Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects
Abstract
Since the first space mission in 1957 (Sputnik 1), artificial objects of different size appeared in orbits around the Earth. Nowadays, the number of such objects is estimated to be over 2 millions. More than 300000 of them have a size greater than 10 cm although only 5% of them are catalogued. The observation and tracking of Resident Space Objects (RSOs) has become a crucial task in launch planning of new satellites, collisions-avoidance operations and in general to ensure the safety of operational satellites. The number of space debris has been constantly increasing. Particularly, in 2007 and in 2009, two events (Fengyun antisatellite test and Iridium-Cosmos collision) dramatically increased the number of debris in Low Earth Orbit (LEO). One of the issues related to RSOs tracking and classification regards data and track association, which is the problem of identifying and associating data that is generated by the same object. A possible solution to this problem consists of an extension of the number of parameters (or features) that are used to characterize an object and use them to discriminate it among others. These features can be estimated using both Optical or Radar sensors, which typically perform differently depending on the operational conditions (illumination, distance, etc.). Optical sensors produce a more accurate estimation of the object angular position also with objects that are very distant from the sensor, but are affected by weather conditions (fog, clouds, rain, etc.). Radar sensors, instead, provide accurate range and range-rate measurements and are robust with respect to weather conditions, although they are limited in terms of object distance. The present technical report presents innovative algorithms that exploits coherent radar signals to accurately estimate RSO features to uniquely discriminate an object from others.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 22, 2017
- Accession Number
- AD1030291
Entities
People
- A. Lupidi
- L. Gentile
- Marco Martorella
- S. Ghio