Microwave sounders aboard small satellite constellations in low Earth orbit (LEO) and geostationary orbit (GEO) satellites represent two promising approaches to improve the temporal resolution of microwave atmospheric observations. A observing system simulation experiment (OSSE) is an effective means to assess the potential contribution of future observations to numerical weather prediction (NWP). Using the established GEO microwave OSSE framework and nature run (NR) data from the CMA-MESO model, this study aims to compare the OSSE results of microwave sounders on a LEO constellation (LEO-C) and a GEO satellite against those derived from the current LEO microwave observations. The cross-comparative analysis reveals that the assimilation of microwave observations from the LEO-C and the GEO satellite into the CMA-GFS model significantly improves typhoon track prediction compared with current LEO microwave observations.

The Chinese Ocean Salinity Mission (COSM) was successfully launched on November 14, 2024, with the objective of observing global sea surface salinity (SSS). To fulfill this mission, two payloads were onboard, one of which is a key instrument called the microwave imager combined active and passive (MICAP). MICAP comprises L-/C-/K-band 1-D synthetic aperture interferometric radiometers (SAIRs) that share a parabolic reflector, as well as an L-band scatterometer. As evidenced by SMOS, accurately locating low- and mid-level radio frequency interference (RFI) signals for SAIR is challenging due to issues such as relatively low sensitivity and Gibbs oscillations in complex scenarios. Owing to the relatively small number of array elements in 1D-SAIRs and advanced data transmission technology, MICAP has achieved the capability to downlink short-duration array raw sampling data before array signal correlation, providing strong support for identifying low- and mid-level RFI signals in complex scenarios. In this article, a signal-level RFI localization method is proposed. First, the strong RFI signal is eliminated by leveraging the spatial point source characteristics of RFI. Then, low- and mid-level RFI detection and localization are achieved by extracting non-Gaussian RFI signals from Gaussian background signals through the calculation of kurtosis spectra. Simulations demonstrate that the detection rate and localization accuracy for low- and mid-level RFI signals can be significantly improved.

The Ice Cloud Imager (ICI) aboard the second generation European meteorological satellites is the first space-borne submillimeter wave radiometer used for ice cloud remote sensing. Since ice clouds simultaneously contain three ice phase particles - ice, snow and graupel - and the physical distribution characteristics and submillimeter wave radiation properties of the three kinds of particles are different, simultaneously retrieving the physical parameters of the three ice cloud particles from submillimeter brightness temperatures is very challenging. This paper proposes a multi-phase ice cloud particle retrieval algorithm based on convolutional neural networks (CNNs) that can simultaneously retrieve total path and profile of ice, snow and graupel particles from submillimeter brightness temperatures. The retrieval experiments using the simulated ICI brightness temperatures show that the average relative error of retrieved total path of the three kinds of frozen particles is all lower than 57% and those of the content profiles is all lower than 68%.

The potential of satellite observations at millimeter and submillimeter wavelengths for retrieving cloud ice has been demonstrated with data from airborne instruments. A major limitation in inversions of ice hydrometeors is the lack of accurate calculations of the bulk scattering parameters of ice clouds. The most uncertain aspect of modeling the millimeter- and submillimeter-wave scattering properties of ice hydrometeors lies in their shape. To address this issue, instead of calculating the bulk scattering properties of ice clouds by integrating single-shaped ice hydrometeors, this letter proposes the construction of an enhanced neural network to directly fit the bulk scattering parameters of ice clouds. The network parameters are optimized using actual satellite observations, such as Advanced Technology Microwave Sounder (ATMS) through deep reinforcement learning (DRL). Finally, a validation experiment is conducted by comparing the calculated brightness temperature (BT) with the actual ATMS-observed BT. The results demonstrate that a 21.3% reduction in the average error of BT calculated by the enhanced network compared to that obtained from the nonspherical particle model.

