Pathway detection holds extreme significance for fields such as aircraft landing, autonomous driving, and terrain mapping. Passive millimeter-wave (PMMW) imaging technology, with its all-weather operational capability and excellent penetration through fog, and clouds, has shown great potential in pathway detection. However, most existing studies focus on the brightness temperature (TB) differences for detection, which is highly susceptible to interference from environmental radiation. Polarization features can effectively characterize the object and its environmental properties. This paper proposes a physics-based pathway detection method that utilizes feature-level fusion technology, fusing the polarization features of the first two Stokes parametersTIandTQ, as well as the degree of linear polarization (DoLP) and the angle of polarization (AoP). By introducing spatial similarity (SS), the method effectively excludes interference from non-pathway areas and improves the detection accuracy. After confirming the horizon position as the vanishing point of the pathway, the detection results are fused with a region growing algorithm to generate the final pathway extraction output. Experimental results demonstrate that this method can distinguish between pathway and non-pathway regions and accurately detect various types of pathways, such as rivers, roads and seas. Quantitative analysis shows that, compared to existing methods, the proposed method has significant advantages in terms of detection accuracy and robustness.

The critical role of personnel security screening (PSS) devices in modern public transportation has driven a growing demand for real-time, lightweight, noninteractive, and high-performance screening systems. Currently, the predominant methods involve handheld metal scanners that require active cooperation by the subject, generally suffering from low efficiency, limited detection range, and blind spots for nonmetallic objects. Millimeter-wave-based PSS technology, featured by its speediness, harmlessness, and capability of detecting nonmetallic materials, represents an emerging trend in security screening. However, traditional millimeter-wave system-wide dynamic scanning schemes face challenges such as bulky size, high complexity, and poor integration. To address these problems, we develop a wide-angle metalens based on a neural-network-assisted end-to-end inverse design method, aiming to replace thick and heavy dielectric lenses in traditional millimeter-wave PSS systems, thereby enhancing system integration and lightweight capabilities while improving wide-angle scanning performance. As proof of concept, we propose a system-wide static noninteractive wide-angle dual-polarized meta-detector, which consists of a dynamic beam steering transmitter, dual-polarized receivers, and a data acquisition module. This meta-detector can rapidly, precisely, and statically identify concealed objects of various materials on the human body in a noninteractive manner within a 32° horizontal and 26.8° vertical field-of-view at an operating range of 15–25 m. The proposed PSS system offers long detection range, high integration, and noninteractive operation, highlighting its potential for practical deployment in real-world public security screening scenarios.

Acquiring diverse information (e.g., polarization, frequency) within a scene can significantly enhance the imaging system’s effectiveness and reliability. In this article, we develop a dual-frequency and dual-polarization passive millimeter-wave (PMMW) imager for outdoor imagery. The system is based on a focal plane array (FPA) with a$1.5\times 0.8$m parabolic reflector. A row of horizontally polarized W-band radiometers was placed on the focal plane alongside two rows of Ka-band radiometers with horizontal and vertical polarizations. Multiple scanning modes were employed to adapt to various application scenarios. An angle resolution of 0.4° (Ka) and 0.14° (W) is achieved with a 15° field of view, and the thermal sensitivity is about 0.5 K (W) and 0.3 K (Ka). The imager can complete a 360° azimuthal scan in 3 s and achieve full airspace imaging in 18 s. Several outdoor experiments are conducted, which include imaging of stationary targets such as outdoor buildings and trees, as well as moving targets involving commercial aircraft flying in the airspace. The experimental results demonstrate that the proposed imager can accurately capture both stationary and dynamic targets with frequency and polarization diversities.

The adoption of blockchain technology has catalyzed the expansion of decentralized finance (DeFi), leading to the harnessing of blockchain platforms. However, the decentralization of blockchain has given rise to blockchain extractable value (BEV) activities, influenced by consensus mechanisms. This study centers on BEV, unveiling a real-time discovery and mining system (RDMS) tailored for arbitrage-based DeFi activities. The system employs innovative methodologies for localized computation and execution. It establishes a comprehensive monitoring system for arbitrage and liquidation activities, contributing positively to the DeFi ecosystem. Leveraging round-the-clock on-chain data indexing and event-driven parsing methods, the RDMS enables automated and periodic analysis of BEV activities. This system provides valuable insights for BEV research, particularly in the context of arbitrage and liquidation activities. And we are able to consistently extract value using arbitrage strategies on blockchains, using RDMS that monitors the chain in real time and applies gas cost reduction mechanisms. Experimental testing and comparative analysis validate the RDMS’s effectiveness, showcasing minimal latency and remarkable gas optimization capabilities.

