In recent years, the application of passive millimeter-wave imaging (PMWI) in target detection, reconnaissance, and surveillance has drawn our attention again, especially passive interferometric millimeter-wave imaging (PIMI). However, PIMI suffers with high hardware and signal processing complexity, and passive interferometric millimeter-wave sensors (PIMSs) are very expensive. In this letter, a W/D-band millimeter-wave radiometer on unmanned aerial vehicles (UAVs) is first developed for target detection, reconnaissance, and surveillance, especially for ship detection. The W/D-band millimeter-wave radiometer has the advantages of low hardware and signal processing complexity and low cost. The W/D-band millimeter-wave radiometer is introduced, and a proof prototype of the W/D-band millimeter-wave radiometer is manufactured. Numerical simulations are performed to analyze the brightness temperature (TB) characteristic of the metallic ships. Outdoor experiments are performed to assess the feasibility of the metallic target detected by the W/D-band millimeter-wave radiometer on a UAV. Initial experimental results have demonstrated that metallic targets can be detected by the W/D-band millimeter-wave radiometer from UAVs, as expected.

Passive interferometric microwave sensors (PIMSs) have been proposed for target detection, such as ships and aircrafts. However, the detection performance of the PIMSs has not been analyzed and discussed for target detection in detail before. In this letter, the detection performance of the PIMSs is analyzed in terms of detection probability at a constant false alarm rate (FAR). An end-to-end performance simulator is established to analyze the detection performance of the PIMSs in terms of detection probability at a constant FAR that 10-5in detail. Numerical simulations are performed to analyze the detection performance of the airborne PIMS for ship detection. The numerical results indicate that the detection performance depends on the height (distance), the targets, and the PIMSs, as expected.

Researches have demonstrated that passive interferometric microwave sensors (PIMSs) can be used in the imagery acquired and target detections, especially in low visibility and/or high sea clutter environments. Target detection by PIMSs is based on the high contrast against the background in the brightness temperature (TB) maps, and it is found that the targets still exhibit a high contrast within the alias field of view (FOV) of the background cancellation TB maps obtained by a background canceling method in the target detection algorithm. As a result, the targets can also be detected within the alias FOV of PIMSs, and PIMSs exhibit a wider FOV for target detection than that for Earth’s remote sensing. However, the effective FOV of PIMSs has never been studied for target detection before. In this article, the effective FOV of PIMSs is first studied for target detection. The effective FOV of PIMSs is analyzed and discussed for target detection in theory, which is based on the FOV of PIMSs for Earth’s remote sensing and the background canceling method. Numerical simulations are performed to analyze the effective FOV of PIMSs in terms of the metallic with a “cold” TB characteristic and the island with a “hot” TB characteristic. A series of experiments have been performed to verify the effective FOV of the PIMSs for target detection in ships, islands, and aircrafts. Both numerical and experimental results have demonstrated that the effective FOV of the PIMSs for target detection based on the background canceling method can be extended to the central hexagonal regions (${H}$), and it exhibits a wider FOV than alias-free FOV (AF-FOV) and/or extended AF-FOV (EAF-FOV) in Earth’s remote sensing, as expected.

A microwave radiation model with coherent surface scattering is developed for analyzing the topographic effects of the lunar permanently shadowed region (PSR) on microwave radiometric observations. The coherent surface scattering to the observer, which comes from the nearby surface due to the variation of the surface slope, is quantified by the ray-tracing method. In addition, the vertical distribution of temperatures of the PSR is estimated by a one-dimensional thermal model with three-dimensional shading and scattering effects. The impact of coherent scattering on microwave brightness temperatures by a nadir-look radiometer becomes more noticeable with high-resolution microwave brightness temperature data, such as 4 pix/deg by 4 pix/deg in this study. The rise in brightness temperature at certain locations in PSR may reach up to ~8 K at 37 GHz. However, when compared with the low resolution of the brightness temperature by Chang’E-2, the averaged contribution of coherent surface scattering is less than 1 K. Meanwhile, there is a discrepancy between the model-generated microwaveTBand the measuredTBof Chang’E-2, both in small and large craters. This discrepancy may be explained by a calibration issue or the uncertainty of model parameters. Nonetheless, the trend in the model-generated brightness temperature is consistent with the measurements, indicating that the proposed model has the potential to predict brightness temperature effectively within the permanently shadowed region.

