More: Increasing Output for the Cyclone Global Navigation Satellite System (CYGNSS) Mission

2019
In December 2016, the National Aeronautics and Space Administration (NASA) launched a constellation of eight spacecraft for the Cyclone Global Navigation Satellite System(CYGNSS) mission in Low-Earth Orbit (LEO) at an inclination of 35 degrees. The mission's science goal is to understand the coupling between ocean surface properties, moist atmospheric thermodynamics, radiation, and convective dynamics in the inner coreof Tropical Cyclones(TCs). CYGNSS uses an innovative technique, Global Navigation Satellite System-Reflectometry (GNSS-R), to derive surface wind speed by measuring the strength of the specular reflectionof Global Positioning System ( GPS) signalsfrom the surface of the ocean. Despite limited onboardprocessing resources and relatively short ground station contacts, the CYGNSS data processing systemhas been effective - it has collected and successfully delivered to the science team hundreds of gigabytesof data in just eighteen months of operation. Since GNSS-R has proven useful, scientists are looking at other applications for observations over water and land. To make accurate measurements over land, it is of considerable interest to increase the data production rate for the Delay-Doppler Maps (DDMs), one of the chief onboarddata products. This increase would significantly reduce the smearing effect that results from integrating over a longer time interval, resulting in higher-resolution imagery. This paper reexamines the flight segment and ground segmentdata processing for CYGNSS. It considers some of the limiting constraints and explores some changes that have improved performance or may potentially improve it. For the flight segment, we evaluate enhancements such as alternative formatting and additional data compression. For the ground segment, we look at improved planning and increased ground contacts.
    • Correction
    • Source
    • Cite
    • Save
    2
    References
    1
    Citations
    NaN
    KQI
    []
    Baidu
    map