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.

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.

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.

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.

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.

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.

In the domain of synthetic aperture radiometry, a Fourier transform (FT) relationship can be established between the brightness temperature of the target in the far-field region and the visibility function output by the system. However, when the target is in the near-field range of the imaging system, the existing far-field imaging methods cannot be used directly for the inversion of the near-field visibility function, because the target radiation signal cannot simply be regarded as a plane wave signal. In this letter, we propose a novel imaging method for synthetic aperture radiometer systems on the basis of the characteristics of the near-field target radiation signal. We introduce the near-field error term to reformulate the relationship between visibility function and brightness temperature in near field. Based on the reestablished signal model, we present an image reconstruction algorithm via fractional Fourier transformation, named near-field fractional FT (NF-FRFT), to estimate the near-field brightness temperature. Compared with the conventional near-field imaging method, the proposed NF-FRFT method can achieve better image reconstruction quality without extra hardware consumption. Furthermore, another advantage of this approach is that no additional array layout design is required. The validity of this method is demonstrated by a series of simulations and experiments.

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.

Millimeter-wave (MMW) radiation characteristic has been used in object classification and recognition. Complex permittivity is a key factor affecting the radiation characteristic. Previous study has shown that estimation values of complex permittivity based on multipolarization measurements spread around a special curve (called “C-curve”). In this letter, we analyzed the influence of complex permittivity on MMW radiation. We find that “C-curve” is the manifestation of the solution instability and can be avoided by our new estimation method based on the measurements of multiple incident angles. The simulation and experiment have proved the validity of our method. During research, we also find that objects whose complex permittivities are near the “C-curve” are hard to distinguish by MMW radiometry, unless we measure their radiation of vertical polarization near the Brewster angle. This finding has the guiding significance for object classification, recognition, and information acquisition.

This paper presents a passive-millimeter imaging algorithnm for synthetic aperture interferometric radiometry (SAIR) in near-field. The SAIR measures the visibility function of the scene by antenna arrays. In the far field, according to the Van Cittert-Zernike theorem, there is a Fourier transform between visibility function and scene brightness temperature. However, in the near field, because of the cross-coupling of azimuth direction and distance direction, Van Cittert-Zernike theorem is not valid. In this paper, the visibility function is transformed into wave number domain using spherical wave decomposition. After compensating the phase term related to the distance, the SAIR near-field imaging is realized.

Radio-frequency interference (RFI) contaminations hamper the retrieval of geophysical parameters from brightness temperature maps in Synthetic Aperture Imaging Radiometers (SAIRs). This paper is concerned with RFI localization by one snapshot, and an artifact-free detection algorithm based on virtual array and spatial smoothing MUSIC has been proposed. The virtual array is composed of the baseline coverage of the SAIRs. The spatial smoothing method is applied to enhance the rank of the covariance matrix. And the classical MUSIC algorithm is used for direction finding. Experiments on SMOS data are carried out, and the results show better performance than the existing RFI localization algorithms in terms of artifact reduction, which is helpful to make sure whether there are relative weaker RFI sources around a strong RFI source.

Aperture synthesis (AS) passive imaging technique has been proven effective in remote sensing for high resolution. Generally, a synthetic aperture radiometer needs the same number of channels as the antennas. As a consequence, the system complexity, volume, and cost increase rapidly as the size of the array expands. In this letter, the channel compressive AS (CCAS) method is proposed to reduce the receiver channels and correlators. Every channel connects to several different antennas by a selected connection network, and the visibilities are rebuilt from the cross correlation between output signals of the channels. Also, the principles of choosing connection network are discussed to guarantee the performance of the reconstructed brightness temperature (BT) images. Simulation results have shown the validation of the proposed method. It is of great application potential for very large-scale array in the future.

Radio frequency interference (RFI) is a significant limiting factor in the retrieval of geophysical parameters measured by microwave radiometers. RFI localization is crucial to mitigate or remove the RFI impacts. In this paper, a novel RFI localization approach using covariance matrix augmentation in synthetic aperture interferometric radiometry (SAIR) is proposed. It utilizes the property of the sparse array configuration, which is commonly used in SAIR, where the sparse array can be viewed as a virtual filled array with much larger number of antenna elements. The approach can be applied in SAIR with a sparse array configuration, such as the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. Results on real SMOS data show that, compared with the previous approach, the presented approach has an improved performance of RFI localization with comparable accuracy of localization, such as improved spatial resolution, lower sidelobes, and larger identifiable number of RFIs.

