Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Radar Imaging Theory, Techniques, and Applications
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Radar Imaging Theory, Techniques, and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 109014

Special Issue Editors

Prof. Dr. Kun-Shan Chen

E-Mail Website
Guest Editor
Institute of Space Earth Science, Nanjing University, Suzhou 215163, China
Interests: wave scattering; radar imaging of terrains and sea; the applications of radar image
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saibun Tjuatja

E-Mail Website
Guest Editor
Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
Interests: radar imaging; subsurface sensing and imaging; scattering and emission from random media
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaofeng Li

E-Mail Website
Guest Editor
NCWCP - E/RA3, 5830 University Research Court, College Park, MD 20740, USA
Interests: AI oceanography; big data; ocean remote sensing; physical oceanography; boundary layer meteorology; synthetic aperture radar imaging mechanism; multiple-polarization radar applications; satellite image classification and segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been an explosive growth in interest in high spatial resolution radar imaging of natural and manmade targets, from ground-based to spaceborne. Advances of theoretical research in stochastic electromagnetic scattering and modern signal processing techniques have been progressively made at a swift pace. Radar images, an imprint of the wave scattering process in the presence of targets, be them finite or extent, have found applications in many areas from remote sensing of terrain and sea to medical imaging. Electromagnetic wave-target interactions and the image formation are physically connected, so that certain critical issues regarding image sensitivity and information content, uniqueness, accuracy, and resolution will be properly addressed. This Special Issue focuses on reporting new imaging theory and novel techniques, and their interplay, in a wide frequency region, and, perhaps, equally imperative, on better usage of images and new applications. The issue covers a broad, but comprehensive, treatment of subjects closely-related to the imaging of objects to random media by means of theory modeling, numerical simulation, and laboratory measurements. Papers on applications to interpret and thus to exploit the target features, and all the way to detect, to identify, and perhaps to recognize the targets of interest, with aids of deep learning algorithms are welcome. Pioneering works from internationally-recognized experts are invited to this well-focused issue. Contributions are welcome for the following topics (but are not limited to them):

  • Scattering theory related to radar imaging
  • Approximations and numerical techniques
  • Active and passive imaging techniques
  • Image formation, focusing, and enhancement
  • Advanced feature extraction techniques
  • Information content and image quality assessment
  • Novel algorithms for target detection, classification, identification, and recognition

We look forward to your contributions.

Prof. Dr. Kun-Shan Chen
Prof. Dr. Saibun Tjuatja
Dr. Xiaofeng Li

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Radar imaging
  • Electromagnetic scattering
  • Image focusing, enhancement
  • High resolution image
  • Feature extraction
  • Detection, classification Identification, Recognition
  • Deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (18 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 6496 KiB  
Article
Surface Parameters Retrieval from Fully Bistatic Radar Scattering Data
by Ying Yang, Kun-Shan Chen and Guofei Shang
Remote Sens. 2019, 11(5), 596; https://doi.org/10.3390/rs11050596 - 12 Mar 2019
Cited by 6 | Viewed by 7910
Abstract
Fully bistatic radar scattering from rough surfaces is of vital importance in terrain remote sensing, but results in bulky data volume. The scattering is dependent on physical parameters of the media and is controlled by the radar observation geometry. Together, the two sets [...] Read more.
Fully bistatic radar scattering from rough surfaces is of vital importance in terrain remote sensing, but results in bulky data volume. The scattering is dependent on physical parameters of the media and is controlled by the radar observation geometry. Together, the two sets of parameters determine the scattering patterns in a bistatic plane confined by incident and polar angles in both incident and scattering directions. For radar remote sensing, it is desirable to infer surface parameters of interest, with satisfactory accuracy, from large volumes of measured data sets. This is essentially a task of data mining. In this paper, we present model-generated bistatic radar scattering data, followed by a sensitivity analysis, to identify a suitable configuration in terms of parameter inversion from fully bistatic measurements by a Kalman filter-trained dynamic learning neural network (DLNN). Results indicate that with bistatic observation, superior retrieval performance (as compared to backscattering observation) can be readily achieved. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

