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The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas. [ABSTRACT FROM AUTHOR]

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc. [ABSTRACT FROM AUTHOR]

Shortwave infrared polarization imaging can increase the contrast of the target to the background to improve the detection system's recognition ability. The division of focal plane polarization indium gallium arsenide (InGaAs) focal plane array (FPA) detector is the ideal choice due to the advantages of compact structure, real-time imaging, and high stability. However, because of the mismatch between nanostructures and photosensitive pixels as well as the crosstalk among the different polarization directions, the currently reported extinction ratio (ER) of superpixel-polarization-integrated detectors cannot meet the needs of high-quality imaging. In this paper, a 1024 × 4 InGaAs FPA detector on-chip integrated with a linear polarization grating (LPG) was realized and tested. The detector displayed good performance throughout the 0.9–1.7 um band, and the ERs at 1064 nm, 1310 nm and 1550 nm reached up to 22:1, 29:1 and 46:1, respectively. For the crosstalk investigation, the optical simulation of the grating-integrated InGaAs pixel was carried out, and the limitation of the ER was calculated. The result showed that the scattering of incident light in the InP substrate led to the crosstalk. Moreover, the deviation of the actual grating morphology from the designed structure caused a further reduction in the ER. [ABSTRACT FROM AUTHOR]

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc.

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

In this paper, the scattering characteristics of polarized light are studied based on the microsurface element theory, and the Polarization Bi-directional Reflection Distribution Function (pBRDF) model applicable to rough surfaces is established according to the three-component model, and the expression of polarization of reflected light from the target surface is derived based on the model, and the accuracy of the model is verified by comparing the model with the conventional model. The modeling is also carried out from the scattering perspective, and the relationship between different artificial target particle diameters and polarization characteristics is analyzed. The results show that the Degree Of Polarization (DOP) is strongly related to the detection zenith angle. The DOP of the target is largest in the direction of the mirror reflection of the source. The polarization characteristics of different materials are different, and the relative error of the established model is significantly smaller than that of the conventional model. And the scattering model can make preliminary classification of different man-made targets according to different particle diameters, which can provide theoretical support for the analysis of the polarization characteristics of the target surface.

Shortwave infrared polarization imaging can increase the contrast of the target to the background to improve the detection system’s recognition ability. The division of focal plane polarization indium gallium arsenide (InGaAs) focal plane array (FPA) detector is the ideal choice due to the advantages of compact structure, real-time imaging, and high stability. However, because of the mismatch between nanostructures and photosensitive pixels as well as the crosstalk among the different polarization directions, the currently reported extinction ratio (ER) of superpixel-polarization-integrated detectors cannot meet the needs of high-quality imaging. In this paper, a 1024 × 4 InGaAs FPA detector on-chip integrated with a linear polarization grating (LPG) was realized and tested. The detector displayed good performance throughout the 0.9–1.7 um band, and the ERs at 1064 nm, 1310 nm and 1550 nm reached up to 22:1, 29:1 and 46:1, respectively. For the crosstalk investigation, the optical simulation of the grating-integrated InGaAs pixel was carried out, and the limitation of the ER was calculated. The result showed that the scattering of incident light in the InP substrate led to the crosstalk. Moreover, the deviation of the actual grating morphology from the designed structure caused a further reduction in the ER.

This article investigates regularized estimation of Kronecker-structured covariance matrices (CMs) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex elliptically symmetric (CES) distributions with a Kronecker-structured CM. To obtain a well-conditioned estimate of the CM, we add penalty terms of Kullback–Leibler divergence to the negative log-likelihood function of the associated complex angular Gaussian (CAG) distribution. This is shown to be equivalent to regularizing Tyler’s fixed-point equations by shrinkage. A sufficient condition that the solution exists is discussed. An iterative algorithm is applied to solve the resulting fixed-point iterations, and its convergence is proven. In order to solve the critical problem of tuning the shrinkage factors, we then introduce two methods by exploiting oracle approximating shrinkage (OAS) and cross-validation (CV). The proposed estimator, referred to as the robust shrinkage Kronecker estimator (RSKE), is shown to achieve better performance compared with several existing methods when the training samples are limited. Simulations are conducted for validating the RSKE and demonstrating its high performance by using the IPIX 1998 real sea data. [ABSTRACT FROM AUTHOR]