Circular Synthetic Aperture Radar Research Articles

Target Scattering Feature Extraction Based on Parametric Model Using Multi-Aspect SAR Data

The multi-aspect SAR observation can obtain the backscattering information of the illuminated scene target. There are targets with different structures in the scene, and their backscattering responses are also different. Using backscattering amplitude information to analyze the differences between targets is a conventional method. For point target types, one-dimensional backscattering curves can be used to analyze scattering characteristics, but it is difficult to analyze the overall structure of the target. Therefore, it is necessary to perform statistical analysis on the backscattering information combinating with the multi-aspect target area, and establish parameters to model the target area. In this paper, the algorithm uses the G0 distribution based on expectation maximization (EM) to fit the target area of the SAR scene. For different target types in the scene, the β and σ parameters obtained by the model combined with the backscattering amplitude information are used to perform the target. The results show that full-target in multi-aspect SAR image can be differentiated by two parameters. The scattering of partial-target slices can be characterized using two parameters (amplitude difference from surrounding points, scattering energy). The parametric model quantitatively characterizes the scattering feature of the target level, and the parameters changing corresponds to the change of the target image feature. C-band circular SAR data is used to validate our method. The experimental results give the parameter representation with sampling window based on the analysis of the target scattering, and give parameter estimates to characterize the partial target scattering.

Radar-Based Microwave Breast Imaging Using Neurocomputational Models

In this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.

A Channel Imbalance Calibration Scheme with Distributed Targets for Circular Quad-Polarization SAR with Reciprocal Crosstalk

As polarimetric antennas can be isolated through excellent electronic frameworks in circular quad-polarization (CQP) synthetic aperture radar (SAR) systems, cross-polarization (x-pol) and co-polarization (co-pol) channel imbalances are more challenging and essential to calibrate than crosstalk in polarimetric calibration (PolCAL). In uncalibrated CQP SAR images without corner reflectors (CRs), the reciprocity and reflection symmetry assumptions of the distributed targets are commonly used to estimate the x-pol and co-pol channel imbalances, respectively. To suppress the influence of additive noise on determining channel imbalances through distributed targets, high signal-to-noise ratio (SNR) distributed targets should be obtained from the x-pol and co-pol channels of the CQP SAR images: namely, surface-dominated and volume-dominated targets. However, some reflection symmetry assumptions used in the existing calibration literature have poor applicability with volume-dominated targets, resulting in unsatisfactory estimation results for the co-pol channel imbalance phase. In this paper, we assess the priority of the reflection symmetry properties of volume-dominated targets used to calibrate the co-pol channel imbalance phase in CQP SAR data synthesized from linear quad-polarization data of ALOS, GF-3, and RADARSAT-2. In the theoretical part, high-priority reflection symmetry (termed semireflection symmetry) assumptions are confirmed as the most suitable for estimating the co-pol channel imbalance phase, and were selected to develop an algorithm for estimating the co-pol channel imbalance phase. Furthermore, based on the novel method for estimating the co-pol channel imbalance phase, a channel imbalance calibration scheme is proposed for CQP SAR systems with reciprocal crosstalk, including extracting surface-dominated and volume-dominated targets, and estimating and filtering channel imbalances. We demonstrate the effectiveness of our proposed scheme with CRs in simulated CQP SAR images. The experimental results show that the calibration scheme is an effective workflow for estimating channel imbalances in CQP SAR systems with reciprocal crosstalk.

A Novel Motion Compensation Method Applicable to Ground Cartesian Back-Projection Algorithm for Airborne Circular SAR

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.

A Novel Imaging-Based Target Detection and Parameter Estimation Scheme for Airborne Multichannel Circular Stripmap SAR

Airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) has drawn increasing attention in wide-area surveillance, reconnaissance and traffic monitoring due to its short revisit times. In this paper, a novel scheme for CSSAR-GMTI systems is proposed to offer high-resolution focusing of moving targets and enable efficient detection and accurate parameter estimation, which are ensured by integrating moving target imaging with a space-time adaptive processing clutter suppression step. The presented scheme develops a target imaging algorithm that forms SAR images focused on a particular 2-D motion parameter to maximize target signal energy and recover its high resolution. Moreover, benefiting from the suitable phase compensation designed in the 2-D frequency domain, moving targets can finally be properly focused in the SAR image without displacement and their parameters can be uniquely determined. Experiments on an emulated radar dataset are conducted to validate the effectiveness of the proposed scheme.