Geostationary orbit (GEO) microwave sounding technology, which can continuously monitor Earth and intensively observe weather conditions such as strong convection, has unique advantages. An observing system simulation experiment (OSSE) is an important means to evaluate new meteorological instruments and explore assimilation techniques for new observations. In this article, an OSSE based on the China Fengyun-4M (FY-4M) GEO microwave satellite is introduced in detail. The nature run (NR) of the OSSE is generated by the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO), while the Global Forecast System of the CMA (CMA-GFS) is used to achieve the 4-D variational (4D-Var) assimilation and prediction experiment. The atmospheric parameters from the NR are used as inputs to the radiative transfer (RT) for the TIROS operational vertical sounder (RTTOV) model to simulate the FY-4M GEO microwave brightness temperature. The OSSE results show that assimilating GEO microwave data has a positive effect on typhoon forecasting. Specifically, the shorter the observation time interval is, the smaller the observation noise and the better the assimilation effect. There are differences in the role of different frequency band channels in improving forecasts; the best observation effect can only be achieved by setting the observation error appropriately, and the data preprocessing strategy is crucial for improving the assimilation quality. The GEO microwave OSSE further reveals the influence of future FY-4M geostationary microwave observations on typhoon forecasts and the issues that need to be addressed when assimilating these new data.

The mirrored aperture synthesis (MAS) was proposed to improve the spatial resolution in passive microwave remote sensing. In MAS, the elements in the (H matrix) are the sampled values of the h function. In this article, the h function considering practical factors is derived. These practical factors include the normalized antenna pattern, the reflector reflectivity, the reflector length, and the distance between the array and the scene. The normalized antenna pattern modulates the received signals. The reflector reflectivity affects amplitudes of the reflected signals. The reflector length determines whether the reflected signals can be received. The distance between the array and the scene influences the incident angles of the signals from the same point source in the scene. The distance also influences the path differences between signals. The formulas and related analysis are verified through the numerical simulations and experiments.

L-band Radio Frequency Interferences (RFIs) seriously contaminate remote sensing data. This poses a major challenge in processing microwave radiometric data and can substantially degrade the quality of the data. There is an urgent need to estimate RFI intensity in order to mitigate Gibbs errors caused by RFIs along the sidelobes. The traditional CLEAN algorithm is limited in its ability to account for the interactions among multiple RFIs, which results in biased intensity estimations. In this paper, the RFI brightness temperature model is modified to incorporate the interactions among multiple RFI sources. Building upon this modified model, we establish fundamental equation sets and propose a numerical solution method for calculating RFI intensity. Experiments and simulations based on Soil Moisture and Ocean Salinity (SMOS) data confirm that the method presented in this paper, which we have named MUSID, outperforms the traditional CLEAN algorithm.

Mirrored aperture synthesis (MAS) has been proposed as a novel interferometry to achieve higher spatial resolution in passive microwave remote sensing. The phase and amplitude errors for MAS are a crucial problem that has yet to be analyzed. In this letter, a model for the phase and amplitude errors is established. A correction method based on an external source is proposed to correct the phase and amplitude errors. An analysis of the position of the external source is performed by computing its impact on the radiometric accuracy of a reference scene. The imaginary parts of the cross correlations for MAS are analyzed for the first time. Simulations and experiments are carried out to demonstrate the effectiveness of the correction method.

In this letter, 1-D mirrored aperture synthesis with two tilted reflectors (1-D MAS-T) is proposed to improve the spatial resolution of an antenna array without extending its size. The principle of 1-D MAS-T is given. The size of the reflector is analyzed, and detailed calculation formulae is derived. Numerical simulations and experiments are carried out to illustrate the performance of 1-D MAS-T. The results demonstrate that 1-D MAS-T can achieve a higher spatial resolution than conventional aperture synthesis with the same antenna array.

The arrangement of the antenna array of the mirrored aperture synthesis (MAS) determines the transformation matrix for solving the cosine visibilities. When the transformation matrix is rank deficient, the solved cosine visibilities will introduce the rank-deficient error. For the two-dimensional mirrored aperture synthesis (2-D MAS), the existing literature has discussed the relationship between the arrangement of the antenna array and the rank-deficient error but has not proposed an array that can achieve a full rank transformation matrix, which is call the rank-full array (RFA). Based on a new method for calculating the rank of the transformation matrix and the literature on one-dimensional (1-D) RFAs, this paper proves that 2-D RFA cannot be obtained when the reflectors are all metal, and by introducing a reflector made of magnetic conductor, 2-D RFA can be obtained. Besides, this paper obtained the existence conditions for 2-D RFA. Based on those conditions, two methods are proposed to obtain 2-D RFAs.