16 pages, 8140 KiB  
Article
Modulation of Dual-Polarized X-Band Radar Backscatter Due to Long Wind Waves
by Irina A. Sergievskaya, Stanislav A. Ermakov, Alexey V. Ermoshkin, Ivan A. Kapustin, Alexander A. Molkov, Olga A. Danilicheva and Olga V. Shomina
Remote Sens. 2019, 11(4), 423; https://doi.org/10.3390/rs11040423 - 19 Feb 2019
Cited by 24 | Viewed by 3884
Abstract
Investigation of microwave scattering mechanisms is extremely important for developing methods for ocean remote sensing. Recent studies have shown that a common two-scale scattering model accounting for resonance (Bragg) scattering has some drawbacks, in particular it often overestimates the vertical-to-horizontal polarization radar return [...] Read more.
Investigation of microwave scattering mechanisms is extremely important for developing methods for ocean remote sensing. Recent studies have shown that a common two-scale scattering model accounting for resonance (Bragg) scattering has some drawbacks, in particular it often overestimates the vertical-to-horizontal polarization radar return ratio and underestimates the radar Doppler shifts if the latter are assumed as associated with quasi linear resonance surface waves. It is supposed nowadays that radar backscattering at moderate incidence angles is determined not only by resonance Bragg mechanism but also contains non polarized (non Bragg) component which is associated supposedly with wave breaking but which is still insufficiently studied. Better understanding of the scattering mechanisms can be achieved when studying variations of radar return due to long wind waves. In this paper, results of experiments from an Oceanographic Platform on the Black Sea using dual co-polarized X-band scatterometers working at moderate incidence are presented and variations of Bragg and non-Bragg components (BC and NBC, respectively) and radar Doppler shifts are analysed. It is established that BC and NBC are non-uniformly distributed over profile of dominant (decametre-scale) wind waves (DWW). Variations of BC are characterized by some “background” return weakly modulated with the dominant wind wave periods, while NBC is determined mostly by rare and strong spikes occurred near the crests of the most intense individual waves in groups of DWW. We hypothesize that the spikes are due to intensification of nonlinear structures on the profile of short, decimetre-scale wind waves when the latter are amplified by intense DWW. Bragg scattering in slicks under the experimental conditions was suppressed stronger than NBC and spikes dominated in total radar return. It is obtained that radar Doppler shifts at HH-polarization are larger than at VV-polarization, particularly in slicks, the same relation is for NBC and BC Doppler shifts, thus indicating different scattering mechanisms for these components. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Figure 1

9 pages, 828 KiB  
Communication
Generalized-Capon Method for Diff-Tomo SAR Analyses of Decorrelating Scatterers
by Fabrizio Lombardini and Francesco Cai
Remote Sens. 2019, 11(4), 412; https://doi.org/10.3390/rs11040412 - 18 Feb 2019
Cited by 9 | Viewed by 3687
Abstract
In synthetic aperture radar (SAR) remote sensing, Differential Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography. Diff-Tomo produces advanced 4D (3D+Time) SAR imaging capabilities, extensively applied to urban deformation monitoring. More [...] Read more.
In synthetic aperture radar (SAR) remote sensing, Differential Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography. Diff-Tomo produces advanced 4D (3D+Time) SAR imaging capabilities, extensively applied to urban deformation monitoring. More recently, it has been shown that, through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating scatterers, the important decorrelation-robust forest Tomography functionality is possible. To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an adaptive, just semi-parametric, generalized-Capon Diff-Tomo method, first conceived at University of Pisa in 2013, for joint extraction of height and dynamical information of natural distributed (volumetric) scatterers, with its formalization and a series of insights. Particular reference is given to the important functionality of the separation of different decorrelation mechanisms in forest layers. Representative simulated and P-band forest data sample results are also shown. The new Diff-Tomo method is getting a flexible and rich decorrelation-robust Tomography functionality, and is able to profile height-varying temporal decorrelation, for significantly distributed scatterers. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Figure 1

