A combined RFI localization algorithm of BT image and subspace decomposition for synthetic aperture interferometric radiometer

Radio frequency interference (RFI) in the L-band heavily contaminate remote sensing data and bring many challenges to the product quality of synthesis aperture interferometric radiometers. RFI detection and localization is a critical step in dealing with RFIs. The localization algorithm based on the brightness temperature (BT) image is limited by the resolution to separate closely spaced RFIs and achieve high localization accuracy in some cases. To achieve super-resolution, the Multiple Signal classification (MUSIC) method using subspace decomposition technique is suggested. Nevertheless, when the BT image SNR is low, the power of RFIs insufficient to overwhelm the background scene, and the performance of the MUSIC algorithm is unsatisfactory. In this paper, a further improvement is proposed by combining BT image and subspace decomposition. In the BT image domain, background scene cancellation and RFI target enhancement are carried out for enhancing the SNR. In the frequency domain, the MUSIC algorithm is applied utilizing subspace-decomposition to achieve super-resolution and high precision localization. Experiments and simulations based on SMOS data validate that the method presented in this paper performs better than both the classical MUSIC algorithm and BT-based localization algorithm in low SNR cases. Moreover, the localization accuracy of the method is also superior to the localization algorithm based on point source ripples in localizing multiple weak RFI sources.