Airborne passive interferometric microwave sensors (PIMSs) were proposed as a powerful complementary tool for ship detection, especially in low visibility and high sea clutter environments. At present, there is only one algorithm for ship detection by airborne PIMSs, and this algorithm is only based on the “cold” brightness temperature (TB) characteristic of metal ships with wakes in passive interferometric microwave images (PIMIs). Moreover, it has been found that ship wakes always exhibit a “hot” TB characteristic in PIMIs. In this article, an improved algorithm is innovatively proposed for ship detection by airborne PIMSs to improve detection performance, and it is simultaneously based on the “cold” and “hot” TB characteristics of metal ships with wakes. The microwave TB model of metal ships with wakes is established to analyze its TB characteristics. Given the TB model and its characteristics, an improved algorithm is introduced, which comprises five steps: searching targets, improving TB maps, clustering targets, estimating ship headings, and estimating ship trajectories. The improved algorithm can detect ships with a low false-alarm probability (FAR) and accurately estimate ship headings using only a single snapshot and the ship trajectory. The practicability and detection performance of the improved algorithm is verified by airborne experiments using an X-band PIMS. The experimental results demonstrate that the improved algorithm can accurately detect ships, estimate ship headings using only a single snapshot, and estimate ship trajectories. The impact of the algorithm parameters on the detection performance is analyzed and discussed. Moreover, the detection performance of the improved algorithm is analyzed and compared with the performance of the only existing algorithm, and the experimental results indicate that the improved algorithm has a better detection performance with a lower FAR value than the only existing algorithm, as expected.

The critical advantage of passive microwave imaging is that it can be used in all-day and in low visibility conditions, such as hazes, fogs, clouds, smokes, and sandstorms. In this article, the ground-based passive interferometric microwave sensor (PIMS) is proposed to detect civil aircrafts, especially in low visibility conditions. The fundamental of civil aircraft detection by a ground-based PIMS is introduced and analyzed. Numerical simulations are performed to assess its feasibility and detection performance, and the impact of the low visibility weather is analyzed and discussed. To verify the practicability of civil aircraft detection by the ground-based PIMS, a series of experiments have been performed by a ground-based X-band PIMS, and a data processing procedure is introduced, which contains a target detection algorithm. Experimental results have demonstrated that the civil aircrafts can be accurately detected and consecutively tracked by the ground-based PIMS using the target detection algorithm. Meanwhile, a mirrored TB phenomenon, the TB change, and an application scheme are analyzed and discussed. As a conclusion, the ground-based PIMS can be considered as a powerful complementary tool for civil aircraft detection.

The scanning microwave radiometer (SMR) is an important payload of the Haiyang-2B (HY-2B) satellite. The main reflector of the SMR has its own emission due to the imperfect reflector material and surface roughness; however, the reflector’s emissivity was not measured or estimated during the prelaunch phase. During the post-launch phase, it was discovered that the radiation contribution from the reflector changed with its position and over time. This phenomenon affected the accuracy of the calibration by a few Kelvins. This report analyzes the main reflector’s emissivity based on the observed results when HY-2B pitched over to view the homogeneous and isotropic cold space. The emissivity of the reflector was approximately 3%, but owing to the larger field of view, the emissivity of 6.925 and 10.7 GHz were not well assessed. After the radiation from the reflector has been corrected, the calibration error caused by the reflector’s emissivity is reduced.

Finding out and turning off the radio-frequency interference (RFI) sources as many as possible is an important work for synthetic aperture interferometric radiometer (SAIR). While the impact of strong RFIs on reconstructed brightness temperature (BT) image is clearly observable, the presence of low- or moderate-level RFIs is often hard to be aware of, leaving the RFI detection and localization a difficult problem. In this article, an improved RFI detection method is proposed by making use of the point-source ripple to give prominence to the RFIs presented in the SAIR imagery. The method tests based on Soil Moisture and Ocean Salinity satellite (SMOS) visibility samples and the performance evaluations by numerical simulations validate the correctness and effectiveness of the developed method.