The antenna array arrangement determines the rank of the transformation matrix in 1-D mirrored aperture synthesis (1-D MAS). Only when the transformation matrix is of full rank can cosine visibilities be precisely solved from the transformation equations. However, the existing literature states that full rank cannot be realized with the default perpendicular polarization of antennas. In this letter, the transformation matrices of arrays with various polarizations of antennas are discussed, and a type of full array with a full-rank transformation matrix is found. Based on these results, the existence conditions for an array with a full-rank transformation matrix are obtained. In addition, a search algorithm with a shorter search time than the existing algorithm is designed to find low-redundancy arrays with a full-rank transformation matrix. The simulations verify that cosine visibilities are precisely solved from the transformation equations for the array with a full-rank transformation matrix.

The mirrored aperture synthesis radiometer (MASR) has been proposed for high-resolution observation in recent years. Since the MASR is a kind of linear system, the matrix reconstruction method should be an alternative for image reconstruction of a practical MASR. However, the matrix reconstruction method is neither theoretically analyzed nor experimentally validated at present, and the performance of the matrix reconstruction method for a MASR in practical applications is still unknown. In this letter, the H-matrix reconstruction method including the truncated singular value decomposition (SVD) and Tikhonov regularization, not yet applied to a MASR, is introduced. Then, the numerical simulations and imaging experiments are performed to validate the presented method, and the performance of the presented method is also shown. Finally, the comparisons between the presented method and the cosine-transform-based iterative method are made in the view of imaging experiments. The H-matrix reconstruction method is more convenient in the situation of a small MASR with a small alias-free field of view (FOV).

The assimilation of spaceborne passive microwave measurements often suffers from representativeness errors due to the mismatch between the observation footprints and numerical weather prediction (NWP) model grids. In this article, a new brightness temperature remapping technique based on a deep convolutional neural network (CNN) is proposed to reduce the representativeness error in FengYun-3D (FY-3D) microwave radiation imager (MWRI) observation data assimilation. The remapping technique uses an adapted dataset construction method in which the training data consist of synthetic NWP-model-grid-based MWRI brightness temperature ($T_{B}$) images and synthetic MWRI-observed antenna temperature ($T_{A}$) images. The synthetic$T_{A}$and$T_{B}$, which are generated through radiative transfer for TOVS (RTTOV) model and MWRI degradation model, make the network learn the spatial remapping relationship between observation and background$T_{B}$in data assimilation. In addition, land–sea mask information is input into the CNN to help the network better analyze the coastline area data. The CNN-based remapped MWRI observation data are evaluated through observation minus background (OMB) diagnosis with the Global/Regional Assimilation and PrEdiction System (GRAPES) four-dimensional variational (4D-Var) system. The experimental results illustrate that the bias and standard deviation of OMB with the CNN-based remapped MWRI observation are quantitatively reduced compared with the raw measurements in GRAPES 4D-Var.

Setting two reflectors perpendicular to each other and an array produces mirrored aperture synthesis (MAS), which obtains a higher spatial resolution than aperture synthesis (AS) for the same antenna array. The proposal of the MAS theory and the experimental verification suggest that the spatial resolution can be improved by setting the reflector. This paper proposes a method in which four tilted planar reflectors are set around an antenna array. This method is named two-dimensional (2-D) mirrored aperture synthesis with four tilted planar reflectors (2-D MAS-T). Because 2-D MAS-T uses more reflectors than MAS, the spatial resolution is further improved. The principle of 2-D MAS-T with an antenna array is given in this paper. The relationship between the size of the reflectors, the spatial resolution and the field of view (FOV) is obtained. The simulation and experimental results demonstrate that 2-D MAS-T can achieve a higher spatial resolution with the same antenna array than 2-D AS and 2-D MAS.

