LAUSR

Recently, radio frequency interference (RFI) poses a growing threat to microwave interference radiometer with aperture synthesis (MIRAS) led by the European Space Agency, whose scientific goal is to monitor the soil moisture and ocean salinity of the Earth. RFI localization is a critical step to mitigate the impact of RFI sources on the brightness temperature (BT) maps. In this article, we propose an effective and robust RFI source localization approach combined with the joint sparse recovery (JSR) theory using multisnapshot data. The JSR method uses the joint sparsity property of RFI sources in the spatial domain to improve the robustness and accuracy for RFI source localization problem. First, we propose a JSR model by exploiting the joint sparsity of multisnapshot visibility data with RFI contained from MIRAS after conducting the field of view (FOV) registration. Then, we present a greedy algorithm using the orthogonal matching pursuit on multisnapshot data (MSOMP) to localize RFI sources. Results on synthetic data and real satellite data show that compared with the previous existing approaches, the proposed method has a better performance on the mitigation of localization accuracy bias, especially for a low BT value range case and a mixed BT value range case with multiple RFI sources.