Radiometric sensitivity and angular resolution are two of the most important performances for microwave and millimeter-wave interferometric radiometers. These two performance metrics are mutually constrained. Generally, low-degradation arrays (LDAs) or low-redundancy arrays (LRAs) are employed to individually optimize radiometric sensitivity or angular resolution in interferometric array synthesis tasks. In this article, we propose a novel kind of array configuration, named block-wise multiple-fold redundancy arrays (BlockMFRAs), to achieve joint optimization of radiometric sensitivity and angular resolution for interferometric radiometers. The BlockMFRA with any number of elements can be efficiently constructed by exploiting combinatorial natures of three number sequences, that is, a difference basis (DB), a cyclic difference set (CDS), and a bunched pattern (BP). In more detail, we introduce a set of new analytical DBs, corresponding to multiple-fold redundancy arrays (MFRAs) with$\beta $-fold redundant baselines for$\beta \in \mathbb {N}^{+}$, to determine layout positions of normal blocks in the BlockMFRA. Each normal block shares an identical subarray configuration with elements located by a suitable CDS. Then, a specific BP, used as a supplementary block, is properly combined with the above normal blocks. The generated block-wise structure enables the BlockMFRA to possess a relatively uniform distribution of baseline redundancy for attaining satisfactory radiometric sensitivity. Meanwhile, for a given baseline redundancy’s fold$\beta $, the BlockMFRA can achieve a better angular resolution than arrays designed by traditional methods. Several important properties of BlockMFRAs are proved theoretically, and numerical analyses are conducted to demonstrate BlockMFRAs’ superior performances.

Source localization is a potential and useful application in Microwave Interferometric Radiometry (MIR), such as radio frequency interference (RFI) localization, ship target detection or tracking. In this paper, we propose a new source localization method based on generalized augmented covariance matrix reconstruction (GACMR) for MIR. First, we construct a generalized virtual array (GVA) with larger aperture size, by relaxing the constraint on baseline (or spatial-frequency) coverage of physical array. Next, a generalized augmented covariance matrix (GACM) is generated based on the GVA correspondingly. We exploit the low-rank property of the GACM in source localization application, and then present a new generalized reweighted nuclear norm minimization (GRNNM) algorithm to accomplish the GACM reconstruction. Finally, a spatial spectrum analysis-based algorithm (i.e., MUSIC) is adopted on the reconstructed GACM to locate target sources. Experimental results demonstrate that the proposed GACMR-based localization method performs improved localization accuracy and superior angular resolution, compared with traditional source localization methods in MIR.

The performance of the Soil Moisture and Ocean Salinity (SMOS) mission headed by the European Space Agency (ESA) deteriorated due to the influence of radio-frequency interferences (RFIs). Accurate location of RFI sources is essential for effectively improving the SMOS mission performance. This paper proposes a new RFI location method based on generalized augmented covariance matrix reconstruction (GACMR). This method relaxes the constraint of the sparse array expansion criterion to obtain the generalized augmented covariance matrix (GACM). After that, a generalized reweighted strategy is introduced to reconstruct the GACM. Experimental results demonstrate that compared with some existing RFI localization methods, this proposed method not only can effectively find weak sources and reduce artifacts but also has better localization accuracy.

Radio frequency interference (RFI) has become an increasing and challenging problem in microwave interferometric radiometry (MIR). Accurate localization of RFI sources is helpful to provide location information for switching off unauthorized transmitters causing RFI and mitigating the impact of these RFI sources. In this article, we propose a new RFI localization method based on reweighted nuclear norm minimization (RNNM). This method exploits the low-rank property of augmented covariance matrix (ACM) collecting visibility samples in MIR and introduces a singular value weighting strategy to consider different contributions of ACM components. First, ACM is constructed from the original covariance matrix of sparse array, which increases the degree of freedom (DOF) for array processing and hence improves the angular resolution performance. Second, we present a fixed point iteration (FPI)-based RNNM Algorithm, named FRA, to achieve low-rank approximation of ACM involving contribution degrees of ACM components. In this way, the ACM components corresponding to RFI signals are retained well and ones corresponding to background noises are suppressed. Third, we use a subspace-based direction-of-arrival (DOA) estimation approach, i.e., MUSIC algorithm, on the weighted completed ACM (WCACM) (obtained by FRA in the second stage) to locate the potential RFI sources. Retrieved results using synthetic data and real soil moisture and ocean salinity (SMOS) satellite data demonstrate that the proposed RNNM-based method not only has the superiority on improved detection performance, especially for identifying weak sources, but also shows better or competitive localization accuracy and angular resolution, compared with the existing commonly used RFI localization methods in MIR.