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.

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.

Millimeter-wave radiometric imaging technology holds enormous potential for hidden-object detection in the application of security inspection. Existing hidden-object detection methods based on pixel or regional processing have limited detection performance on the low signal-to-clutter ratio or the targets with multiple sizes or irregular shapes. This paper proposes a new detection method combining the probabilistic fuzzy fusion and Fisher vectors processing for hidden object detection in multiple polarization passive millimeter-wave images. Experimental results employing the real data collected from Multiple Polarization Scanning Imaging Radiometer demonstrate that the proposed method improves the detection performance and the extraction accuracy of object contours compared with the state-of-the-art methods.

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 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.