We propose a novel approach that integrates underparameterized RANSAC (UPRANSAC) with Hough Transform to detect vanishing points (VPs) from un-calibrated monocular images. In our algorithm, the UPRANSAC chooses one hypothetical inlier in a sample set to find a portion of the VP’s degrees of freedom, which is followed by a highly reliable brute-force voting scheme (1-D Hough Transform) to find the VP’s remaining degrees of freedom along the extension line of the hypothetical inlier. Our approach is able to sequentially find a series of VPs by repeatedly removing inliers of any detected VPs from minimal sample sets until the stop criterion is reached. Compared to traditional RANSAC that selects 2 edges as a hypothetical inlier pair to fit a model of VP hypothesis and requires hitting a pair of inliners, the UPRANSAC has a higher likelihood to hit one inliner and is more reliable in VP detection. Meanwhile, the tremendously scaled-down voting space with the requirement of only 1 parameter for processing significantly increased the performance efficiency of Hough Transform in our scheme. Testing results with well-known benchmark datasets show that the detection accuracies of our approach were higher or on par with the SOTA while running in deeply real-time zone.

In 2-D mirrored aperture synthesis (2-D MAS), the array configuration determines the rank of the transformation matrix, which can affect the accuracy of the solved cosine visibilities. Ultimately, errors in solved cosine visibilities degrade the quality of reconstructed brightness temperature images. In this letter, the maximum rank of the transformation matrix is analyzed and proven by mathematical induction. An array optimization model of 2-D MAS based on the “maximum-rank” criterion is established, and optimal arrays are presented. Finally, the simulations and experiments are carried out to demonstrate the effectiveness of the optimization model.

In two-dimensional mirrored aperture synthesis (2-D MAS), the rank of the transformation matrix can affect the accuracy of the solved cosine visibilities; therefore, it has an impact on the accuracy of the reconstructed brightness temperature image. In this article, the influence of the rank deficient on the accuracy of the reconstructed brightness temperature image of 2-D MAS is discussed. An analysis of the rank-deficient error is performed by computing its impact on the radiometric accuracy for a reference scene. Two correction methods based on multiple measurements are proposed to correct the rank-deficient error. The simulations and experiments are carried out to demonstrate the effectiveness of the proposed methods.

The concept, principle, and method of microwave radiometric imaging with mirrored aperture synthesis (MAS) were proposed by the Huazhong University of Science and Technology (HUST), Wuhan, China. MAS can achieve higher spatial resolution without extending the size of the antenna array. However, the MAS principle has not been sufficiently validated by practical experiments. To solve this problem, an innovative experiment system of MAS at the V-band (MAS-V) has been developed by HUST. Experiments on the interference fringe pattern, spatial resolution, as well as the imaging of an electric warmer and the absorbing material in the styrofoam box filled with liquid nitrogen are performed. The initial experimental results demonstrate that MAS can achieve higher spatial resolution with the same antenna array compared with the conventional AS. For MAS, the experimental results are consistent with theory and simulation results.

A numerical simulator for analysis of the geostationary orbit multispectral passive microwave (Geo-MW) mapping and sounding is described, which mainly includes upwelling brightness temperatures forward, microwave radiometer payload simulation and atmospheric profile retrieval. This simulator allows evaluation and optimization of the ability of microwave sensors to retrieve atmospheric temperature and humidity profile. In this paper, end to end simulation study of a Geo-MW radiometer observation at selected frequencies from 50 to 425 GHz with a real-aperture antenna is performed and the accuracy of the brightness temperature measurement and the atmospheric temperature and humidity profile sounding are quantitatively analyzed and evaluated.

Mirrored Aperture Synthesis (MAS) was proposed to improve spatial resolution for passive microwave remote sensing. However, the MAS theory needs experimental verification. In this paper, an Experimental System of MAS (MAS-V) at V frequency band is introduced, which is developed for verifying the principle and performance of MAS. MAS-V is composed of an antenna array, reflectors, a receiving channel array, a synchronous acquisition subsystem, a computing server, and a rotatory platform. The initial experimental results demonstrate that the principle of MAS is valid. The experimental spatial resolutions are consistent with the theoretic spatial resolutions.

