Synthetic aperture interferometric radiometer (SAIR), as a passive and high-sensitivity receiver, often encounters the pollution issue of radio frequency interference (RFI) sources. An effective method is to get the geolocalization of RFI sources and disable them by the government or other civilizations. Therefore, RFI geolocalization is a crucial step for RFI mitigation. Previous works based on matrix completion (MC) show improved spatial resolution for RFI geolocalization. However, the singular value decomposition (SVD) of the MC is time-consuming. In this study, we propose a fast RFI localization method based on the reweighted matrix factorization (RMF) to improve computation efficiency. First, we establish a robust MC model by leveraging the low-rank property of the RFI-contained covariance matrix. Then, we reformulate the MC model as an RMF model by introducing matrix factorization. Third, the alternating direction method of multipliers (ADMMs) is used to solve the RMF model. Finally, the multiple signal classification (MUSIC) algorithm locates RFI sources. Results obtained using Soil Moisture and Ocean Salinity (SMOS) satellite data demonstrate the effectiveness of the proposed method in computation efficiency.

In synthetic aperture interferometric radiometer (SAIR), compressive sensing (CS)-based imaging methods significantly improve reconstruction quality while requiring substantially fewer measurement constraints, thereby reducing hardware complexity for target detection applications. Despite the CS promising capabilities, the imaging quality is still limited in scenes with fewer visibility functions. The main reason is the elevated side-lobe level of the array factor, which results from the limited number of u-v baselines. To address this challenge, we propose a target imaging method via joint sparse and low-rank (JSLR) priors. The proposed method combines the target imaging framework of compressed interferometric radiometer (CIR) with matrix completion (MC) to enhance imaging quality. In the spatial domain, the target demonstrates sparsity, while in the spatial-frequency domain, it exhibits low-rank properties. Then, the MC method is employed to recover unobserved visibility functions. By combining sparsity and low-rank priors, we formulate the JSLR model. Finally, we introduce a reconstruction algorithm, known as the alternating direction method of multipliers (ADMM), to effectively solve the JSLR model. Results from simulations and real aircraft experiments demonstrate the superiority of the proposed JSLR method in enhancing imaging quality and stability.

Synthetic aperture interferometric radiometers (SAIRs) have attracted increasing attention for target detection applications, owing to their high spatial resolution and passive sensing capability. In such applications, fast and wide-area imaging is essential for efficient target localization, with a wide instantaneous field of view (FOV) being critical for capturing more information per snapshot and reducing data acquisition time. However, the effective FOV and alias-free FOV (AF-FOV) of uniformly sampled arrays are limited by the minimum element spacing in interferometric arrays. While non-uniform sampling offers a potential solution to alleviate these limitations, a comprehensive framework for optimizing non-uniform arrays in SAIRs remains lacking. To address this gap, we propose a sidelobe suppression-assisted array design method to enlarge the effective FOV of SAIRs. We begin by defining a novel optimization objective named modified array factor (MAF), with consideration of the spatial-variant array factor (AF) for non-uniform sampling SAIR systems. Subsequently, a constrained multi-objective optimization model is formulated to improve the effective FOV while maintaining angular resolution and sensitivity, subject to constraints on array aperture size and minimum element spacing. Finally, a global optimization algorithm is employed to solve this model. Simulation results demonstrate that the optimized non-uniform arrays (ONAs) significantly extend the effective FOV while maintaining competitive angular resolution and sensitivity compared to conventional arrays, validating the effectiveness of the proposed approach.

In the field of Earth remote sensing, synthetic aperture interferometric radiometers (SAIR) are employed to retrieve geophysical parameters, such as soil moisture and ocean salinity, through inverse brightness temperature (BT) images. However, the high sensitivity of SAIR makes it susceptible to interference from radio frequency interference (RFI) sources, which significantly degrade the quality of BT images by contaminating the visibility function. To address this issue, this article proposes a robust imaging framework based on principal component analysis to mitigate RFI and recover scene BT. First, using a covariance matrix model, we analyze the low-rank property of RFI sources and the sparsity of natural scenes through theoretical analysis and experiments. Subsequently, leveraging these low-rank and sparse properties, we propose a reweighted nuclear norm (RNN) model and a reweighted matrix factorization (RMF) model to suppress RFI sources while recovering the visibility functions of natural scenes. These two RFI mitigation models are then solved using the alternating direction method of multipliers (ADMM). Finally, the discrete Fourier transform (DFT) method is applied to obtain BT images from the recovered visibility functions. Results from both simulated and measured data confirm the efficacy of the proposed method.