Object segmentation is an important issue in the field of passive millimeter-wave imaging remote sensing and detection. The brightness temperature (TB) difference of millimeter-wave radiation is usually used for the segmentation and detection of different target types. However, the target TB is affected by many factors such as dielectric constant, shape, physical temperature, and environmental radiation. In the actual scene, it is difficult to achieve effective object segmentation only depending on TB difference. In this paper, a physically-based method for object segmentation based on polarization random features is proposed. Through an in-depth analysis of the physical model and characteristics of the angle of polarization (AoP), the AoP statistical distributions of various polarized and non-polarized targets were given. We found that the AoP random feature is very sensitive to the polarization characteristics of the object, and its local statistical standard deviation can be used for polarized and unpolarized object segmentation. Two multi-polarization imaging experiments of complex scenes have verified the effectiveness of the proposed method. Compared with several methods based on polarization degree, the superior performance of the proposed method is qualitatively and quantitatively verified. Our work breaks through the inherent thinking that AoP is generally used for three-dimensional reconstruction, and opens up a new perspective of object segmentation based on AoP.

Passive millimeter-wave imaging is one of the most important approaches for personnel security inspection to detect concealed objects hidden under clothing. Many imaging systems have been constructed and tried out experimentally in various real scenarios. However, plenty of imaging results show that some hidden objects placed on the body edge areas may have low contrast and are difficult to detect. There are different contrasts in different polarization images. In this article, we propose a physically-based subregional polarization fusion method by modulating multipolarization information to enhance the contrast between edge objects and the body background. The physical model and polarization properties of edge objects are investigated by theoretical calculation and simulation analysis. By dividing the body area into four typical subregions based on the second and third Stokes components ($T_{Q}$and$T_{U}$), four linear polarization images are automatically fused to directly generate an enhancement image. Without complicated parameter adjustment, the proposed method can adaptively realize the global optimal contrast by fusing the local optimal regions of different polarization images at the pixel level. Simulation and measurement results demonstrate the effectiveness of the method. By comparing with three traditional fusion methods, differential signal noise ratio, receiver operating characteristic curve, and segmentation metric are utilized to quantitatively evaluate the superior performance of our fusion method.

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

Passive millimeter-wave (PMMW) imaging is one of the im-portant approaches for target detection with the background of the sea surface. The fluctuating sea waves and the reflections of surface vessels produce increased difficulties for ob-servers and processing algorithms to detect objects. In this paper, we explore the polarimetric properties of a typical sur-face vessel by PMMW imaging simulation. Stokes and polar-ization parameters have been calculated based on four linear polarization images. The simulated images have been dis-cussed qualitatively and quantitatively by using the contrast ratio (CR) between the vessel and the sea surface. The analy-sis results indicate that polarimetric imaging can improve the detection performance for surface vessels.

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.

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.

Passive millimeter-wave (PMMW) imaging has been used for several close-range applications such as personal security checks, scene monitoring, and so on. PMMW images contain a variety of types of image edges which represent intensity discontinuities. As a special edge, an object contour edge is an important feature for object detection and recognition, which denotes the external shape of the object. In this article, a physically based contour edge display method using adjustable linear polarization ratio (ALPR) by multipolarization imaging is proposed. Polarization brightness temperature (TB) models of the object and radiometer observation are built. According to the physical principle of edge generation, the ALPR properties of the contour edge are investigated. By fusing multipolarization information, the simple feature parameter ALPR is sensitive to the object contour edge. Then, the specific operation process of the proposed method based on multipolarization images is presented. Simulations and measurements of polarimetric imaging for personal security inspection scenes were conducted to verify the contour edge display performance. Finally, the applicability of the method is discussed and summarized.

Passive millimeter-wave and terahertz (MMW/THz) imaging is a significant technique used in personal security inspection and scene surveillance. However, a human body generally generates an obvious reflection on the floor, which may have negative influences on target detection and observer vision. In this article, we present a physically based method using dual-polarization feature and saliency information to solve the problem of reflection removal. The physical principle of a special dual-polarization phenomenon has been revealed by theoretical calculation and simulation analysis. By combining the second Stokes parameter (TQ) and modified linear polarization ratio (mLPR), a new feature parameter has been created to extract reflections of human bodies. By using a saliency analysis, the false positive alarms are eliminated completely. The experimental results demonstrate the robustness and effectiveness of the proposed method using various measured MMW/THz images.

Passive millimeter-wave/terahertz (PMMW/PTW) imaging has been widely developed for personal security screening in recent years. In PMMW/PTW images, object contours are an important feature for object detection and recognition. In this paper, a physical-based contour display method by fusing multi-polarization information is proposed. The polarization characteristics are investigated by analyzing the physical principle of contour edge generation. The feasibility of displaying contour edges is verified through simulation experiments for a typical security check scene. The presented method can enhance the contrast between concealed objects and human body, and then label contour edges.

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.

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.

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.