Airborne Synthetic Aperture Radar Research Articles

A Case Study on Image Co-Registration of Hyper Spectral and Dual (L & S) Band SAR Data and Ore Findings Over Zewar Mines, India

The technique of superimposing two or more photographs in a way that ensures that for each image, the same pixel corresponds to the same location of the target scene is known as image coregistration It is a crucial stage in the picture enhancement process for satellite images. Different frequency bands store feature. Image fusion makes it possible to superimpose co-registered pictures taken by several sensors to get a superior image incorporating elements from both sources. On many match patches that are evenly dispersed over the two scenes, we estimate pixel offsets between possibly coherent picture pairings as image coregistration allows a more detailed single image to be obtained than many photos with distinct attributes. This study presents existing various fusion methods for ASAR (Airborne Synthetic Aperture Radar) images in the S-band and L-band to interpret urban, forestry, and agricultural areas. AVIRIS hyper spectral data also shows mining possibilities on ore of region. Hence, the seeking of ore region, and coregistration using fusion facilitates the remote sensing architecture next to drones.

Design of an integrated imaging system of airborne SAR and visible light camera based on common aperture antenna

Design of an integrated imaging system of airborne SAR and visible light camera based on common aperture antenna

Impact Analysis and Compensation Methods of Frequency Synchronization Errors in Distributed Geosynchronous Synthetic Aperture Radar

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

Recognition for SAR deformation military target from a new MiniSAR dataset using multi-view joint transformer approach

Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at https://github.com/Lvjiming/MJT.

Enhancing SAR Multipath Ghost Image Suppression for Complex Structures through Multi-Aspect Observation

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image.

A Robust Track Estimation Method for Airborne SAR Based on Weak Navigation Information and Additional Envelope Errors

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm.

A Multicomponent Linear Frequency Modulation Signal-Separation Network for Multi-Moving-Target Imaging in the SAR-Ground-Moving-Target Indication System

Multi-moving-target imaging in a synthetic aperture radar (SAR) system poses a significant challenge owing to target defocusing and being contaminated by strong background clutter. Aiming at this problem, a new deep-convolutional-neural-network (CNN)-assisted method is proposed for multi-moving-target imaging in a SAR-GMTI system. The multi-moving-target signal can be modeled by a multicomponent LFM signal with additive perturbation. A fully convolutional network named MLFMSS-Net was designed based on an encoder–decoder architecture to extract the most-energetic LFM signal component from the multicomponent LFM signal in the time domain. Without prior knowledge of the target number, an iterative signal-separation framework based on the well-trained MLFMSS-Net is proposed to separate the multi-moving-target signal into multiple LFM signal components while eliminating the residual clutter. It works well, exhibiting high imaging robustness and low dependence on the system parameters, making it a suitable solution for practical imaging applications. Consequently, a well-focused multi-moving-target image can be obtained by parameter estimation and secondary azimuth compression for each separated LFM signal component. The simulations and experiments on both airborne and spaceborne SAR data showed that the proposed method is superior to traditional imaging methods in both imaging quality and efficiency.

A comparative study on the efficacy of dual-pol and full-pol ASAR data in radiative transfer modeling for forest above-ground biomass estimation

ABSTRACT In this study, a comprehensive evaluation of the potential of dual-polarization and full-polarization L- and S-band airborne SAR (LS-ASAR) data (ISRO’s dual-frequency airborne SAR mounted on NASA’s Gulfstream III aircraft) in forest above-ground biomass (AGB) estimation has been carried out. The study area comprises the temperate forests of the Lenoir landing site in Southwest Alabama, United States of America. A trunk scattering model based on Vector Radiative Transfer (VRT) theory is used in this study to estimate the AGB. Full-polarization ASAR data inversion retrieves three critical biophysical parameters: tree height, diameter at breast height (DBH), and tree count, whereas with dual-polarization ASAR data, the model is limited to retrieving only two parameters (tree height and DBH). These parameters are then employed in a suitable allometric equation to estimate the AGB over the study area. Validation of the retrieval results is performed through comparison with ground-truth measurements taken from the study site. The findings demonstrate that the inversion of L-band full-polarized data yields the highest correlation (R2 = 0.87, RMSE = 20.37 t/ha), revealing high accuracy in AGB estimation. The L-band dual-polarized data shows a high correlation (R2 = 0.83, RMSE = 30.66 t/ha), indicating reasonable accuracy. However, the S-band data, both full and dual-polarized, reveals weaker correlations with higher RMSE values (R2 = 0.59, RMSE = 55.07 t/ha, and R2 = 0.49, RMSE = 58.31 t/ha, respectively), suggesting a lower degree of reliability. This study emphasizes the pre-eminence of L-band fully polarized data inversion for reliable and accurate AGB estimation while also showing the utility of relatively ubiquitous dual-pol data (L-band) to achieve reasonable accuracy for the same.