16 pages, 2096 KiB  
Article
Statistical Properties of an Unassisted Image Quality Index for SAR Imagery
by Luis Gomez, Raydonal Ospina and Alejandro C. Frery
Remote Sens. 2019, 11(4), 385; https://doi.org/10.3390/rs11040385 - 13 Feb 2019
Cited by 11 | Viewed by 4586
Abstract
The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates [...] Read more.
The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich the M estimator to extend its capabilities. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

28 pages, 21128 KiB  
Article
Theory and Statistical Description of the Enhanced Multi-Temporal InSAR (E-MTInSAR) Noise-Filtering Algorithm
by Antonio Pepe
Remote Sens. 2019, 11(3), 363; https://doi.org/10.3390/rs11030363 - 11 Feb 2019
Cited by 16 | Viewed by 4305
Abstract
In this work, the statistical fundaments of the recently proposed enhanced, multi-temporal interferometric synthetic aperture radar (InSAR) noise-filtering (E-MTInSAR) technique is addressed. The adopted noise-filtering algorithm is incorporated into the improved extended Minimum Cost Flow (EMCF) Small Baseline Subset (SBAS) differential interferometric SAR [...] Read more.
In this work, the statistical fundaments of the recently proposed enhanced, multi-temporal interferometric synthetic aperture radar (InSAR) noise-filtering (E-MTInSAR) technique is addressed. The adopted noise-filtering algorithm is incorporated into the improved extended Minimum Cost Flow (EMCF) Small Baseline Subset (SBAS) differential interferometric SAR (InSAR) processing chain, which has extensively been used for the generation of Earth’s surface displacement time-series in several different contexts. Originally, the input of the InSAR EMCF-SBAS processing toolbox consisted of a sequence of multi-looked, small baseline interferograms, which were unwrapped using the space-time EMCF phase unwrapping algorithm. Subsequently, the unwrapped interferograms were inverted through the SBAS algorithm to retrieve the expected InSAR deformation products. The improved processing chain has complemented the original codes with two additional steps. In particular, a new multi-temporal noise-filtering algorithm for sequences of time-redundant multi-looked DInSAR interferograms, followed by a proper interferogram selection step, has been proposed. This research study is aimed at primarily assessing the performance of the E-MTInSAR noise-filtering algorithm from a theoretical perspective. To this aim, the principles of directional statistics and errors propagation are exploited. Experimental results, carried out by applying the E-MTInSAR algorithm to a sequence of SAR data collected over the Los Angeles bay area, have been used to corroborate the academic outcome of this research. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

27 pages, 26591 KiB  
Article
Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing
by Guanyong Wang, Man Zhang, Yan Huang, Lei Zhang and Fengfei Wang
Remote Sens. 2019, 11(3), 340; https://doi.org/10.3390/rs11030340 - 8 Feb 2019
Cited by 32 | Viewed by 5782
Abstract
Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift [...] Read more.
Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

18 pages, 6894 KiB  
Article
The Frequency Selective Effect of Radar Backscattering from Multiscale Sea Surface
by Dengfeng Xie, Kun-Shan Chen and Jiangyuan Zeng
Remote Sens. 2019, 11(2), 160; https://doi.org/10.3390/rs11020160 - 16 Jan 2019
Cited by 17 | Viewed by 7479
Abstract
The sea surface essentially contains multiscale roughness with capillary waves of many sizes riding on large-scale waves that are also of many sizes. It is instructive to exploit the effect of radar frequency and observation geometry on the effective roughness scales responsible for [...] Read more.
The sea surface essentially contains multiscale roughness with capillary waves of many sizes riding on large-scale waves that are also of many sizes. It is instructive to exploit the effect of radar frequency and observation geometry on the effective roughness scales responsible for radar backscattering so that the scattering mechanism and the scattering source can be better understood and quantitated. Based on common sea spectra and a theoretical scattering model, an attempt is made to attain the above objective. Model predictions, with selective roughness scales, are compared with wide validation data, including L-band radar observations, and predictions from C-band and Ku-band empirical models: geophysical model function (CMOD7) and NASA scatterometer (NSCAT-4) for C- and Ku-bands at different incident angles. Numerical results indicate that effective roughness scales for radar backscattering vary with radar frequency and incidence angle and are related to a portion of sea spectral components; the low limit of which is linearly proportional to the Bragg wavenumber determined by frequency and incidence angle, and the scale factor of the linear relationship is about 0.05. In addition, the root mean square (RMS) height and the correlation length of the effective roughness (i.e., scattering source) derived from the effective roughness decrease gradually as incident angle increases. In particular, the correlation length also linearly depends on the effective wavelength with a coefficient of 3.2. Moreover, these two coefficients are both independent of wind speed, radar frequency, and incident angle. These findings also reveal the essential properties of the spectral components contributing to radar backscattering and its variation with radar frequency and incident angle. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

