• feature estimation
• feature extraction
• inverse radon transform
• radar
• resident space object
• space debris
• space situational awareness
• time-frequency analysis

The amount of resident space objects (RSOs) orbiting around the Earth has seen a dramatic grow through the recent years. Its rising population increases the potential danger to space missions. At present time, it is urgent to gain as much information as possible in order to characterize this environment. The research presented in this work outlines a methodology for utilizing radar data in the presence of single and multiple looks. This method is based on the use of the inverse Radon transform computed on the time-frequency representation of the received signal. The image in the inverse Radon domain is used to jointly estimate the RSO’s rotation speed and its size with respect to the image plane. Then, this approach is extended for a bistatic case and the development of features fusion rules to integrate data from spatially distributed independent radar systems is studied and carried out. The algorithms are tested on a variety of simulated and real datasets. In particular, real data analysis exploiting a K-band FMCW radar and a turntable has been performed in a controlled environment. A period analysis of real light curve data is also presented inside this thesis. This analysis is done using two different methods to cross check results. Specific accomplishments include: the development of a method for geometrical and dynamical parameter extraction of rotating RSOs validated with measurements obtained in a controlled laboratory environment.