The presence of unauthorized radio frequency interference (RFI) sources seriously affects the retrieval of brightness temperature of a synthetic aperture interferometric radiometer (SAIR). Postobservation RFI geolocation is important for improving the RFI situation hereafter and mitigating the impact of the identified RFIs. Previous works have demonstrated the potential of using high-/super-resolution direction-of-arrival (DOA) estimation techniques to achieve accurate localization of RFI sources in SAIR imagery. In this article, a multiple snapshot difference method is proposed to further reduce the RFI source geolocation bias. The covariance matrices of the consecutive snapshots with power changed RFIs are picked out and subtracted to cancel out the background scene. A MUSIC-based DOA algorithm is developed. Numerical simulations validate the theoretical correctness and the practical effectiveness of the proposed method.

Currently, many microwave radiometers have been successfully used in earth-observation and ocean-monitoring systems. In China, the scanning microwave radiometers (SMR) onboard the Haiyang-2A/2B (HY-2A/2B) satellites are multichannel radiometers capable of obtaining oceanic and atmospheric parameters, such as sea surface temperature (SST), sea surface wind speed (WS), water vapor (WV), and cloud liquid water (LW). As a successor to HY-2A, the HY-2B satellite was launched successfully on October 25, 2018. Compared with the HY-2A’s SMR, the HY-2B’s SMR is an improved microwave radiometer. This article is concerned with the instrument design of the HY-2B’s SMR and its improvements. The important improvements of the HY-2B’s SMR in regard to the feedhorns and cold-sky reflector (CSR) are verified, and the early in-orbit performance of the HY-2B’s SMR is evaluated. The results demonstrate that the HY-2B’s SMR exhibits an important improvement on the HY-2A’s SMR in terms of the quality of the brightness temperature (TB) products and calibration accuracy. The results also show that the HY-2B’s SMR has a good performance in terms of the global TB images and the retrieved products with a good agreement with the expectations.

Radio frequency interference (RFI) seriously deteriorates the quality of the retrieval of geophysical parameters, e.g., Earth surface moisture and ocean salinity, measured in microwave interferometric radiometry (MIR). The accurate detection of RFI sources is crucial for locating these illegal sources and mitigating their impact. In this article, we propose a new method based on reweighted$\ell _{1}$-norm minimization to detect RFI sources. First, we exploit the sparsity of RFI sources in the spatial domain and formulate the RFI detection as a problem of reweighted$\ell _{1}$-norm minimization, by which the RFI signals can be well recovered and the background noises can be suppressed. Then, we present two algorithms, termed RL1 and NRL1, to achieve RFI source detection. The RL1 algorithm employs a fast iterative shrinkage thresholding (FIST) technique, and the NRL1 algorithm combines the FIST with a neighbor-reweighting strategy that helps to further enhance the RFI target regions. Finally, simulations and experiments using Soil Moisture and Ocean Salinity (SMOS) satellite data demonstrate the superiority of the proposed method on the RFI-signal-to-background ratio (RSBR) in recovered images and the detection performance of RFI sources, compared with the existing RFI processing methods in MIR.

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.

Radiometric sensitivity is crucially important for microwave interferometric radiometers. To pursue optimum performance of radiometric sensitivity, the minimum-degradation arrays (MDAs) or low-degradation arrays (LDAs) are usually employed. In this article, we propose a deterministic method for designing low-degradation linear arrays (LDLAs), which exploits the multiple-fold redundancy property of baseline coverage (i.e.,$u$–$v$coverage) of interferometric arrays and further devises analytical patterns for closed-form geometric construction. The proposed method can not only attain LDLAs with satisfactory radiometric sensitivity in significantly low computational complexity, given any number of sensor elements, but also has easy adoption on large array synthesis and configuration expansion scenarios. In addition, such analytically designed LDLAs also have the advantage of array robustness (or system reliability) in the sense of$u$–$v$coverage shrinking and “hole” occurrence (resulted from sensor failures). Numerical results are given to demonstrate the effectiveness of the proposed LDLA design method through comparison with stochastic algorithms based on heuristic search and combinatorial approaches uniting specific integer sequences, e.g., cyclic difference sets.