23 pages, 3783 KiB  
Article
Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network
by Xiaoran Shi, Feng Zhou, Shuang Yang, Zijing Zhang and Tao Su
Remote Sens. 2019, 11(2), 135; https://doi.org/10.3390/rs11020135 - 11 Jan 2019
Cited by 43 | Viewed by 7376
Abstract
Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative [...] Read more.
Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

22 pages, 5113 KiB  
Article
Ship Classification and Detection Based on CNN Using GF-3 SAR Images
by Mengyuan Ma, Jie Chen, Wei Liu and Wei Yang
Remote Sens. 2018, 10(12), 2043; https://doi.org/10.3390/rs10122043 - 14 Dec 2018
Cited by 90 | Viewed by 9512
Abstract
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible [...] Read more.
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

25 pages, 7197 KiB  
Article
Ground Moving Target Imaging and Analysis for Near-Space Hypersonic Vehicle-Borne Synthetic Aperture Radar System with Squint Angle
by Zhanye Chen, Yu Zhou, Linrang Zhang, Chunhui Lin, Yan Huang and Shiyang Tang
Remote Sens. 2018, 10(12), 1966; https://doi.org/10.3390/rs10121966 - 6 Dec 2018
Cited by 32 | Viewed by 4765
Abstract
Near space is the key to integrating “sky” and “space” into the future. A synthetic aperture radar (SAR) that works in this area would initiate a technological revolution for remote sensing applications. This study mainly focused on ground moving target imaging (GMTIm) for [...] Read more.
Near space is the key to integrating “sky” and “space” into the future. A synthetic aperture radar (SAR) that works in this area would initiate a technological revolution for remote sensing applications. This study mainly focused on ground moving target imaging (GMTIm) for a near-space hypersonic vehicle-borne SAR (NS-HSV-SAR) with squint angle. The range history, parameter coupling, and Doppler ambiguity of the squint-looking NS-HSV-SAR are more complicated than traditional side-looking airborne or space-borne SARs. Thus, a precise range model is presented on the basis of phase error analyses. Then, all potential distributions of echo’s azimuth spectrum are derived, and a GMTIm method is proposed on the basis of a detailed analysis of the echo characteristics. The proposed method consists of three steps. Firstly, a prior information-based pre-processing function was created to decrease the Doppler ambiguity and range migration effects. Secondly, a blur matched keystone transform was developed to correct the residual range walk migration. Thirdly, a time-saving chirp Fourier transform was investigated for azimuth focusing. Implementation considerations, including the curvilinear trajectory of the NS-HSV-SAR, multiple moving target imaging, and applicability and limitation of the method, are discussed. Finally, simulation results are presented to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

16 pages, 7906 KiB  
Article
ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform
by Hongyin Shi, Ting Yang and Zhijun Qiao
Remote Sens. 2018, 10(11), 1810; https://doi.org/10.3390/rs10111810 - 15 Nov 2018
Cited by 12 | Viewed by 4898
Abstract
The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular [...] Read more.
The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