Radio frequency interference (RFI) has becoming a seriously limitation in the retrieval of geophysical parameters from the measurements of microwave radiometers. In this work, a novel RFI localization approach is presented here to improve the computationally complexity by using virtual arrays and direction of arrival (DOA) estimations techniques. The proposed RFI localization method utilizes the underlying rotational invariance among signal subspaces induced by the virtual array, which is expanded by the sparse array in synthetic aperture interferometric radiometer (SAIR). Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) manifests remarkable performance and computational advantages in the test result over the proceeding approach like MUSIC without searching over parameter space.

The SMOS mission is developed for monitoring the surface soil moisture and ocean salinity by a two dimensional L-band synthetic aperture interferometric radiometer. However, artificial sources emitting in the protected L-band are contaminating the retrievals of the soil moisture and ocean salinity from the measurements. To mitigate the RFIs' impacts, the classical CLEAN algorithm was introduced and works well for most isolated point RFI sources. However, in presence of interactions between different RFI sources, the performance of the classical CLEAN algorithm will deteriorate. Thus, in this work, we present an improved CLEAN algorithm to compensate for the RFIs' impacts more accurately in presence of interactions from adjacent RFI sources. It adds back one previously detected RFI to the cleaned map and then reestimates the parameter of this RFI source. Numerical studies using synthetic SMOS data have been carried out to demonstrate that the proposed algorithm outperforms the classical CLEAN algorithm in presence of interactions between RFI sources.

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.

The output signal of millimeter-wave (MMW) radiometer is usually affected by 1/f-noise that is characterized by self-similarity and non-stationary. Wavelet thresholding techniques are widely used to de-noise. However, soft-thresholding has good de-noising effect but is poor in details preservation, and hard-thresholding is good in details preservation but has poor de-noising effect. To eliminate 1/f-noise from white Gauss noise (WGN), an improved thresholding function is introduced in this paper. This method overcomes the permanent bias in soft-thresholding and the discontinuous point in hard-thresholding. Additionally, a least square estimation (LSE) is introduced to estimate 1/f-type parameters. Simulations and experiments have been carried out to validate this method.

Radio frequency interference (RFI) is one of the most significant limiting factors in the retrieval of geophysical parameters measured by microwave radiometers. In this work, based on the measured visibilities of European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission, RFI mitigation results are presented using two approaches of array factor synthesis of synthetic aperture interferometric radiometry, i.e., RFI mitigation based on null control and low sidelobe synthesis. Results show that, for the approach of array factor synthesis with null control, the Gibbs-like contamination of weak and moderate RFIs can be mitigated very well, but it is not enough effective for strong RFIs because of instrument errors. On the other hand, for strong RFIs, at the cost of spatial resolution degradation which may be not critical for ocean salinity retrieval, result shows the approach of low sidelobe synthesis can be effective to mitigate the Gibbs-like contamination of strong RFIs.

Aperture synthesis measurements, also termed complex visibilities, are the sparse samplings in the (u,v) frequency plane, which causes undesirable side lobes in the synthesized beam. Through the side lobes, artificial sources emitting in the protected 1400-1427MHz band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. An effective way to correct the artificial interferences is to create a synthetic signal as close as possible to the interference and subtract it from the measured data. Based on the same idea, in this paper, we describe an approach to compensate for the effect of interference iteratively, which uses the CLEAN algorithm that was first developed to deconvolve a map made up of some point sources in radio astronomy. It works by finding the brightest point and then removing its contribution iteratively. Experiments based on real SMOS data have been carried out to demonstrate that the proposed algorithm is effective in correcting the influence of RFIs.

Scene imaging simulation is an indispensable work in passive millimeter-wave object detection and remote sensing. In this paper, a 3-D scene simulation model of passive millimeter-wave imaging based on ray-tracing is presented, with consideration of the essential affecting factors such as multiple reflections, sky radiation, polarization rotation and antenna pattern smoothing. In order to accelerate the ray-tracing process in simulation model, a fast ray-triangle intersection algorithm is adopted. The outdoor imaging experiment and scene imaging simulation are carried out to validate the presented method at 94GHz. From the results, it is shown that the speed of scene imaging simulation process is significantly improved and the simulated image is in good agreement with the measured brightness temperature image.

