检索结果-内蒙古大学图书馆

Robust Estimation of Insect Morphological Parameters for Entomological Radar Using Multifrequency Echo Intensity- Independent Estimators

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Rui Wang, Jiangtao Li, Weidong Li, Muyang Zhang, Fan Hu, Cheng Beijing Inst Technol Radar Technol Res Inst Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Adv Technol Res Inst Jinan 250300 Peoples R China Beijing Inst Technol Key Lab Real Time Informat Proc Technol Embedded Beijing 100081 Peoples R China Beijing Inst Technol Radar Technol Res Inst Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Real Time Informat Proc Technol Embedded Beijing 100081 Peoples R China

Insect morphological parameters, including mass and length, are crucial for species identification. Entomological radar can estimate morphological parameters by establishing mappings from insect radar cross section (RCS) estimators to them. Current high-accuracy methods relying on absolute RCS estimators are sensitive to echo intensity. When applied to radars without angle measurement capability, these methods may underestimate morphological parameters. This underestimation arises from the inability of such radars to compensate for reduced echo intensity caused by insects deviating from the beam center. A method using single-frequency echo intensity-independent estimators (EIIEs) was attempted;however, it could only estimate the mass of insects below 200 mg with limited accuracy. This article explores the use of multifrequency EIIEs (MFEIIEs) to enhance the estimation of insect mass and length. Based on the multifrequency scattering dataset for 159 insects measured in an anechoic chamber, the insect multifrequency scattering matrix (SM) was studied. The study revealed that four EIIEs, including the amplitude ratio and phase difference of SM eigenvalues, and two relative RCS features related to the shape of the insect polarization pattern, were correlated with insect mass and length with varied correlations with frequency. Subsequently, morphological parameter estimation was achieved by establishing the mappings from MFEIIEs to mass and length using a random forest algorithm. The presented dataset demonstrated that this method was suitable for insects below 1000 mg. Finally, the method's effectiveness and robustness were demonstrated through field measurements on 160 insects, which yielded mean relative estimation errors of 21.82% for mass and 13.18% for length.

关键词： Insects Radar cross-sections Accuracy Estimation Scattering Length measurement Frequency estimation Echo intensity-independent estimator (EIIE) entomological radar insect morphological parameter estimation radar cross section (RCS)

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Performance Analysis of L-Band Geosynchronous SAR Imaging in the Presence of Ionospheric Scintillation (vol 55, pg 159, 2017)

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第1期55卷 615-615页

作者： Hu, Cheng Li, Yuanhao Dong, Xichao Wang, Rui Ao, Dongyang Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR.

关键词： Azimuth Geosynchronous synthetic aperture radar (GEO SAR) Image resolution imaging quality analysis Indexes Ionosphere ionospheric scintillation L-band Low earth orbit satellites SAR focusing Synthetic aperture radar

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Region-of-Interest Detection via Superpixel-to-Pixel Saliency Analysis for Remote Sensing Image

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2016年第12期13卷 1752-1756页

作者： Ma, Long Du, Bin Chen, He Soomro, Nouman Q. Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Mehran Univ Engn & Technol Dept Software Engn SZAB Campus Khairpur 66020 Pakistan

Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and are less efficient when applied on large high-resolution images. This letter presents an accurate and efficient approach via superpixel-to-pixel saliency analysis for ROI detection. At first, the image is downsampled and segmented into superpixels by simple linear iterative clustering. Next, structure tensor and background contrast are used to yield superpixel feature maps for texture and color. After fusing the feature maps, the overall superpixel saliency map is obtained and then used to achieve the final pixel-level saliency map by superpixel-to-pixel mapping. Through experimentations, we validate the effectiveness and computational efficiency of the proposed model in comparison with state-of-the-art techniques.

关键词： Background contrast region-of-interest (ROI) detection structure tensor superpixel segmentation

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Spatial Filtering Strategies on Deforestation Detection using SAR Image Textures

Spatial Filtering Strategies on Deforestation Detection usin...

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CIE International Conference on Radar (RADAR)

作者： Dong, Xichao Zhang, Dandan Cui, Kai Hu, Cheng Lv, Xin Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781509048281

For SAR images, the inherent speckles have to be suppressed first by employing spatial filtering. However, the spatial filtering is sure to smooth images and lose the textural information. The compromise has to be made between the speckle suppress and the texture extraction in order for a better deforestation detection. In this paper, the focus is to study effects of filtering strategies on the textural information extraction and assess the deforestation detection performance. Adaptive spatial filters are employed in estimating the texture information and constructing the textural change measures. The effects of different filtering strategies on image texture and detection are analyzed using L-band SAR data and the optimized estimation are proposed. The study recommends that the best detection performance appears when the texture map is estimated using a mean filter with a large window size and then the change map is constructed at the single pixel level.

关键词： SAR spatial filtering textural information deforestation

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Coupling Signal Suppression Method of Ground-based Synthetic Aperture Radar Based on Singular Value Decomposition

Coupling Signal Suppression Method of Ground-based Synthetic...