18 pages, 5818 KiB  
Article
Analysis and Simulation on Imaging Performance of Backward and Forward Bistatic Synthetic Aperture Radar
by Tingting Li, Kun-Shan Chen and Ming Jin
Remote Sens. 2018, 10(11), 1676; https://doi.org/10.3390/rs10111676 - 24 Oct 2018
Cited by 12 | Viewed by 4196
Abstract
In recent years, bistatic synthetic aperture radar (SAR) technique has attracted considerable and increasing attention. Compared to monostatic SAR for which only the backscattering is measured, bistatic SAR expands the scattering measurements in aspects of angular region and polarization, and greatly enhances the [...] Read more.
In recent years, bistatic synthetic aperture radar (SAR) technique has attracted considerable and increasing attention. Compared to monostatic SAR for which only the backscattering is measured, bistatic SAR expands the scattering measurements in aspects of angular region and polarization, and greatly enhances the capability of remote sensing over terrain and sea. It has been pointed out in recent theoretical researches that bistatic scattering measured in the forward region is preferable to that measured in the backward region in lines of surface parameters retrieval. In the forward region, both dynamic range and signal sensitivity increase to a great extent. For these reasons, bistatic SAR imaging is desirable. However, because of the separated positions of the transmitter and receiver, the degrees of freedom in the parameter space is increased and the forward bistatic imaging is more complicated than the backward bistatic SAR in the aspects of bistatic range history, Doppler parameter estimation and motion compensation, et, al. In this study, we analyze bistatic SAR in terms of ground range resolution, azimuth resolution, bistatic range history and signal to noise ratio (SNR) in different bistatic configurations. Effects of system motion parameters on bistatic SAR imaging are investigated through analytical modeling and numerical simulations. The results indicate that the range resolution is extremely degraded in some cases in forward bistatic SAR imaging. In addition, due to the different imaging projection rules between backward and forward bistatic SAR, the ghost point is produced in the forward imaging. To avoid the above problems, the forward bistatic imaging geometry must be carefully considered. For a given application requirement with the desired imaging performances, the design of the motion parameters can be considered as a question of solving the nonlinear equation system (NES). Then the improved chaos particle swarm optimization (CPSO) is introduced to solve the NES and obtain the optimal solutions. And the simulated imaging results are used to test and verify the effectiveness of CPSO. The results help to deepen understanding of the constraints and properties of bistatic SAR imaging and provide the reference to the optimal design of the motion parameters for a specific requirement, especially in forward bistatic configurations. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

16 pages, 23524 KiB  
Article
Imaging Simulation for Synthetic Aperture Radar: A Full-Wave Approach
by Chiung-Shen Ku, Kun-Shan Chen, Pao-Chi Chang and Yang-Lang Chang
Remote Sens. 2018, 10(9), 1404; https://doi.org/10.3390/rs10091404 - 3 Sep 2018
Cited by 12 | Viewed by 9590
Abstract
Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a [...] Read more.
Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a simple mathematical form, stating only the synthetic process and lacking the physical process based on electromagnetic theory. In this paper, the full-wave method is applied to include the electromagnetic effects in raw data generation, and then a refined omega-K algorithm is used to perform image focusing. According to the proposed method, the focused images not only demonstrate the difference under dielectric constant variation but also present the diversified interaction among the targets with the spacing change. In addition, the images are simulated in different observation modes and bandwidths to provide a satisfactory reference for the design of system parameters. The simulation results from the full-wave method also compare well with chamber experiments. The simulation of SAR imaging based on a full-wave method offers more complete recovery of scattering information and is useful in designing future novel SAR systems and in speckle reduction analysis. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

18 pages, 24747 KiB  
Article
On the Very High-Resolution Radar Image Statistics of the Exponentially Correlated Rough Surface: Experimental and Numerical Studies
by Ming Jin, Kun-Shan Chen and Dengfeng Xie
Remote Sens. 2018, 10(9), 1369; https://doi.org/10.3390/rs10091369 - 28 Aug 2018
Cited by 4 | Viewed by 3707
Abstract
The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponentially correlated rough surfaces. A Ka-band radar system was used to collect the return signal [...] Read more.
The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponentially correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponentially correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponentially correlated rough surface as a good representative for the ground surface. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