Airborne Circular Flight Array SAR 3-D Imaging Algorithm of Buildings Based on Layered Phase Compensation in the Wavenumber Domain

Airborne Circular Flight Array SAR 3-D Imaging Algorithm of Buildings Based on Layered Phase Compensation in the Wavenumber Domain

High-Resolution Sea Surface Current Vector Field Inversion Based on Circular Scanning SAR

High-Resolution Sea Surface Current Vector Field Inversion Based on Circular Scanning SAR

On the Method of Circular Polarimetric SAR Calibration Using Distributed Targets

The channel imbalance and cross-talks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) SAR systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, etc. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and cross-talks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross-polarization and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the cross-talks. Then the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

3-D Tomographic Circular SAR Imaging of Targets Using Scattering Phase Correction

3-D Tomographic Circular SAR Imaging of Targets Using Scattering Phase Correction

Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.

A Sidelobe Suppression Method for Circular Ground-Based SAR 3D Imaging Based on Sparse Optimization of Radial Phase-Center Distribution

Circular ground-based SAR (GBSAR) is a new 3D imaging GBSAR with the potential of acquiring high-quality 3D SAR images and 3D deformation. However, its donut-shaped spectrum and short radius of antenna rotation cause high sidelobes on 3D curved surfaces, resulting in 3D SAR images with poor quality. The multi-phase-center circular GBSAR with full array can effectively suppress sidelobes by filling the donut-shaped spectrum to be the equivalent solid spectrum, but it requires a larger number of phase centers, increasing system cost and engineering difficulties. In this paper, a sidelobe suppression method for circular GBSAR 3D imaging based on sparse optimization of radial phase-center distribution is proposed to suppress high sidelobes at low cost. By deriving the point spread function (PSF) of multi-phase-center circular GBSAR and taking the peak sidelobe level (PSL) and integrated sidelobe level (ISL) of the derived PSF as multi-objective functions, we solve the multi-objective optimization problem to optimize the sparse distribution of radial phase centers. The advantage of the proposed method is that the solved optimal radial phase-center distribution can effectively suppress the 3D sidelobes of circular GBSAR with a limited number of phase centers. Finally, the sidelobe suppression effect of the proposed method is verified via 3D imaging simulations.

A novel method for extracting geometric parameter information of buildings based on CSAR images

ABSTRACT Compared with the traditional linear synthetic aperture radar (LSAR), circular SAR (CSAR) system can observe 360° of the observation area. In turn, more abundant scattering information in all directions of the observation area can be obtained, making it easier to observe the target information. Based on the observation advantages of CSAR, a new method for extracting geometric parameters of buildings is proposed in this paper. In this method, the scattering features of buildings on CSAR sub-aperture images and full-aperture images are extracted, and the corresponding relationship between the scattering features and building geometric parameters is deduced, and then the building geometric parameters are calculated. Through the above corresponding relationship, the geometric parameters of the building are calculated. At the same time, in this method, the advantages of CSAR omnidirectional imaging are used to extract the geometric parameters of buildings from multiple sub-aperture images. This method realizes the high-precision extraction of building geometric parameters by CSAR imaging for the first time. Finally, the experimental verification is carried out by introducing the measured data of the Ku-band autonomously measured, which confirms the effectiveness and accuracy of the proposed method.

A Novel Detection Scheme in Image Domain for Multichannel Circular SAR Ground-Moving-Target Indication.

Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) caused by strong clutter. To mitigate this, a novel, three-step scheme combining clutter background extraction, multichannel clutter suppression, and the degree of linear consistency of radial velocity interferometric phase (DLRVP) test is proposed. In the first step, the spatial similarity of the scatterers and the correlation between sub-aperture images are fused to extract the strong clutter mask prior to clutter suppression. In the second step, using the data remaining after elimination of the background clutter in Step 1, an amplitude-based detector with higher processing gain is utilized to detect potential moving targets. In the third step, a novel test model based on DLRVP is proposed to further reduce the PFA caused by isolated strong scatterers. After the above processing, almost all false alarms are excluded. Measured data verified that the PFA of the proposed method is only 20% that of the comparison method, with improved detection of slow and weakly moving targets and with better robustness.

An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR

This paper studies the imaging of a ground moving target with airborne circular stripmap synthetic aperture radar (CSSAR). First, the range equation of a target moving with accelerations is developed. Then, a new range model of high accuracy is proposed, since the commonly used second-order Taylor-approximated range model is inaccurate when the azimuth resolution is relatively high or the target moves with accelerations. The proposed range model also makes it easy to derive an accurate analytical expression for the target’s 2-D spectrum. Third, based on the proposed range model, the target’s 2-D spectrum is derived and an efficient imaging method is proposed. The proposed imaging method implements focusing via a phase multiplication in the 2-D frequency domain and utilizes the genetic algorithm to accomplish an efficient search of the parameters of the proposed range model. Finally, numerical experiments are conducted to validate the proposed range model and the proposed imaging method.

