Airborne Synthetic Aperture Radar Research Articles

An Image Planar Positioning Method Base on Fusion of Dual-View Airborne SAR Data

Effective utilization of airborne synthetic-aperture (Airborne SAR) imagery often requires precise location of each image pixel. Historically, the positioning of airborne SAR imagery either relies on the use of reliable reference points to determine the relative position of the image, or requires the precise motion information of the aircraft and the characteristics of the SAR data collection system as input to determine the absolute position of the image. However, for many applications, the accuracy of traditional positioning methods is not high due to the challenge in obtaining the accurate geographic positions of reliable reference points and the inaccuracy of the recorded aircraft motion information. This study introduces an airborne SAR image planar positioning approach based on the premise that the systematic positioning error of the dual-view airborne SAR images are relatively consistent. The suggested planar positioning method applies the positioning auxiliary parameters of the initial ground-range airborne SAR image to ascertain the transformation relationship between the target’s initial geographic position and pixel position, and it then uses the equivalent equation for the position of the homologue point to assess the systematic positioning error of the SAR image and determine the geographic position of a pixel in a digital SAR image. This approach has advantages over previous techniques in that it requires no precise geographic position information of the ground reference points, and on the basis of using the RD model to accomplish coarse positioning of four corners of SAR image, it no longer needs aircraft trajectory data. Tests were conducted using two airborne SAR images actually captured, and the experimental results indicate that the proposed method can achieve high precision planar positioning of dual-view airborne SAR images. Error sources are analyzed and recommendations are given to improve image positioning accuracy in future airborne SARs.

Hybrid Domain Efficient Modulation-Based Deceptive Jamming Algorithm for Nonlinear-Trajectory Synthetic Aperture Radar

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency.

A Novel Point Target Attitude Compensation Method Using Electromagnetic Reflectance Theory

During the process of the airborne synthetic aperture radar (SAR) system platform in space, platform attitude deflection is inevitable. However, large attitude deflection angles are unacceptable for polarimetric calibration using point targets, especially the dihedral, which is very sensitive to the pointing angle of the radar. To mitigate the impact of attitude angles on calibration accuracy, attitude compensation of the corner reflector is necessary during the calibration process. The conventional approach to attitude compensation typically maps the three-dimensional attitude angle information to the one-dimensional polarimetric orientation angle (POA) information. However, the reduction of dimension inevitably results in information loss, leading to errors that affect calibration performance when the attitude angle is large. In order to ensure the accuracy of point target calibration, this paper proposes a novel point target compensation method based on the reflection theory of electromagnetic waves. This method is based on three-dimensional attitude angle information and has higher reliability than the POA method. Finally, this paper calculates the distance between the scattering matrices obtained after compensation based on the proposed method and the POA method to obtain the difference in the performance of the two methods. Through a simulation, this paper finds that when the attitude angle is small, the results of the two schemes are approximately the same, but as the attitude angle increases, the error between the two gradually increases. This suggests that the proposed method has greater advantages in the case of attitude deflection. Furthermore, the proposed method does not require additional information supplementation compared with the equivalent POA method, making it highly practical.

Analysis of Geometric Characteristics and Coverage for Moon-Based/Spaceborne Bistatic SAR Earth Observation

With the rapid development of Earth system science, a new understanding of the complete Earth system has highlighted the crucial importance of integrated observations, especially in research involving large-scale geoscience phenomena. As an active sensor with all-time and all-weather capabilities, synthetic aperture radar (SAR) has been widely used in recent decades for Earth observation. However, the existing spaceborne, airborne, and ground-based SAR systems have difficulty providing temporally consistent and spatially continuous Earth observation data on a global scale. As Earth’s only natural satellite, the Moon is a very promising Earth observation platform. By deploying a transmitter on the Moon and a receiver on the high-orbit satellite, a Moon-based/spaceborne bistatic synthetic aperture radar (MS-BiSAR) can be formed. In this paper, the MS-BiSAR geometric model of Earth observation was established using ephemeris and orbit propagators with reference system transformations, and three different MS-BiSAR configurations were used to calculate and analyze their geometric characteristics and Earth observation coverage. The results show that with the advantage of wide swaths, continuous observation capabilities, and large coverage, such an MS-BiSAR could significantly contribute to monitoring and understanding large-scale geoscience phenomena.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Two-Step Matching Approach to Obtain More Control Points for SIFT-like Very-High-Resolution SAR Image Registration