This paper discusses the near-field imaging issue of synthetic aperture interferometric radiometers (SAIR). It is challenging for near-field SAIR to realize accurate and fast imaging. To handle this issue, we use convolution to describe the relationship between the near-field visibility function and the brightness temperature and demonstrate that near-field imaging is a deconvolution operation. Then, a band-limited filtering function is constructed to compensate for the synthetic phase factor introduced by the angular spectrum propagation. Finally, the band-limited filtering near-field imaging (BFNI) algorithm is proposed to generate the spectrum of the near-field brightness temperature and realize accurate near-field imaging. Moreover, we present the fast BFNI (FBFNI) algorithm to achieve fast and precise imaging based on the sub-arrays. Unlike the time-domain-based (TDB) imaging algorithm, the proposed BFNI and FBFNI algorithms adopt the frequency-domain-based methods to recover the 2-D brightness temperature image from the 4-D visibility function. Simulation and experiment results show that the proposed frequency-domain algorithms can achieve the same imaging quality as the time-domain algorithm.

The scientific goal of the Soil Moisture and Ocean Salinity (SMOS) mission is to retrieve the geophysical parameter from brightness temperature (TB) maps. However, radio frequency interference (RFI) significantly influences the interpretation of TB maps, leading to a deteriorated retrieval performance. RFI localization is essential for switching off these illegal emitters and mitigating their impacts on TB maps. This article proposes a novel high-resolution RFI localization method via jointly non-convex low-rank approximation and expanded virtual array (EVA). Concretely, the RFI localization problem is first formulated from the perspective of non-convex low-rank recovery, which better approximates the rank of the covariance matrix collecting visibility samples. Then, we propose the EVA concept by relaxing the size constraint on the physical antenna array. Moreover, we use a new algorithm based on the joint Schatten-$p$and$Lp$(JSL) norms to solve the above non-convex low-rank recovery problem. This JSL algorithm can improve the spatial resolution for RFI localization. Combining the JSL algorithm and the EVA can further improve the detection performance and enhance the spatial resolution for RFI localization. The experimental results using synthetic data and real SMOS data prove that the proposed method shows enhanced spatial resolution, better detection performance, and competitive or better localization accuracy compared with the currently existing methods.

The target imaging application is significant to various sensing systems, such as radiometers, radars, and infrared. However, high system complexity impedes the application of interferometric radiometers to target imaging tasks to some extent. Specifically for anN-element interferometric radiometer with aperture synthesis technique, complex correlators are of the order of O(N2), giving rise to the great difficulty of system hardware implementation. In this paper, we propose a new compressed interferometric radiometer (CIR) concept for target imaging applications, which exploits the sparsity property of targets in the spatial domain. The CIR target imaging framework mainly adopts the compressive measurement method to acquire partial visibility function samples in the spatial-frequency domain via a proper sparse sampling pattern. Then, these partially observed visibility samples are inverted to image the target contrast information by sparse recovery methods. For the above image recovery process, we propose two novel algorithms named local regional information-based reweightedl1-norm minimization (LRRL1) and local regional convolution-based reweightedl1-norm minimization (LRCRL1). The experiments using simulated and real data demonstrate the validity and effectiveness of the proposed CIR target imaging framework, showing superiority in both imaging performance and system complexity compared with conventional algorithms used in interferometric radiometers.

For near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visi-bility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very-close range accurate imaging with large field of view (FOV). In this paper, we present a new SAIR near-field imaging algorithm based on angular spec-trum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data sam-ples of a 4-D visibility function acquired from planar arrays. First, we invert the 4-D visibility samples into the angular spectrum domain via the Fourier transformation. Second, a dedicated phase factor compensation is adopted and the dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distri-bution. Finally, we reconstruct the BT image by making use of the generated SAS data. Experiment results show that the presented SASI algorithm can reconstruct the BT image.

Recently, the radio frequency interference (RFI) poses a growing threat to the 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 paper, we propose an effective and robust RFI source localization approach combined with the joint sparse recovery (JSR) theory by using multi-snapshot data. The JSR method utilizes 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 multi-snapshot 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 multi-snapshot data (MSOMP) to localize RFI sources. Results on synthetic data and real satellite data both show that, compared with the previous existing approaches, the proposed method has a better performance on the mitigation of the localization accuracy bias, especially for a low BT value range case and a mixed BT value range case with multiple RFI sources.

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.