Abstract
The performance of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission deteriorates due to radio-frequency interference (RFI) sources such as unwanted or unauthorized emissions in the L-band and its ad-j acent bands. Accurate detection of these RFI sources is the foundation of the following RFI localization and mitigation, which are crucial to guarantee the quality of SMOS products. In this paper, we present an RFI detection approach based on reweighted l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm minimization (RL1). This approach exploits the sparsity of RFI sources in the spatial domain and recovers the RFI source signals within an RL1 framework. Experimental results indicate that the presented RL1- based approach performs better RFI detection performance compared with the conventional approaches based on discrete Fourier transformation (DFT) and spatial spectrum analysis.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