Airborne VHR SAR image registration is a challenging task. The number of CPs is a key factor for complex CP-based image registration. This paper presents a two-step matching approach to obtain more CPs for VHR SAR image registration. In the past decade, SIFT and other modifications have been widely used for remote sensing image registration. By incorporating feature point location affine transformation, a two-step matching scheme, which includes global and local matching, is proposed to allow for the determination of a much larger number of CPs. The proposed approach was validated by 0.5 m resolution C-band airborne SAR data acquired in Sichuan after the 2008 Wenchuan earthquake via a SAR system designed by the IECAS. With the proposed matching scheme, even the original SIFT, which is widely known to be unsuitable for SAR images, can achieve a much larger number of high-quality CPs than the one-step SIFT-OCT, which is tailored for SAR images. Compared with the classic one-step matching approach using both the SIFT and SITF-OCT algorithms, the proposed approach can obtain a larger number of CPs with improved precision.

Sub-aperture SAR imaging with uncertainty quantification

In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed to account for this issue, including both full-aperture and sub-aperture methods based on filtering, regularized least squares, and Bayesian methods. A full-aperture method that uses a hierarchical Bayesian prior to incorporate appropriate speckle modeling and reduction was recently introduced to produce samples of the posterior density rather than a single image estimate. This uncertainty quantification information is more robust as it can generate a variety of statistics for the scene. As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient. Moreover, the method was not explicitly designed to mitigate the effects of the faulty isotropic scattering assumption. In this work we therefore propose a new sub-aperture SAR imaging method that uses a sparse Bayesian learning-type algorithm to more efficiently produce approximate posterior densities for each sub-aperture window. These estimates may be useful in and of themselves, or when of interest, the statistics from these distributions can be combined to form a composite image. Furthermore, unlike the often-employed -regularized least squares methods, no user-defined parameters are required. Application-specific adjustments are made to reduce the typically burdensome runtime and storage requirements so that appropriately large images can be generated. Finally, this paper focuses on incorporating these techniques into SAR image formation process, that is, for the problem starting with SAR phase history data, so that no additional processing errors are incurred. The advantage over existing SAR image formation methods are clearly presented with numerical experiments using real-world data.

A Single-Layer S/X- Band Shared Aperture Antenna with MIMO Characteristics at X-Band for Airborne Synthetic Aperture Radar Applications

New frequencies can be supported with very effective space use by using the shared aperture antenna This work presents on designing a dual-banddual-polarized (DBDP) S/X-band shared aperture antenna (SAA) for synthetic aperture radar (SAR) applications operating at S-band frequency (3.2 GHz) and X-band frequency (9.65 GHz). The single-layer SAA DBDP S-band antenna is designed in a square-shaped patch with coaxial feeding in both vertical and horizontal polarization. The X-band antenna design is in 1 × 3 vertical series with microstrip feeding and arranged at four corners of the proposed antenna. The S-band antenna is mainly used for airborne applications such as air traffic control and surface ship radar. In contrast, the X-band antenna application is maritime vessel traffic control, defense tracking, and vehicle speed detection for law enforcement. To verify the antenna, a prototype is fabricated and measured with s-parameters. The proposed design exhibits that the gain of the S-band is 7.2 dB and for the X-band is 12.4 dB, and the isolation is achieved more than −35 dB, and for this antenna, we achieved a bandwidth of 0.12 GHz for S-band and 0.27 GHz for X-band. However, the X-band antenna is a multi-input and multioutput antenna that is to be validated by using MIMO characteristic parameters such as envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), mean effective gain, and mutual coupling. The MIMO characteristic parameter of X-band antenna values is found to be in a similar manner to both simulated and measured values. For this X-Band antenna, ECC, DG, CCL, and mutual coupling were achieved as below 0.05, 9.5 dB, 0.5 bps/Hz, and −30 dB to −55 dB, respectively. The total size of the antenna is 100 mm × 100 mm × 1.6 mm.

