Polarimetric Detection Research Articles

Polarization-space-time domain adaptive detection for the heterogeneous array

In this paper, to investigate the effect of different antenna attitudes on the problem of polarimetric target detection, we take the heterogeneous array (HA), which is a uniform linear array (ULA) composed of antennas with different attitudes, as the research object, and use the generalized likelihood ratio test (GLRT) criterion to derive an adaptive detection algorithm. Meanwhile, the probability of detection, as well as the probability of false alarm of this algorithm is presented. Through numerical simulations, we analyze the detection ability of the proposed algorithm in various scenarios, and the detection performance of the HA is compared with the ULA and the uniform linear array consisting of the orthogonal pairs of dipoles (ULAP). The simulation results indicate that the HA can improve its detection performance by exploiting the information in the polarization domain, and therefore, the polarimetric target detection ability of the array, which is composed of antennas with different attitudes, is proved.

Polarization-Resolved THz Imaging With Orthogonal Heterostructure Backward Diode Detectors

A fully integrated lens-coupled dual-polarization detector for imaging was designed, fabricated, and characterized in the terahertz (THz) region. Two zero-bias heterostructure backward diodes (HBDs) were monolithically integrated into a planar dual-polarization annular-slot antenna. An impedance matching network consisting of an interdigitated DC block and shorted stubs was implemented for maximum power transfer. The fabricated chip was then mounted on an extended hemispherical silicon lens for high antenna efficiency. The performance of the integrated detector has been characterized at 200 GHz. The detector module can simultaneously measure the two orthogonal linear polarized components of the incident THz waves, and hence obtain the polarization direction. On the basis of this polarimetric detection, polarization-resolved imaging of a Gaussian beam, as well as birefringent samples, was performed using the integrated detectors. The angular resolution of the polarization detection has been demonstrated to be as small as 3°. The single-pixel detector demonstrated here is amenable to array applications for THz focal-plane arrays that will have a significant impact on a wide range of remote sensing, though-barrier imaging, and detection/identification applications.

An Advanced Data Processing Algorithm for Extraction of Polarimetric Radar Signatures of Moving Automotive Vehicles Using the H/A/α Decomposition Technique

A dedicated signal and data processing chain is proposed for a fully polarimetric Doppler surveillance S-band radar to extract the polarimetric signatures of moving targets. To extract the target’s polarimetric features, detection, clustering, and tracking steps are realized for a multi-target environment in the range-Doppler domain. A dedicated data fusion method for all four polarimetric radar channel signals is implemented to take full advantage of the additional polarimetric information and improve the detection performance. While tracking each particular target, polarization information is collected and used to describe their polarization scattering characteristics. Using the polarimetric H/A/α decomposition technique, the polarimetric features of moving automotive targets are extracted and investigated. The developed processing chain has been applied to the signals scattered from vehicles moving in a highway. By employing both time averaging and spatial averaging of the statistical coherency matrix, the polarimetric signatures of both moving vehicles and static clutter have been presented in the two-dimensional H/α plane. It has been found that the spatial averaging approach results in polarimetric signatures of moving vehicles that give the opportunity to directly and without consideration of the motion of the targets compare the polarization features of moving targets and static clutter. Therefore, this method can be used to improve the performance of target detection or target classification.

Study of structural effects on the polarization characteristics of subwavelength metallic gratings in short infrared wavelengths.

Applications of subwavelength grating based-polarizers for polarimetric detections are being hindered due to the limited extinction ratio. In this work, the structural effect, including the line edge roughness (LER), of the gratings on the polarizing characteristics was studied by both numerical simulations using finite difference and time domain (FDTD) method and experiments, aiming to figure out the optimal grating profile for achieving high transmittance as well as high extinction ratio. Two different configurations of the gratings, one is dual layer Au lines and the other is parabolic shaped Al lines on structured spin-on-carbon (SOC) films were systematically studied and compared. Nanofabrication of the gratings by electron beam lithography without lift-off process were conducted and optical measurements of their polarization properties demonstrate superior performance of the developed polarizers. The origin of the structural effect was explained by the local surface plasmonic modes, existing in the nano-slits in metallic gratings, which is instructive for further enhancement of the polarization performance.

Polarimetric SAR Ship Detection Based on Scattering Characteristics

A synthetic aperture radar is crucial for detecting ships. In this study, to improve the detection capability of weakly scattering targets and reduce false alarms, a detector <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{vh}$ </tex-math></inline-formula> based on the scattering characteristics of ships is proposed after an in-depth study of the differences between ship and sea surface scattering mechanisms. The volume scattering mechanism and the helix scattering mechanism are fused, and the volume scattering power is used to describe the scattering structure on the ship, and the helix scattering power is used to increase the difference between the ship and the sea surface. We test on 4 GF-3 fully polarized synthetic aperture radar data. The experimental results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{vh}$ </tex-math></inline-formula> has improved detection capability and can successfully minimize false alarm generation.

