Yayun Cheng

Polarization-Based Method for Object Surface Orientation Information in Passive Millimeter-Wave Imaging

Surface orientation information is essential to depicting the 3-D structure of an object. This paper analyzes the linear polarization characteristics of several typical objects by simulations and measurements. Then, a polarization-based method is presented to acquire the object surface orientation information from three different linear polarization brightness temperature images by using a 94-GHz imaging radiometer. The experiments are conducted outside, where a wooden plate is placed obliquely on a metal box. Experimental results indicate that our method is capable of achieving the surface orientation information that is beneficial to recognizing the object. This method suggests possible applications for terrain models and object detections.

Polarization-based material classification technique using passive millimeter-wave polarimetric imagery

The polarization properties of thermal millimeter-wave emission capture inherent information of objects, e.g., material composition, shape, and surface features. In this paper, a polarization-based material-classification technique using passive millimeter-wave polarimetric imagery is presented. Linear polarization ratio (LPR) is created to be a new feature discriminator that is sensitive to material type and to remove the reflected ambient radiation effect. The LPR characteristics of several common natural and artificial materials are investigated by theoretical and experimental analysis. Based on a priori information about LPR characteristics, the optimal range of incident angle and the classification criterion are discussed. Simulation and measurement results indicate that the presented classification technique is effective for distinguishing between metals and dielectrics. This technique suggests possible applications for outdoor metal target detection in open scenes.

RFI Mitigation in Aperture Synthesis Radiometers Using a Modified CLEAN Algorithm

For aperture synthesis radiometers, sparse samplings on the

Super-resolution RFI localization with compressive sensing in synthetic aperture interferometric radiometers

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An equivalent method of mixed dielectric constant in passive microwave/millimeter radiometric measurement

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RFI localization in synthetic aperture interferometric radiometers based on sparse Bayesian inference

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Influence of surface roughness on polarization property in passive millimeter-wave imaging

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 interferences impact. Based on the similar idea, in this letter, we describe an algorithm to compensate for the interferences 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.

Wavelet-based 1/f-Noise elimination in millimeter-wave radiometer

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.

Passive millimeter-wave scene imaging simulation based on fast ray-tracing

Dielectric constant is an important role to describe the properties of matter. This paper proposes This paper proposes the concept of mixed dielectric constant(MDC) in passive microwave radiometric measurement. In addition, a MDC inversion method is come up, Ratio of Angle-Polarization Difference(RAPD) is utilized in this method. The MDC of several materials are investigated using RAPD. Brightness temperatures(TBs) which calculated by MDC and original dielectric constant are compared. Random errors are added to the simulation to test the robustness of the algorithm. Keywords: Passive detection, microwave/millimeter, radiometric measurement, ratio of angle-polarization difference (RAPD), mixed dielectric constant (MDC), brightness temperatures, remote sensing, target recognition.

Polarization ratio property and material classification method in passive millimeter wave polarimetric imaging

ABSTRACT The presence of radio frequency interference (RFI) sources emitting in the L-band, which is reserved for passive measurements by International Telecommunications Union (ITU) regulations, has seriously deteriorated the data quality of many brightness temperature (BT) snapshots in the Soil Moisture and Ocean Salinity (SMOS) project. In order to obviate the Gibbs-like contamination on the BT maps, one effective way is to locate the positions of RFI sources and switch them off. This article discusses a new method for RFI localization that is tailored to the scenario of synthetic aperture interferometric radiometry. The novel aspect lies in addressing the problem of RFI localization from a probabilistic viewpoint. By introducing the sparsity of RFI distribution in the spatial domain as a priori knowledge, we have employed the sparse Bayesian inference (SBI) strategy to estimate the locations of RFI sources. In addition, we have also tested the proposed method using numerical simulations and actual SMOS data. The results indicate that the proposed method has advantages in both accuracy and resolution of RFI source localization over the conventional direction-of-arrival (DOA) methods used in the beamforming technique.