Moving Targets Artifacts Removal in Multiaspect SAR Imagery Based on Logarithm Background Subtraction

Multiaspect SAR has the capability of providing a high-resolution image due to its long synthetic aperture feature. However, a moving target can generate long and complex signatures in a multiaspect SAR image, which may hamper the applications like image interpretation and target detection. In this article, two methods are proposed to remove the moving target signature in a single-channel multiaspect SAR image. The two methods are all based on logarithm background subtraction. The first one is a fast scheme with a cost of reduced resolution. While the second one focuses on preserving the high resolution, it takes more time than the previous one. The first method utilizes the fact of target signal position changes in subaperture image sequence to obtain the static background. The second method combines the detection results to exclude the moving target signal in each complex-valued subaperture image, then obtaining the high resolution of static background by coherent summation. The methods are validated by synthetic and real airborne SAR data.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar (SAR), the three-dimensional (3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection (BP) and the data extraction method based on modified uniformly redundant arrays (MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing (CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally, by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

A novel linear frequency modulation signal generator in real‐time processing of airborne synthetic aperture radar

Generally, synthetic aperture radar(SAR)generates linear frequency modulation (LFM) signals by method of look-up table (LUT), which requires calculating different LFM signals and storing these waveforms in advance. With the demand for multi-mode, high-resolution and wide swath of airborne SAR systems, the method of LUT is not available, because that requires huge storage resources. In order to solve these drawbacks in limited hardware resources, this letter proposes a novel LFM signal generator in real-time processing. By taking advantage of high level synthesis (HLS) on FPGA, the proposed generator calculates the LFM signal simply using parameters of pulse width, bandwidth, and sampling frequency. Meanwhile, in order to compensate for the SAR system phase error, the proposed generator calculates phase error using a linear polynomial algorithm, which can compensate for the phase error in real time. The simulation results prove that this approximation can achieve a good effect. The performance of this method is implemented by experiments with an X-band airborne SAR system.

Motion Error Estimation and Compensation of Airborne Array Flexible SAR Based on Multi-Channel Interferometric Phase

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR.

Guidance-Aided Triple-Adaptive Frost Filter for Speckle Suppression in the Synthetic Aperture Radar Image

Speckle noise exists inherently in the synthetic aperture radar (SAR) image. Its multiplicative property leads to lots of difficulties in SAR image processing. A novel guidance-aided triple-adaptive Frost filter is proposed in this paper, which has potential for real-time processing platforms. Firstly, a scale-adaptive sliding window sizing method is adopted to determine the neighborhood ranges for every point in the image. All the subsequent processing is based on it. Then, an adaptive calculation for the tuning factor in the Frost filter is embedded into the proposed method. Lastly, the feature information apertured from the original image is used to provide guidance for edge recovery automatically, which guarantees the satisfactory ability for feature preservation. Thus, a novel improved Frost filter is proposed with triple adaptabilities. Both the positioning accuracy and response sensitivity of the scale-adaptive sliding window sizing method are verified first. The superiority of the adaptive tuning factor combined with the scale-adaptive sliding window is confirmed by two comparison experiments. At last, the results of speckle suppression experiments on the synthetic images and two natural airborne SAR images present a better performance than other methods.

Generation of Multiple Frames for High Resolution Video SAR Based on Time Frequency Sub-Aperture Technique

Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, the application of ViSAR is limited in practice. Although back projection (BP) can avoid unnecessary repetitive processing of overlapping parts between consecutive video frames, it is still time-consuming for high-resolution video-SAR data processing. In this article, in order to achieve the same or a similar effect to BP and reduce the computational burden as much as possible, a novel time-frequency sub-aperture technology (TFST) is proposed. Firstly, based on azimuth resampling and full aperture azimuth scaling, a time domain sub-aperture (TDS) processing algorithm is proposed to process ViSAR data with large coherent integration angles to ensure the continuity of ViSAR monitoring. Furthermore, through frequency domain sub-aperture (FDS) processing, multiple high-resolution video frames can be generated efficiently without sub-aperture reconstruction. In addition, TFST is based on the range migration algorithm (RMA), which can take into account the accuracy while ensuring efficiency. The results of simulation and X-band airborne SAR experimental data verify the effectiveness of the proposed method.

