Real-time Synthetic Aperture Radar Research Articles

Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping.

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image.

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing.

LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion

In recent years, significant strides have been made in the field of synthetic aperture radar (SAR) ship detection through the application of deep learning techniques. These advanced methods have substantially improved the accuracy of ship detection. Nonetheless, SAR images present distinct challenges, including complex backgrounds, small ship targets, and noise interference, thereby rendering the detectors particularly demanding. In this paper, we introduce LRTransDet, a real-time SAR ship detector. LRTransDet leverages a lightweight vision transformer (ViT) and a multi-scale feature fusion neck to address these challenges effectively. First, our model implements a lightweight backbone that combines convolutional neural networks (CNNs) and transformers, thus enabling it to simultaneously capture both local and global features from input SAR images. Moreover, we boost the model’s efficiency by incorporating the faster weighted feature fusion (Faster-WF2) module and coordinate attention (CA) mechanism within the feature fusion neck. These components optimize computational resources while maintaining the model’s performance. To overcome the challenge of detecting small ship targets in SAR images, we refine the original loss function and use the normalized Wasserstein distance (NWD) metric and the intersection over union (IoU) scheme. This combination improves the detector’s ability to efficiently detect small targets. To prove the performance of our proposed model, we conducted experiments on four challenging datasets (the SSDD, the SAR-Ship Dataset, the HRSID, and the LS-SSDD-v1.0). The results demonstrate that our model surpasses both general object detectors and state-of-the-art SAR ship detectors in terms of detection accuracy (97.8% on the SSDD and 93.9% on the HRSID) and speed (74.6 FPS on the SSDD and 75.8 FPS on the HRSID), all while demanding 3.07 M parameters. Additionally, we conducted a series of ablation experiments to illustrate the impact of the EfficientViT, the Faster-WF2 module, the CA mechanism, and the NWD metric on multi-scale feature fusion and detection performance.

Application of geospatial tools in the assessment of Flood hazard impact on social vulnerability of Malda district, West Bengal, India

Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inundation. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inundation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.

Advancements in Spaceborne Synthetic Aperture Radar Imaging with System-on-Chip Architecture and System Fault-Tolerant Technology

With the continuous development of satellite payload and system-on-chip (SoC) technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems play a crucial role in various defense and civilian domains, including Earth remote sensing, military reconnaissance, disaster mitigation, and resource exploration. However, designing high-performance and high-reliability SAR imaging systems that operate in harsh environmental conditions while adhering to strict size, weight, and power consumption constraints remains a significant challenge. In this paper, we introduce a spaceborne SAR imaging chip based on a SoC architecture with system fault-tolerant technology. The fault-tolerant SAR SoC architecture has a CPU, interface subsystem, memory subsystem, data transit subsystem, and data processing subsystem. The data processing subsystem, which includes fast Fourier transform (FFT) modules, coordinated rotation digital computer (CORDIC) modules (for phase factor calculation), and complex multiplication modules, is the most critical component and can achieve various modes of SAR imaging. Through analyzing the computational requirements of various modes of SAR, we found that FFT accounted for over 50% of the total computational workload in SAR imaging processing, while the CORDIC modules for phase factor generation accounted for around 30%. Therefore, ensuring the fault tolerance of these two modules is crucial. To address this issue, we propose a word-length optimization redundancy (WLOR) method to make the fixed-point pipelined FFT processors in FFT modules fault tolerant. Additionally, we propose a fault-tolerant pipeline CORDIC architecture utilizing error correction code (ECC) and sum of squares (SOS) check. For other parts of the SoC architecture, we propose a generic partial triple modular redundancy (TMR) hardening method based on the HITS algorithm to improve fault tolerance. Finally, we developed a fully automated FPGA-based fault injection platform to test the design’s effectiveness by injecting errors at arbitrary locations. The simulation results demonstrate that the proposed methods significantly improved the chip’s fault tolerance, making the SAR imaging chip safer and more reliable. We also implemented a prototype measurement system with a chip-included board and demonstrated the proposed design’s performance on the Chinese Gaofen-3 strip-map continuous imaging system. The chip requires 9.2 s, 50.6 s, and 7.4 s for a strip-map with 16,384 × 16,384 granularity, multi-channel strip-map with 65,536 × 8192 granularity, and multi-channel scan mode with 32,768 × 4096 granularity, respectively, and the system hardware consumes 6.9 W of power to process the SAR raw data.

FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging.