In this letter, 1-D mirrored aperture synthesis with a rotating reflector (1-D MAS-R) is proposed to improve the spatial resolution and reduce the number of required antennas for passive microwave remote sensing. The principle of the 1-D MAS-R with an antenna array is given, and from the principle, the 1-D MAS-R with only one antenna can also reconstruct the image of the scene. Simulation results demonstrate the validity of the 1-D MAS-R even with only one antenna, and the spatial resolution is improved by increasing the distance between the reflector and the antenna or antenna array.

The passive microwave observations from geostationary earth orbit (GEO) are able to enhance the short-time forecasting of tropical cyclones (TCs) due to its ability to track and detect the internal structure of TCs. To assess the operational capabilities of the candidate GEO microwave instruments, the numerical simulations of GEO microwave observation for the three TC cases were carried out. This paper compares the abilities of 3 candidate passive microwave sensors, based on GEM/GOMAS, GeoSTAR and GIMS-II respectively, to observe the upwelling brightness temperatures of TCs at 50-57GHz band. The analysis is based on WRF model variables and radiative transfer mode DOTLRT.

Mirrored aperture synthesis (MAS) can improve spatial resolution compared with conventional aperture synthesis (AS). In this paper, for 1D MAS and 2D MAS, the array optimization models with the autocorrelation are established. The optimal array configurations based on the models are presented. The simulation results demonstrate that the optimal array configurations have good performance in imaging and can achieve higher spatial resolution than other MAS array configurations with the same antenna number.

Mirrored Aperture Synthesis (MAS) was proposed to improve spatial resolution with fewer antennas for passive microwave remote sensing. In this paper, an Experiment System of MAS (ESMAS) at 36.4GHz is introduced, which is designed for verifying the principle and performance of MAS. ESMAS is composed of an antenna array, four reflectors, a receiving channel array, a synchronous acquisition subsystem, a processor, and a rotatory platform. The initial experiment results are given.

To assimilate satellite-based passive microwave observation over heavy clouds and precipitation into numerical weather prediction (NWP) systems and thus to improve the performance of it has become an intensely studied topic. These attempts rely on the development of a radiative transfer (RT) model that accounts for particle scattering and accurately simulates the observation process at an acceptable computational speed. In this paper, two existing RT models (the RTTOV and the DOTLRT) are tested and the accuracies are evaluated by comparing the simulated brightness temperature with real observational data of a satellite-based sounder ATMS. RTTOV is much more well-known and widely applied, however, since these two models use different solvers to deal with the scattering effect, whether it is possible to improve the performance of RT model is still open to discussion.

Nonuniform aperture synthesis radiometers (NASRs) have the advantage of flexibility in practical applications. However, the main limitation of NASRs for Earth observation is the poor reconstruction quality. In this letter, the array factor of 1-D NASR is analyzed. Array factor forming (AFF) is introduced to improve the reconstruction quality of 1-D NASRs. The limitations of the AFF on array configurations are given. The NASR that meets the given limitations is called the optimizable NASR (ONASR). The numerical results and experimental results demonstrate that the AFF is able to improve the reconstruction quality of 1-D ONASRs. To assess the performance of the AFF thoroughly, the comparisons are made against the linear interpolation and the regularized G-matrix method. Meanwhile, the comparisons between the AFF-based 1-D ONASRs and the 1-D uniform ASRs are also made. The comparison results demonstrate that the AFF can obtain better reconstruction quality than the linear interpolation and the regularized G-matrix method. Additionally, the comparison results also demonstrate that the reconstruction quality of AFF-based 1-D ONASRs can approach that of 1-D uniform ASRs.

The calibration of aperture synthesis radiometers (ASRs) with large arrays by internal noise injection suffers from high increased mass and volume and is not capable to deal with the out-of-plane distortion of antenna arrays. In this letter, a hybrid calibration method is proposed, which combines the redundant baselines and reduced noise injection. The hybrid method needs only a small noise injection network and can deal with the out-of-plane distortion of antenna arrays. The performance of the hybrid method depends on the noise injection element distribution, which is analyzed and given based on minimizing the root mean square of all residual amplitude and phase errors. Simulation results demonstrate its performance. The proposed hybrid method is particularly helpful to the calibration of ASRs with large arrays in geostationary orbit.