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Airborne Synthetic Aperture Radar (SAR) has the potential to monitor remotely the road traffic infrastructure on a large scale. Of particular interest is the road surface roughness, which is an important road safety parameter. For this task, novel algorithms need to be developed. Machine learning approaches, such as Artificial Neural Networks (ANN) and Random Forest Regression, which can perform non-linear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with DLR's airborne F-SAR radar system. Several machine learning-based approaches were tested on the datasets to estimate road surface roughness. The resulting models were then compared with ground truth surface roughness values and also with the semi-empirical surface roughness model studied in previous work.

Learning Scattering Similarity and Texture-Based Attention With Convolutional Neural Networks for PolSAR Image Classification

The varying polarimetric orientation angles (POAs) result in scattering diversity, leading to ambiguity in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Exploring the scattering characteristics in the polarimetric rotation domain (PRD) and the complementary features can help overcome the ambiguity. To address this, we propose a novel PolSAR image classification algorithm called learning scattering similarity and texture-based attention with convolutional neural networks (LSTCNNs). Three strategies are included in the proposed method. First, a pixel-level scattering similarity learning (SSL) module is proposed to analyze the scattering components of radar targets by learning the mapping from PolSAR data in the PRD to typical scattering models, with rotation angles as learnable parameters to utilize scattering diversity and avoid ambiguity. Second, a neighborhood-level texture-based attention (TA) module is proposed to learn the spatially enhanced features of PolSAR images, with the attention module design guided by the physical meaning of texture and consideration of channel and position importance. Finally, the proposed LSTCNN, which includes the SSL module, the TA module, and the classification module, combines pixel-level scattering features in the PRD and neighborhood-level texture features to increase the discriminability of features. The experimental results on three PolSAR images acquired by airborne SAR (AIRSAR) and experimental SAR (E-SAR) demonstrate the robustness and excellence of LSTCNN.

An X/Ku-Band Series Feed Center Fed Shared Aperture Array Antenna for AIR-Borne Synthetic Aperture Radar Applications

An X/Ku-Band Series Feed Center Fed Shared Aperture Array Antenna for AIR-Borne Synthetic Aperture Radar Applications

A High-Resolution Airborne SAR Autofocusing Approach Based on SR-PGA and Subimage Resampling With Precise Hyperbolic Model

High-resolution airborne synthetic aperture radar (SAR) imaging requires long synthetic aperture time (LSAT). However, the LSAT invalidates the Fresnel approximation in the traditional autofocusing method, leaving a residual signal phase in the motion error. To solve the problem, a signal-reconstruction-based phase gradient autofocus (SR-PGA) and a sub-image resampling (SIR) methods are proposed. They are developed on the hyperbolic model. First, a sub-aperture division strategy divides the full-aperture high-order error into multiple low-order sub-aperture errors. Then, the SR-PGA is developed to estimate the sub-aperture error (SPE), in which the precise deramping is reconstructed to eliminate the residual signal phase in the SPE. Third, the SIR is proposed to eliminate residual Doppler-variant shift of the adjacent sub-images, improving the accuracy of the SPE combination. Finally, simulation and actual data processing verify the effectiveness and validity of the algorithm.

High-isolation and Side Lobe Level Reduction for Dual-band Series-fed Centre-fed X/Ku Shared Aperture Binomial Array Antenna for Airborne Synthetic Aperture Radar Applications

High-isolation and Side Lobe Level Reduction for Dual-band Series-fed Centre-fed X/Ku Shared Aperture Binomial Array Antenna for Airborne Synthetic Aperture Radar Applications

Suppressive Interference Suppression for Airborne SAR Using BSS for Singular Value and Eigenvalue Decomposition Based on Information Entropy

Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.