Sea-Crossing Bridge Detection in Polarimetric SAR Images Based on Windowed Level Set Segmentation and Polarization Parameter Discrimination

As sea-crossing bridges are important hubs connecting separated land areas, their detection in SAR images is of great significance. However, under complex scenarios, the sea surface conditions, the distribution of coastal terrain morphologies, and the scattering components of different structures in the bridge area are very complex and diverse, which makes the accurate and robust detection of sea-crossing bridges difficult, including the sea–land segmentation and bridge feature extraction on which the detection depends. In this paper, we propose a polarimetric SAR image detection method for sea-crossing bridges based on windowed level set segmentation and polarization parameter discrimination. Firstly, the sea and land are segmented by a proposed windowed level set segmentation method, which replaces the construction of the level set segmentation energy function based on the isolated pixel distribution with a joint distribution of pixels in a certain window region. Secondly, water regions of interest are extracted by a proposed water region merging algorithm combining the distances of the water contour and polarization similarity parameter. Finally, the bridge regions of interest (ROIs) are extracted by merging close water contours, and the ROIs are discriminated by the polarimetric parameters of the polarization entropy and scattering angle. Experimental results using multiple AirSAR, RADARSAT-2, and TerraSAR-X quad-polarization SAR data from the coastal areas of San Francisco in the USA, Singapore, and Fuzhou, Fujian, and Zhanjiang, Guangdong, in China show that the proposed method can achieve 100% detection of sea-crossing bridges in different bands for different scenes, and the accuracy of the intersection of the ground-truth (IoG) index of bridge body recognition can reach more than 85%. The proposed method can improve the detection rate and reduce the false alarm rate compared with the traditional spatial-based method.

Analysis of the performance of a polarized LiDAR imager in fog.

This paper focuses on exploring ways to improve the performance of LiDAR imagers through fog. One of the known weaknesses of LiDAR technology is the lack of tolerance to adverse environmental conditions, such as the presence of fog, which hampers the future development of LiDAR in several markets. Within this paper, a LiDAR unit is designed and constructed to be able to apply temporal and polarimetric discrimination for detecting the number of signal photons received with detailed control of its temporal and spatial distribution under co-polarized and cross-polarized configurations. The system is evaluated using different experiments in a macro-scale fog chamber under controlled fog conditions. Using the complete digitization of the acquired signals, we analyze the natural light media response, to see that due to its characteristics it could be directly filtered out. Moreover, we confirm that there exists a polarization memory effect, which, by using a polarimetric cross-configuration detector, allows improvement of object detection in point clouds. These results are useful for applications related to computer vision, in fields like autonomous vehicles or outdoor surveillance where many variable types of environmental conditions may be present.

Weyl semimetal integrated three-unit polarimeters

The unique topology of Weyl semimetals’ band structure has been recently shown to lead to a host of novel optoelectronic properties. Among them is the prospect of polarization-dependent photocurrents, most notably the circular photogalvanic effect arising from the spin texture of the Weyl cones in the presence of symmetry breaking. Here we show that these helicity-dependent photocurrent processes can be employed to realize fully integrated polarimetric detection systems. In this respect, a TaAs-based polarimeter is demonstrated involving three pixels that can uniquely map the polarization state of light on the Poincaré sphere. Our work could enable a class of optoelectronic devices that directly respond to the polarization of incident light, while paving the way toward a better understating of light–matter interactions in Weyl semimetals.

Level-crossing resonances on open atomic transitions in a buffered Cs vapor cell: Linewidth narrowing, high contrast, and atomic magnetometry applications