The numerical simulations of imaging technology for analysis of geostationary multispectral passive microwave observation is described. This paper compares the abilities of three candidate passive microwave sensors to image the upwelling atmospheric brightness temperature. Presented are scattering-based simulations of a land falling hurricane that illustrate the imaging capabilities of a filled-aperture antenna instrument refer to GEM, a Y-shaped aperture synthesis array instrument refer to GeoSTAR and a Ring-shaped aperture synthesis array instrument refer to GIMS.

Aperture synthesis technique can provide high spatial resolution without requiring very large and massive real aperture. However, large aperture synthesis systems are very complicated. In this paper, Rotating Mirrored Aperture Synthesis (RMAS) is proposed, which combines rotating and mirrored aperture synthesis to improve spatial resolution with fewer antennas. The principle of RMAS is presented. The initial simulation result shows the RMAS system can approximately reproduce the brightness temperature image of the observed scene.

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.

Nonuniform aperture synthesis radiometers (NASRs) are emerging in Earth remote sensing. The main disadvantage of NASRs is the complexity of the image reconstruction. In this letter, the gridding method is introduced as an efficient method to reconstruct brightness temperature images for NASRs. In the gridding method, a Voronoi diagram is applied to dividing the sampling region in the spatial frequency domain into a number of small cells, and the area of each cell is regarded as the density compensation factor. Meanwhile, a method is proposed to produce the outermost Voronoi cells for the outermost visibility samples. The errors induced by using the gridding method are analyzed. The nonuniformity factor (NF) is defined to indicate the nonuniformity of a sampling pattern. The numerical results verify that the gridding method can be applied to image reconstruction for NASRs and that the accuracy of reconstructed images is dependent on NF. The comparison between the gridding method and the nonuniform fast Fourier transform algorithm shows that the gridding method has lower computational complexity.

In order to quantitatively interpret topographic effect on Chang'E (CE) microwave data, a detailed method to compute brightness temperature (TB) over a lunar crater is proposed, which incorporated the effect of surface tilts. The method improves the effective solar irradiance model of the lunar surface to obtain the temperature profile of the lunar crater. The calculated TB at 37 GHz with the proposed computation method, which is based on three digital elevation models (DEMs) from different sources, are consistent with the observed TB from the CE-2 microwave radiometer. The simulated behavior of TB across crater Hercules reproduces the TB undulation observed by CE in a single swath. TB is significantly affected by the lunar dust of a lunar crater and affected by albedo and emissivity in a lesser degree. Based on the explanation with simplified models, the TB variation over a crater is proved to be significantly affected, through physical temperature, by the crater shape described by DEMs. With the simplified crater model, the amplitude of the TB oscillatory curves is proved to depend on the crater shape.

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.

The calibration of aperture synthesis radiometers (ASRs) by the standard method of noise injection would seriously increase hardware requirements and system complexity. To reduce the network of noise injection, redundant space calibration (RSC) method has been considered. However the number of error phases is larger than the number of independent equations. In this paper, an external calibration method with 120-degree antenna array rotation for geostationary ASR with staggered Y-shaped array is proposed. More redundant baselines are produced by rotation. The equations by rotation and by RSC are combined for solving the phase errors. Basic procedure on this method and preliminary simulation are introduced. As another advantage, if twice 120-degree rotation is taken, one of the antenna arms can be removed from staggered Y-shaped arrays with the missing baselines supplied by the rotations. In this way, the number of antenna elements can be reduced by 1/3.

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.

The on-board calibration of aperture synthesis radiometers (ASRs) with large arrays by the standard method of noise injection would seriously increase hardware requirements and system complexity. To avoid the use of noise injection, an external phase calibration method with antenna array rotation is proposed in this letter, which uses the observation scene as reference without needing for an external calibrator. The relationship of the visibility samples before and after the rotation is established and used to solve for the instrumental phase errors. The proposed method has been proved to be valid by simulations as well as some preliminary experimental results. It is applicable to geostationary ASRs with rotating array such as the geostationary atmospheric sounder.

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.

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.