To monitor sea surface salinity (SSS) in coastal zones with high temporal and spatial resolution, an L-band phased array radiometer is developed. However, it is well known that the L-band radiometers suffer from serious radio frequency interference (RFI) effects. To mitigate the RFI effects, an RFI detection and mitigation algorithm is proposed for the L-band phased array radiometer. The experiments in coastal zones are performed to assess the performance of the RFI algorithm. The results indicate that the detection and mitigation algorithm exhibits good performance and can effectively mitigate the RFI effects.

Ship detection is important for a wide range of applications. In this article, an airborne passive interferometric microwave sensor (PIMS) is proposed as a powerful complementary tool for ship detection. The fundamental of ship detection by the airborne PIMS is introduced. A specification called “the detection factor” is addressed to quantitatively assess the performance of the airborne PIMS for ship detection. The detection probability is discussed and addressed based on the constant false-alarm rate (CFAR). Numerical simulations are performed to demonstrate the feasibility of ship detection by the airborne PIMS. The impacts of the atmosphere and the sea wind on the performance of the airborne PIMS for ship detection are also addressed in terms of theoretical analyses and numerical simulations. The airborne experiments are also carried out by an X-band PIMS to verify the practicability of ship detection by the airborne PIMS in a sunny weather and a cloudy weather. The data processing procedure and a ship detection algorithm in the experiments are introduced. The airborne experimental results reveal that the ships can be effectively detected and tracked by the airborne X-band PIMS in a sunny weather and a cloudy weather. It demonstrates the practicability of ship detection by the airborne PIMS. The ship detection probability is also analyzed, which agrees well with the theoretical detection probability.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer working at L and X-band with full polarization measurement for obtaining high precise measurement of sea surface salinity. FPASMR consists of dual Y-shaped arrays containing 23 L-band antennas per arm plus one in the center in L-band array and the same numbers of X-band antennas in X-band array. The demonstrator prototype of FPASMR has been developed and the validation has also been fulfilled.

In this paper, a L-band phased array radiometer for sea surface salinity is developed. To reduce the mass and volume of real-aperture microwave radiometers, the radiometer employs a phased array and a receiver channel, which can observe target scenes with electric scanning. The L-band phased radiometer is analyzed using noise waves. The calibration method of the radiometer is introduced. A Radio Frequency Interference (RFI) detected algorithm is also given. A series of experiment is performed and the present results show that this phased array radiometer exhibits good performance in some beams.

This paper briefly discusses the working principle of synthetic aperture microwave radiometer system. Through the system sensitivity and repeatability test, The sensitivity and repeatability of the system are obtained. Based on the test, a correction algorithm for calibration error of calibration network sensitivity and receiver repeatability is proposed, which can be used to calibrate the data in different environments on the satellite and provide the basis for the future onboard calibration.

For Soil Moisture And Ocean Salinity (SMOS) data, significant errors exist near coastal areas because of the contamination by the nearby land. For analyzing the origin of the land contamination, in this letter, it is found, from the global map of SMOS data error, that although the SMOS data error due to land contamination near coasts varies in different regions, the characteristic that the error increases significantly in proximity to land is similar in global scope. The detailed analysis of SMOS brightness temperature (TB) error in the selected area reveals a decreasing trend with the increase of the distance to coast (DC). An SMOS TB measurement model according to geophysical parameters of the selected area and SMOS/microwave imaging radiometer aperture synthesis antenna array is established to analyze the origin of the TB error variation due to land contamination. The simulation results, which agree with the SMOS TB data analysis, show that within 40 km of DC, TB error is large and decreases sharply from ~60 to ~4 K with the increase of DC, since the mainlobe of the antenna array is departing from land to ocean; during 40-400 km of DC, TB error decreases smoothly from ~4 to ~0.15 K, since the lower near sidelobes are observing and leaving land; TB error slightly decreases to 0 from 400 to 800 km when the lower far sidelobes leave land. The study of the origin of land contamination on ocean TB will be helpful for improving the observation of salinity near coastal areas.

In aperture synthesis radiometers (ASRs), the mutual coupling effect between antennas is a key problem, which degrades the performance of ASRs and should be compensated. In this paper, an improved compensation method is proposed for ASRs, which is based on the receiving mutual impedance. The proposed method is verified by a one-dimension 5-channel L-band ASR. The experimental results show that the proposed method is superior over the existing method based on the transmitting mutual impedance, and is more appropriate to compensate the mutual coupling effect for ASRs.

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.

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.