The Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency (ESA) is aimed to globally monitor the Earth surface moisture and ocean salinity. As the single payload of SMOS satellite, the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) operates in the protected L-band. Nonetheless, the artificial sources emitting close to or/and fully in this band are contaminating the collected remote sensing data and deteriorating the performance of the SMOS mission. Identifying and localizing such sources is crucial to improve the quality of SMOS scientific products. In this article, we propose a method based on matrix completion (MC) for localization of radio frequency interference (RFI) sources. This method mainly exploits the low-rank property of the augmented covariance matrix (ACM) of the sparse array and addresses the ACM incompleteness (i.e., sampling data loss) due to inherent array geometry (e.g., the SMOS Y-shaped array) or potential hardware malfunction (e.g., the correlator failure). Validation results show that, compared with existing RFI localization approaches such as the discrete Fourier transformation (DFT) inversion and covariance-based direction-of-arrival (DOA) estimation, the proposed MC method possesses competitive localization accuracy, superior spatial resolution, and reduced artifacts.

Double difference bases (DDBs) and construction of thinned linear arrays with twofold redundancy are discussed in this communication. We present an analytical method, based on the combinatorial tools, to generate twofold redundancy arrays by employing regular DDB patterns separately or combining them with cyclic difference sets. Besides, the bound for DDBs is derived that would be useful to research on thinned array geometries.

This communication addresses the thinned linear array design for the optimization of radiometric sensitivity and angular resolution, both of which are crucial characteristics of interferometric aperture synthesis radiometers (IASRs). First, we establish a multi-objective optimization model concerning their tradeoff. Then, three state-of-the-art search algorithms, namely, multi-objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA-II), and nondominated neighbor immune algorithm (NNIA), are implemented for the design of linear interferometric arrays. Further, a constraint principle of interferometric arrays (CPIA) is proposed to reveal the inherent relation among array parameters closely related to IASR performances, which would be useful for the multi-objective optimization of IASR arrays.

For aperture synthesis radiometers, sparse samplings on the $u$ -$v$ frequency plane cause undesirable sidelobes in the synthesized beam. Through these sidelobes, artificial sources emitting in the protected 1400-1427 MHz band contaminate the retrievals of the soil moisture and ocean salinity (SMOS) from MIRAS measurements. One effective way to correct the artificial interferences is to create a synthetic signal to compensate for the interference's impact. Based on the similar idea, in this letter, we describe an algorithm to compensate for the interference's impact by constructing an artificial signal as close as possible to the Gaussian beam. Numerical studies using synthetic and real SMOS data have been carried out to demonstrate that the proposed algorithm outperforms the classical CLEAN algorithm in correcting the impact of the extended radio frequency interference source.

Radiometric sensitivity is a crucially important performance metric of interferometric aperture synthesis radiometers (IASRs). When synthesizing array geometries for optimum sensitivity performance, the minimum-degradation or low-degradation arrays (MDAs or LDAs) are employed to fulfill requirements of baseline coverage and redundancy distribution. This paper presents a study on combinatorial design of low-degradation linear arrays (LDLAs), based on a concept of multiple difference basis (MDB). Such a deterministic method can attain a satisfactory radiometric sensitivity performance in short computation time and can be easily applied in large array synthesis, compared with stochastic design methods (e.g., simulated annealing). Furthermore, the resultant LDLAs also have advantages of angular resolution and system reliability to element wreckage. Representative experiments are carried out to demonstrate the significance of the proposed MDB-based technique for designing LDLAs.