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CIE International Conference on Radar (RADAR)

作者： Yin, Lilei Tian, Weiming Wang, Zehai Zeng, Tao Mei, Maokui Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781509048281

In ground-based frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR) images, false targets caused by coupling signal may appear. Traditional digital cancellation methods cannot estimate reference cancellation signal in real-time. A new method based on singular value decomposition (SVD) to suppress radar coupling signal is put forward. In slow time coupling signal is changeless while echo signal is changing, with which the reference cancellation signal can be estimated by SVD method. However, if there are too many points in range and frames in azimuth, the SVD algorithm will be too complicated to process echo data. So echo matrix partition and sub-matrixes decomposition are used in SVD processing to reduce the algorithm cost of time and space. There is a coupling signal suppression experiment using the ground-based FMCW SAR system In this paper. The validity of new method has been verified.

关键词： SVD digital cancellation FMCW SAR matrix decomposition

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Image Enhancement Based on Lognormal Distribution in Ground-Based Synthetic Aperture Radar

Image Enhancement Based on Lognormal Distribution in Ground-...

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CIE International Conference on Radar (RADAR)

作者： He, Qian Hu, Cheng Guo, Shouchang Tian, Weiming Deng, Yunkai Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781509048281

Because of the widely dynamic range of ground targets' backscattering coefficients, original GB-SAR image has disadvantage of low contrast ration [1]. In order to enhance contrast, classical method and improved methods based on statistical property of GB-SAR images are studied. Finally, the feasibility and effectiveness of the improved methods are validated with experimental datasets.

关键词： ground-based SAR lognormal distribution contrast enhancement

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Radar HRRP Ground Target Recognition Using Slant 45° Dual Polarization

Radar HRRP Ground Target Recognition Using Slant 45° Dual P...

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IEEE Radar Conference (RadarConf)

作者： Zhang, Liang Li, Yang Wang, Yanhua Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781728116792

In this paper, the slant 45 degrees dual polarization mode, where the transmitter polarization is orientated at 45 degrees and the receivers are at 45 degrees and 135 degrees polarizations, is applied to radar high resolution range profile (HRRP) ground target recognition. The H/alpha decomposition is performed on the HRRP data as the feature extractor, and a modified H-alpha plane is subsequently applied for scattering mechanism classification. The experiment was carried out with both point and volume targets for verification in real environment, and preliminary results show that the data collected by this mode is able to distinguish typical scattering mechanisms, which are common in man-made targets, such as trihedral and dihedral scattering. Those promising results demonstrate potentials of this mode in radar HRRP ground target recognition.

关键词： radar polarimetry high resolution range profile (HRRP) scattering mechanism classification ground target recognition

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Dual-frequency Motion Compensation for SISAR Imaging in Forward Scatter Radar 2

Dual-frequency Motion Compensation for SISAR Imaging in Forw...

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2nd URSI Atlantic Radio Science Meeting (AT-RASC)

作者： Liu, Feifeng Liu, Changjiang Wang, Rui Hu, Cheng Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9789082598735

To solve the problem of motion compensation for accurate shadow inverse synthetic aperture radar (SISAR) imaging, an SISAR motion compensation algorithm based on dual-frequency is proposed. By conjugate multiplication of two echo signals with different carrier frequencies and different sampling rates, the influence of the scattering phase can be compensated and Doppler phase shift can be obtained by resolving recursive equations. Further, the Doppler variation rate at the baseline-crossing time is used as the initial value to solve the recursive equations. The simulation results show that this algorithm can give a more accurate SISAR contour image, the resolution of which has been greatly improved compared to that obtained by traditional SISAR motion compensation methods.

关键词： Imaging Motion compensation Radar imaging Scattering Doppler effect Atmospheric modeling Image resolution

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Multi-Target Position and Velocity Estimation Using OFDM Communication Signals

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第2期68卷 1160-1174页

作者： Li, Yinchuan Wang, Xiaodong Ding, Zegang Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Columbia Univ Elect Engn Dept New York NY 10027 USA

In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinated and un-synchronizated illuminator and multiple receivers. It is assumed that data demodulation is performed separately based on the direct-path signal, and the error-prone estimated data symbols are made available to the passive radar receivers, which estimate the positions and velocities of the targets in two stages. First, we formulate a problem of joint estimation of the delay-Doppler of reflectors and the demodulation errors, by exploiting two types of sparsities of the system, namely, the numbers of reflectors (i.e., targets and clutters) and demodulation errors are both small. This problem is non-convex and a conjugate gradient descent method is proposed to solve it. Then in the second stage we determine the positions and velocities of targets based on the estimated delay-Doppler in the first stage. For the second stage, two methods are proposed: the first is based on numerically solving a set of nonlinear equations, while the second is based on the neural network, which is more efficient. The performance of the proposed algorithms is evaluated through extensive simulations.

关键词： Passive radar Receivers Demodulation OFDM Estimation Clutter Localization velocity estimation OFDM passive radar super-resolution non-convex conjugate gradient descent atomic norm neural network off-grid sparsity

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SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第12期59卷 10197-10212页

作者： Wei, Yangkai Li, Yinchuan Ding, Zegang Wang, Yan Zeng, Tao Long, Teng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Chongqing Innovat Ctr Beijing Inst Technol Chongqing 401331 Peoples R China

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.

关键词： Synthetic aperture radar Radar polarimetry Imaging Scattering Radar imaging Image reconstruction Radar Deep neural network (DNN) line-segment-scatterer (LSS) multicomponent alternating direction method of multipliers (MC-ADMM) parametric super-resolution (SR) imaging network (PSRI-Net) rectangular-plate-scatterer (RPS) SR image reconstruction synthetic aperture radar (SAR)

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