The ground-state Hanle effect (GSHE) in alkali-metal atomic vapors using a single circularly polarized wave underlies one of the most robust and simplest techniques in atomic magnetometry. This effect causes a narrow (subnatural-width) resonance in the light wave intensity transmitted through a vapor cell. Usually, GSHE-based sensors operate in the spin-exchange-relaxation-free (SERF) regime. However, this regime requires a relatively high temperature of vapors (150 C or higher), leading to a relatively large heat release and power consumption of the sensor head. Besides, without applying special measures, SERF regime significantly limits a dynamic range of measurements. Here, we study a pump-probe scheme involving a single elliptically polarized wave and a polarimetric detection technique. The wave is in resonance with two adjacent optical transitions in the cesium D1 line (894.6 nm) owing to their overlapping in presence of a buffer gas (Ne, 130 Torr). Using a small (0.1 cm$^3$) glass vapor cell, we demonstrate a possibility of observing subnatural-width resonances with a high contrast-to-width ratio (up to 45 %/mG) under a low-temperature (60 C) regime of operation thanks to a strong light-induced circular dichroism. Basing on a $\Lambda$ scheme of atomic energy levels, we obtain explicit analytical expressions for the line shape. The model reveals a linewidth narrowing effect due to openness of the scheme. This result is unusual for magneto-optical atomic spectroscopy because the openness is commonly considered as a undesirable effect, degrading the resonance characteristics. We estimate a sensitivity of 1.8 pT/$\surd$Hz with a 60 fT/$\surd$Hz sensitivity in the photon-shot-noise limit. The results contribute to the theory of GSHE resonances and can be applied to development of a low-temperature high-sensitivity miniaturized magnetic field sensor with an extended dynamic range.

Spectral filter in short-wave infrareds based on a metasurface on silicon-on-insulator with polarizing bandpass.

Spectral filters with polarimetric character in short-wave infrareds are urgently needed because of their broad applications in optic-fiber communications, polarimetric detections, and imaging. Based on our earlier progress in developing polarimetric devices in infrared wavelengths, in this work, a plasmonic-metasurface-based polarization-dependency multi-channel narrowband filter in short-wave infrareds was developed. To meet the requirement by the developing trend of polarimetric detection/spectral imaging in short-wave infrareds, a resonant cavity in the form of the Au hat/elliptical Si/SiO2 pillars/Au layer as the filters was proposed. Numerical simulations by finite-difference time-domain (FDTD) show resonant and polarized transmissions of the designed devices to infrared light in short wavelengths, and the peak positions are relevant to the structural dimensions. Optical characteristics of the filters, fabricated by electron beam lithography/dry-etch technique, agree well with the simulated behavior. To enhance the transmission efficiency to the applicable level, nanoprocessing of the filters still needs to be optimized. Nevertheless, the progress reported is promising for this new type of spectral filters based on modern metasurfaces.

Fabrication and performance analysis of infrared InGaAs polarimetric detector with complete coverage of superpixel-structured grating

Polarization detection is widely used in various fields due to its unique advantages in increasing the dimensionality of information detected effectively. Here, we analyzed the effects of signal crosstalk among pixels and grating morphology on the polarization performance of SWIR InGaAs detector integrated with superpixel-structured subwavelength aluminum grating by test and FDTD simulation. Based on the analysis results, a superpixel-structured subwavelength aluminum grating was integrated on InGaAs FPA to realize the full coverage of grating on a 320 × 256 SWIR InGaAs detector. The detector has a responsivity non-uniformity of less than 6%. The pixel operability is better than 99.99% and peak detectivity reaches 5.99 × 1011 cm·Hz1/2/W at room temperature. An extinction ratio of up to 18:1 at 1064 nm is realized.

A General Framework of Polarimetric Detectors Based on Quadratic Optimization

Ship detection is an important task in civil or military applications and we can use polarimetric synthetic aperture radar (PolSAR). Many polarimetric detectors were proposed and achieved good performances in particular environments, such as optimal polarimetric detector (OPD), polarimetric whitening filter (PWF), polarimetric notch filter (PNF), polarimetric detection optimization filter (PDOF) and diagonal loading detector (DLD) etc. Up to know, the analytical links among different polarimetric detectors have not been found. In this work, the above polarimetric detectors are unified in mathematical forms and a general framework of polarimetric detectors based on quadratic optimization is presented. The mathematical forms are summarized as a trace of two matrices’ product. One is a detection transformation matrix and the other is the polarimetric covariance matrix of the pixel to be detected. We find that all these polarimetric detectors can be regarded as the optimization of such detection matrix, which is the key point of the general framework, and the difficulty turns to be a linear inseparable problem. Pocket Perceptron Linear Algorithm (PPLA) is used to solve the linear inseparable problem. In the case of low resolution, target detection is almost an indivisible problem, and multilayer perceptron (MLP) cannot provide better detection results than PPLA. In the case of high resolution, target detection becomes a nonlinear separable problem, and MLP is gradually superior to PPLA. Additionally, the optimal weights of the recent DLD are obtained to compare with other detectors in the general framework and the DLD is developed to a more general case (GDLD). The experiments validate the general framework of polarimetric detectors. Different detectors in the general framework are utilized and compared in both simulated and measured PolSAR data. The results show the optimal solution in the general framework can always reach the best performance, and the GDLD is the closest one to the optimal detector of the general framework.

Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed, which performs well in most environments. In this study, a novel subspace form of the PDOF [strict PDOF (SPDOF)] was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed for the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target-to-clutter ratio (TCR) case.

Persymmetric Adaptive Target Detection With Dual-Polarization in Compound Gaussian Sea Clutter With Inverse Gamma Texture

This paper investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then the proposed detectors are proved to have the constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

GLRT-Based Polarimetric Detection in Compound-Gaussian Sea Clutter With Inverse-Gaussian Texture

This letter presents the derivation of the generalized likelihood ratio test (GLRT)-based polarimetric detector in the non-Gaussian clutter. We model the non-Gaussian sea clutter as compound-Gaussian distribution with inverse-Gaussian texture (IG-CG), which has better goodness-of-fit for the high-resolution sea clutter. Based on the two-step GLRT criterion, we develop the test statistic of the proposed detector by assuming the texture component is known in the first step. In the second step, with the texture of the secondary data estimated as the power of the clutter, we insert the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimate (MAPE) of the texture of the primary data into the test statistic to achieve the fully adaptive detection. Furthermore, the model-based polarimetric detector, which exploits the independence between the co-polarized and the cross-polarized component, is derived. Finally, we conduct experiments with simulated clutter and real sea clutter to evaluate the performance of the proposed detector. The simulation results verify that the proposed detectors have better detection performance and the MAPE under different hypotheses leads to different levels of robustness to the mismatched signal.

Polarimetric Target Detection in Compound Gaussian Sea Clutter With Inverse Gaussian Texture

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.

The Polarimetric Detection Optimization Filter and its Statistical Test for Ship Detection

Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{G}\Gamma \text{D}$ </tex-math></inline-formula> ) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed.

Improving the Temperature and Vibration Robustness of Fiber Optic Current Transformer Using Fiber Polarization Rotator

Fiber optic current transformers (FOCTs) have advantages over conventional current transformers. However, the application of FOCTs is significantly limited by the birefringence effect caused by temperature and vibration. Thus, this study proposed a sensing scheme of coupling a fiber polarization rotator to improve the robustness of FOCTs. First, the mathematical model of the proposed FOCT was derived using the Jones matrix, and the error caused by the birefringence effect was computed. Then, the FOCT was modeled by applying the finite element method to verify the effectiveness of the proposed FOCT in improving the temperature and vibration robustness. Finally, experiments were conducted, including temperature, vibration, and frequency response characteristics. The proposed scheme was compared with the basic polarimetric detection (PD) scheme and several state-of-the-art schemes. Results show that the maximum error of the proposed scheme is 1.7% in the temperature range from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 20 ^{\circ }\text{C}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$80 ^{\circ }\text{C}$ </tex-math></inline-formula> (that of the basic PD scheme ranges from −7.05% to 9.75%). In the vibration test, the maximum output error of the proposed scheme is 0.72%, while that of the basic PD scheme reaches 4.89%. Meanwhile, the proposed FOCT meets the requirements of the measurement of high-amplitude and high-frequency transient currents, such as lightning impulse current, in power systems.

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized, and the sea&#x2013;land segmentation process to remove land false alarms is time-consuming. Regarding these problems, first, a single low-level path aggregation network is designed specifically for small targets. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Second, adaptive dual-polarimetric feature fusion is proposed to filter the multichannel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Third, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset containing small vessel detection and sea&#x2013;land segmentation labels is created with reference to the LS-SSDDv1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

The Orientation Estimation of Elongated Underground Objects via Multipolarization Aggregation and Selection Neural Network

The horizontal orientation angle and the vertical inclination angle of an elongated subsurface object are key parameters for object identification and imaging in ground-penetrating radar (GPR) applications. Conventional methods can only extract the horizontal orientation angle or estimate both angles in narrow ranges due to limited polarimetric information and detection capability. To address these issues, this letter, for the first time, explores the possibility of leveraging neural networks with multipolarimetric GPR data to estimate both angles of an elongated subsurface object in the entire spatial range. Based on the polarization-sensitive characteristic of an elongated object, we propose a multipolarization aggregation and selection network (MASNet), which takes the multipolarimetric radargrams as inputs, integrates their characteristics in the feature space, and selects discriminative features of reflected signal patterns for accurate orientation estimation. Numerical results show that our proposed MASNet achieves high estimation accuracy with an angle estimation error of less than 5°. The promising results obtained by the proposed method encourage one to think of new solutions for GPR-related tasks by integrating multipolarization information with deep learning techniques. The data and code implemented in the letter can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://haihan-sun.github.io/GPR.html</uri> .