An Extracting DEM Method Based on The Sub-apertures Difference in CSAR Mode

ABSTRACT This paper proposes a method based on the circular track synthetic aperture radar (CSAR) mode to directly obtain the digital elevation model (DEM) information of the target area through single-circle observation data. This paper improves and innovates the existing methods by using sub-aperture correlation. The circular motion trajectory of the entire radar observation platform is divided into multiple overlapping sub-aperture arcs. Each sub-aperture is projected in the height direction to observe the position deviation of the sub-aperture images more intuitively at different heights. Then, a new calculation method is used to calculate the correlation coefficient between sub-apertures, which enhances the accuracy of extracting DEM results. Finally, the measured data of the Ku band recorded by us are used to verify the effectiveness and accuracy of the algorithm proposed in this paper.

Local Road Area Extraction in CSAR Imagery Exploiting Improved Curvilinear Structure Detector

Road extraction is an important part of synthetic aperture radar (SAR) image interpretation. In recent years, circular SAR (CSAR) has attracted extensive attention from researchers owing to its ability of 360-degree observation. Due to the unique imaging geometry of CSAR, CSAR images contain more complete road information. However, the curvilinear-structured appearance of roads in CSAR images and the complexity of the scene result in difficulties in the road extraction. The Curvilinear structure detector (CSD) is capable of extracting the curvilinear structures with a specific width from complicated image scene. Based on the traditional CSD, an improved CSD (ICSD) for local road area extraction from CSAR images is introduced in this paper. First, by adopting ICSD, the edges of a CSAR image and centerlines of local roads are extracted, as well as their direction. Second, the local roads are obtained by geometrical and radiometrical rules. Finally, the missing intersections and edge pixels are repaired to acquire high-precision and high-quality extraction results of local road area. The experimental results on different band CSAR images reveal that the proposed method exhibit enhanced performance than the three state-of-the-art methods.

Single-Channel Circular SAR Ground Moving Target Detection Based on LRSD and Adaptive Threshold Detector

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

An Universal Circular Synthetic Aperture Radar

This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR.

A Hybrid Synthetic Aperture and Time-Reversal MUSIC Algorithm for Subwavelength Radar Imaging

A promising chipless RFID approach uses millimeter-wave synthetic aperture radar (SAR) to image metal ink-printed ID tags from a meter or more away. Due to printing cost, it is desirable to minimize the size and spacing of metal patches within a tag, preferably into the subwavelength regime. Although circular SAR (CSAR) has a sharply peaked point response in 2D, its side lobes of closely-spaced targets interfere strongly with each other to distort the image. An alternative 2D subwavelength imaging approach with minimal side lobes is Time-Reversal MUSIC (TR-MUSIC). Traditional TR-MUSIC, however, requires a large number of transmitters and receivers. We propose a hybrid synthetic aperture TR-MUSIC algorithm (SATR-MUSIC) that combines the benefits of both approaches. Using relatively few transceivers, SATR-MUSIC is able to resolve objects separated by approximately in 2D with minimal background artifacts. It does so by averaging TR-MUSIC’s imaging kernel incoherently over the synthetic aperture.

Precise Near-Range 3-D Image Reconstruction Based on MIMO Circular Synthetic Aperture Radar

This article studies the near-range 3-D image reconstruction based on millimeter-wave multiple-input-multiple-output circular synthetic aperture radar (MIMO-CSAR). We first derive the exact forward wave model of MIMO-CSAR and then propose a range migration algorithm for near-range high-precision 3-D image reconstruction. Our formulation is strictly based on wave theory. The propagation attenuation, which is an inherent factor in wave equation and is significant for near-range wide-angle sensing case, is considered and compensated efficiently with a Fourier-domain matched filtering. Besides, the phase decoupling is achieved with a 2-D Stolt mapping. The computational complexity of the algorithm is at the same level as the state-of-the-art method. Compared with the state of the art, by considering the amplitude variation, the imaging quality can be improved for near-range measurement with large beamwidth. The results from both synthetic and practical measurement data demonstrate that a wider dynamic range (less sidelobes appeared at the same dynamic level) and a better recovery for widespread targets compared with the state-of-the-art imaging method.