Rice Height Estimation with Multi-Baseline PolInSAR Data and Optimal Detection Baseline Combination Analysis

Rice is a primary food source, and height is a crucial parameter affecting its growth status. Consequently, high-precision, real-time monitoring of quantitative changes in crop height are required for improved crop production. Polarimetric interferometric SAR (PolInSAR) has both polarization and interferometric observation capabilities. Due to the short height of crops and rapid growth changes, the large spatial and short temporal baselines of PolInSAR data are essential for effective crop height inversion; however, relevant satellite-borne SAR and airborne SAR data are currently limited. This study presents a PolInSAR rice height inversion algorithm that uses the oriented volume over ground (OVoG) mode with PolInSAR 0-time and controllable spatial baseline data from a LAMP microwave anechoic chamber. Exploiting the advantages of microwave anechoic chamber measurement data, which includes continuous frequency bands, multiple baselines, and varied incidence angles, the influences of incident angles, baseline length, number of baselines, and baseline combinations are assessed. The highest accuracy rice plant height inversion has a root mean square deviation (RMSE) of 0.1093 m and is achieved with an incidence angle of 35–55°, baseline length of 0.25°, and three to four baselines. Furthermore, an imaging geometric equivalence analysis provides reliable foundation data to guide research into new earth observation SAR systems. The results indicate that, under simulated observations from the GF3 satellite at an altitude of 755 km, the optimal spatial baseline ranges for various frequency bands are as follows: L-band: 10.93–42.59 km; S-band: 4.10–15.97 km; C-band: 2.48–9.64 km; X-band: 1.36–5.32 km; Ku-band: 0.87–3.40 km. Notably, the measurement modes corresponding to the C, X, and Ku bands are ultimately identified as the most suitable for PolInSAR rice height inversion.

Self-Supervised Transformers for Unsupervised SAR Complex Interference Detection Using Canny Edge Detector

As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system.

DESIGN OF AMPLITUDE AND PHASE CONTROLLED BUTLER BEAMFORMING NETWORK FOR X/KU-BAND AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

Beamforming networks play an important role in today's communication systems. The Butler matrix network is one of the most useful networks used to feed array antennas and generate multi-beamforming systems. Its complexity increases with an increase in the number of input and output ports. This research work presents simulated results of an unequal amplitude 8 × 8 Butler matrix network, in which the Taylor series synthesis technique was employed to generate unequal amplitudes (-9.626, -9.125, -8.798, -8.636, -8.636, -8.798, -9.125, and -9.626 dB). The design of the Butler matrix beamforming network is based on two frequency bands, i.e., the X-band and Ku-band, with center frequencies of 9.3 and 13.265 GHz, respectively. The design mainly involves 12 hybrid couplers and eight phase shifters that can generate beams in eight different directions (-32.4°, -26.5°, -15.5°, -10.3°, 10.3°, 15.5°, 26.5°, and 32.4°). This design is based on the Taylor series expansion, which has the advantage of sidelobe level reduction of -17 dB. All of the simulations were done using advanced design system software key sight technologies. The simulations results were in line with the theoretical calculations.