In this letter, an imaging method with array factor synthesis in synthetic aperture interferometric radiometers (SAIRs) is presented. The presented method can efficiently control the characteristics of the equivalent array factor of SAIR, such as null position and depth, sidelobe level, and so on, which will be beneficial in the presence of potential interferences, such as radio frequency interference. The method is based on the formulation that the SAIR array is equivalent to a phased array with a virtual antenna element located in each baseline. The previous built-in method and the conventional Fourier inverse method can be seen as a special case of the presented one. Numerical results validate the effectiveness of the presented method.

The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

An accurate geolocation of the radio frequency interference (RFI) sources is significant to effectively switch off illegal transmitters. In this study, utilizing the sparse property of RFI in the observed scene, a super-resolution RFI localization method based on compressive sensing is presented in synthetic aperture interferometric radiometer (SAIR). Numerical results show the presented method can achieve an super-resolution RFI localization even when there are some missing data due to correlator or receiver failure.

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surface at microwave band. In order to reconstruct the brightness temperature maps from the inteferometric measurements stably and uniquely, it has been recommended to cure the corresponding ill-posed problem with the aid of regularization framework. However, the performances of such numerical regularized solutions highly depend on manually choosing the regularized parameters. In this study, we proposed a statistical regularization method to estimate the optimal regularized parameter of the SAIR inversion adaptively. Furthermore, we have carried out some numerical simulations in reference to the SAIR inversion, and relative comparative analysis has been accomplished to validate the proposed method.

Traditionally, the system errors of synthetic aperture interferometric radiometers (SAIRs) are analyzed and classified by the causes they are generated, which is easy for understanding but complicated for calibration. This paper presents a new error model based on the visibility function to analyze the system errors of SAIRs. In this model the system errors are classified by their effects on the ideal visibility function, which is simple for calibration. Firstly, the error model based on the visibility function is proposed, in which the errors according to their effects on the ideal visibility function are grouped in: 1) the orientation-independent multiplicative error, 2) the orientation-dependent multiplicative error, and 3) the additive error. Then, the two overall calibration methods respectively using external reference source and external reference scene are proposed. Finally, the preliminary calibration experiment results on the 8mm-band HUST-SAIR prototype show the validity of the error model and the overall calibration methods. The study in this paper provides a means to reduce the complexity of calibration routine of SAIRs and to improve the imaging performance of SAIRs.

Regularization methods are very efficient in reconstructing the radiometric brightness temperature maps form interferometric measurements. The performance of these methods depends on selecting an optimal regularization parameter, which is typically defined by using the cross-validation or the L-curve. In this paper, we present a novel brightness temperature reconstruction method, which can automatically learn the optimal parameter by Bayesian learning without reducing the performance of the brightness temperature image. To support the theory, numerical simulations are presented and analyzed with emphasis on stability and error analysis.

This letter deals with small target detection in passive millimeter wave (PMMW) imaging. Specifically, it focuses on a general detection scheme, where, first, the background is suppressed through a background prediction algorithm, and then the detection is accomplished. A precise prediction of the background is essential to a successful outcome. In practical applications, background estimation problem is more suitable to be considered as a nonlinear regression problem. Kernel methods are effective to solve the nonlinear problem. To improve the accuracy of the background prediction with kernel methods, we utilize robust loss function, to tolerate the noise outliers, and regularization methods, to avoid overfitting of the data. Experiments are conducted on PMMW images collected by a synthetic aperture imaging radiometer. The results demonstrate the effectiveness of the proposed algorithm.

Mirrored interferometric aperture synthesis (IAS) (MIAS) is a novel interferometry which has the potential to reduce system complexity compared with that of conventional IAS. Array configuration design plays a major role in imaging. As the concept of MIAS was proposed just a few years ago, the problem of array configuration design has not been solved yet. In this letter, the principles of array configuration design of 1-D MIAS are proposed, and the corresponding optimization model is established and refined. The optimal array configurations are presented through simulated annealing method and show that 1-D MIAS can achieve almost twice as much of spatial resolution as 1-D IAS with the same array size.

A general platform for millimeter wave synthetic aperture radiometers is introduced in this paper. The platform consists of hardware and simulation software, and has a flexible and open structure. Simulation and experiment results show the platform is suitable for simulation of synthetic aperture radiometer performance, algorithm performance, error influence on radiometer performance, and calibration performance. The platform can be used for radiometry simulation and design of synthetic aperture radiometers.