21 pages, 3818 KiB  
Article
2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar
by Hong-Cheng Zeng, Jie Chen, Peng-Bo Wang, Wei Yang and Wei Liu
Remote Sens. 2018, 10(7), 1164; https://doi.org/10.3390/rs10071164 - 23 Jul 2018
Cited by 21 | Viewed by 6622
Abstract
Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration [...] Read more.
Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration (RCM) and Doppler frequency migration (DFM) will seriously affect the coherent processing of azimuth signals, and the traditional range match filter will also be mismatched due to the Doppler-intolerant characteristic of GNSS signals. Accordingly, the energy loss of 2-dimensional (2-D) coherent processing is inevitable in traditional methods. In this paper, a novel 2-D coherent integration processing and algorithm for aircraft target detection is proposed. For azimuth processing, a modified Radon Fourier Transform (RFT) with range-walk removal and Doppler rate estimation is performed. In respect to range compression, a modified matched filter with a shifting Doppler is applied. As a result, the signal will be accurately focused in the range-Doppler domain, and a sufficiently high SNR can be obtained for aircraft detection with a moving target detector. Numerical simulations demonstrate that the range-Doppler parameters of an aircraft target can be obtained, and the position and velocity of the aircraft can be estimated accurately by multiple observation geometries due to abundant GNSS resources. The experimental results also illustrate that the blind Doppler sidelobe is suppressed effectively and the proposed algorithm has a good performance even in the presence of Doppler ambiguity. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

24 pages, 7335 KiB  
Article
On Signal Modeling of Moon-Based Synthetic Aperture Radar (SAR) Imaging of Earth
by Zhen Xu and Kun-Shan Chen
Remote Sens. 2018, 10(3), 486; https://doi.org/10.3390/rs10030486 - 20 Mar 2018
Cited by 41 | Viewed by 7824
Abstract
The Moon-based Synthetic Aperture Radar (Moon-Based SAR), using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, [...] Read more.
The Moon-based Synthetic Aperture Radar (Moon-Based SAR), using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, operating in the lunar orbit, Moon-Based SAR imaging is confined within a complex geometry of the Moon-Based SAR, Moon, and Earth, where both rotation and revolution have effects. The extremely long exposure time of Moon-Based SAR presents a curved moving trajectory and the protracted time-delay in propagation makes the “stop-and-go” assumption no longer valid. Consequently, the conventional SAR imaging technique is no longer valid for Moon-Based SAR. This paper develops a Moon-Based SAR theory in which a signal model is derived. The Doppler parameters in the context of lunar revolution with the removal of ‘stop-and-go’ assumption are first estimated, and then characteristics of Moon-Based SAR imaging’s azimuthal resolution are analyzed. In addition, a signal model of Moon-Based SAR and its two-dimensional (2-D) spectrum are further derived. Numerical simulation using point targets validates the signal model and enables Doppler parameter estimation for image focusing. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

21 pages, 3895 KiB  
Article
Sparse Bayesian Learning Based Three-Dimensional Imaging Algorithm for Off-Grid Air Targets in MIMO Radar Array
by Zekun Jiao, Chibiao Ding, Xingdong Liang, Longyong Chen and Fubo Zhang
Remote Sens. 2018, 10(3), 369; https://doi.org/10.3390/rs10030369 - 27 Feb 2018
Cited by 17 | Viewed by 4989
Abstract
In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of [...] Read more.
In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL). Besides, the Bayesian Cramér-Rao Bound (BCRB) for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

16 pages, 2919 KiB  
Article
An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface
by Yu Liu and Kun-Shan Chen
Remote Sens. 2018, 10(2), 286; https://doi.org/10.3390/rs10020286 - 12 Feb 2018
Cited by 5 | Viewed by 4756
Abstract
We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity [...] Read more.
We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-18
Back to TopTop