AN X/Ku DUAL-BAND SHARED APERTURE UNIFORM LINEAR ANTENNA ARRAY FOR AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

An amplitude distribution of shared aperture antenna array for airborne SAR applications using a dual-band (X/Ku-band) synthesis is presented in this research. It is utilizing a linear arrangement with 1 × 8 antenna elements and a series feeding mechanism to operate in the X/Ku bands. The proposed shared aperture antenna array has eight elements providing a uniform array in the X and Ku bands. To realize the proposed scanning array, the X/Ku-band antenna array takes an inter-element spacing of 0.7 λ, which provides a scanning angle of ± 25°. When the space between adjacent antenna arrays is effectively used to reduce the influence of mutual coupling, the synthesis array's X-band and Ku-band antenna arrays can perform well in isolation. The uniform linear array exhibits Side Lobe Level (SLL) of -23.9 dB in X-Band and -24.7 dB in Ku-Band, with maximum realized gains of 14.2 dBi in X-Band and 15.7 dBi in Ku-Band. The isolation between bands and ports exceeds 25 dB. The uniform amplitude distribution shared aperture antenna (UAD SAA) has accepted an SLL performance of -13.5 dB. Additionally, it displays a uniform (X/Ku-band 200/144 MHz) impendence bandwidth that is relatively wide.

Highly Integrated Low-Profile Multilayer Dual-Polarized Phased Array Antenna With Truncated Cavities for First Pulsed Bistatic L-Band Airborne SAR Sensor

Highly Integrated Low-Profile Multilayer Dual-Polarized Phased Array Antenna With Truncated Cavities for First Pulsed Bistatic L-Band Airborne SAR Sensor

Studies on High-Resolution Airborne Synthetic Aperture Radar Image Formation with Pseudo-Random Agility of Interpulse Waveform Parameters

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one.

Optimized SAR imaging algorithm with SSL constraints for the Azimuth direction

In order to address the low azimuth imaging problem of traditional Range Doppler (RD) algorithm, a high-resolution imaging algorithm with optimal azimuth sampling sequence length (SSL) constraints is hereby proposed. According to this algorithm, efforts are made to design a formula for the calculation of the initial azimuth SSL on the basis of SAR azimuth imaging parameters. Having obtained the initial SSL and the criteria for the evaluation of SAR imaging performance, we continue to figure out the extreme values of the curve within a reasonable range of variation. On the basis of these values, the optimal azimuth SSL can be determined to provide data support for global azimuth imaging. Measurement data from imaging experiments on airborne and spaceborne SARs show that, compared with traditional RD algorithm, the proposed algorithm reveals outstanding advantages as indicated by a number of factors critical for the evaluation of imaging performance, such as resolution, peak sidelobe ratio (PSLR) and integral sidelobe ratio (ISLR). Compared with reference methods with local optimal imaging performance, the proposed algorithm can help to obtain global optimal SAR imaging data.

Above Ground Level Estimation of Airborne Synthetic Aperture Radar Altimeter by a Fully Supervised Altimetry Enhancement Network

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm.

Airborne SAR to Optical Image Registration Based on SAR Georeferencing and Deep Learning Approach

Airborne SAR to Optical Image Registration Based on SAR Georeferencing and Deep Learning Approach

MuA-SAR Fast Imaging Based on UCFFBP Algorithm with Multi-Level Regional Attention Strategy

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.

On evaluating the efficacy of air-borne synthetic aperture radar for detecting polar bears: A pilot study

On evaluating the efficacy of air-borne synthetic aperture radar for detecting polar bears: A pilot study

Research on simulation method for nonlinear effects of airborne SAR electromagnetic interference

In order to simulate the nonlinear interference effect of high-power electromagnetic signals on airborne Synthetic Aperture Radar (SAR) imaging, the walk–stop geometric modeling of airborne SAR was established, and the range time-domain pulse coherence method was used to simulate SAR echo. By establishing a SAR receiver RF channel model, the interference mechanism of high-power electromagnetic signals is combined on RF front-end devices, the nonlinear effects on the echo signal when high-power electromagnetic signals enter the receiver are simulated, and finally the range Doppler algorithm is used to image the echo signal and the interference signal. A method for evaluating the interference effect of high-power electromagnetic signals on airborne SAR imaging using the correlation coefficient of images was proposed. The results showed that the change in the correlation coefficient of images before and after interference relative to the interference signal ratio was consistent with the image interference effect. The correlation coefficient showed a stepped trend with the change in the interference signal ratio, and there was a saturation effect after the interference effect reached a certain level.