Synthetic aperture radar (SAR), which can generate images of regions or objects, is an important research area of radar. The chirp scaling algorithm (CSA) is a representative SAR imaging algorithm. The CSA has a simple structure comprising phase compensation and fast Fourier transform (FFT) operations by replacing interpolation for range cell migration correction (RCMC) with phase compensation. However, real-time processing still requires many computations and a long execution time. Therefore, it is necessary to develop a hardware accelerator to improve the speed of algorithm processing. In addition, the demand for a small SAR system that can be mounted on a small aircraft or drone and that satisfies the constraints of area and power consumption is increasing. In this study, we proposed a CSA-based SAR processor that supports FFT and phase compensation operations and presents field-programmable gate array (FPGA)-based implementation results. We also proposed a modified CSA flow that simplifies the traditional CSA flow by changing the order in which the transpose operation occurs. Therefore, the proposed CSA-based SAR processor was designed to be suitable for modified CSA flow. We designed the multiplier for FFT to be shared for phase compensation, thereby achieving area efficiency and simplifying the data flow. The proposed CSA-based SAR processor was implemented on a Xilinx UltraScale+ MPSoC FPGA device and designed using Verilog-HDL. After comparing the execution times of the proposed SAR processor and the ARM cortex-A53 microprocessor, we observed a 136.2-fold increase in speed for the 4096 × 4096-pixel image.

Algorithm/Hardware Codesign for Real-Time On-Satellite CNN-Based Ship Detection in SAR Imagery

Recently, the convolutional neural network (CNN)-based approach for on-satellite ship detection in synthetic aperture radar (SAR) images has received increasing attention since it does not rely on predefined imagery features and distributions that are required in conventional detection methods. To achieve high detection accuracy, most of the existing CNN-based methods leverage complex off-the-shelf CNN models for optical imagery. Unfortunately, this usually leads to expensive computational cost, which is hard to process in real time using resource-constrained devices deployed in the harsh satellite environment. In this article, we propose OSCAR-RT, the first end-to-end algorithm/hardware codesign framework for real-time on-satellite CNN-based SAR ship detection, which can simultaneously produce an accurate and hardware-friendly CNN model and an ultraefficient field-programmable gate array (FPGA)-based hardware accelerator that can be deployed on satellites. With the real-time on-satellite processing speed in mind, we start from a state-of-the-art compact CNN model for optical imagery. To eliminate the sharp decrease in the detection accuracy for SAR imagery, we analyze the discrepancy between the SAR domain and optical domain and propose to adapt the model by adjusting the output feature size to better detect relatively smaller objects in SAR imagery. To improve the detection speed, we propose to develop a fully pipelined interlayer streaming accelerator architecture, where all the layers of the CNN model can be concurrently processed using on-chip FPGA resources. To achieve this architecture, we first propose a hardware-guided, progressive, and structural pruning strategy, which is guided by our modeled hardware metrics and applies state-of-the-art coarse-grained and fine-grained filter pruning as well as mixed-precision quantization techniques. Moreover, to improve the reusability and portability of the hardware accelerator design, we develop a library of highly optimized CNN components in high-level synthesis, together with their performance and resource models. Finally, we map the pruned CNN model onto these hardware library components in a fully pipelined interlayer streaming fashion, by adjusting their parallelism factors to balance the execution of each layer and fit into the resource constraint. Experimental results using the adapted MobileNetV1, MobileNetV2, and SqueezeNet models on the widely used SAR ship detection dataset (SSDD) demonstrate the effectiveness of OSCAR-RT; for the MobileNetV1 model, it achieves an average precision of 94%, a detection speed of 652 frames/s on the Xilinx VC709 FPGA evaluation board while consuming about 5.8-W power.

FPGA Implementation of the Range-Doppler Algorithm for Real-Time Synthetic Aperture Radar Imaging

In this paper, we propose a range-Doppler algorithm (RDA)-based synthetic aperture radar (SAR) processor for real-time SAR imaging and present FPGA-based implementation results. The processing steps for the RDA include range compression, range cell migration correction (RCMC), and azimuth compression. A matched filtering unit (MFU) and an RCMC processing unit (RPU) are required for real-time processing. Therefore, the proposed RDA-based SAR processor contains an MFU that uses the mixed-radix multi-path delay commutator (MRMDC) FFT and an RPU. The MFU reduces the memory requirements by applying a decimation-in-frequency (DIF) FFT and decimation-in-time (DIT) IFFT. The RPU provides a variable tap size and variable interpolation kernel. In addition, the MFU and RPU are designed to enable parallel processing of four 32-bit which are transferred via a 128-bit AXI bus. The proposed RDA-based SAR processor was designed using Verilog-HDL and implemented in a Xilinx UltraScale+ MPSoC FPGA device. After comparing the execution time taken by the proposed SAR processor with that taken by an ARM cortex-A53 microprocessor, we observed a 85-fold speedup for a 2048 × 2048 pixel image. A performance evaluation based on related studies indicated that the proposed processor achieved an execution time that was approximately 6.5 times less than those of previous FPGA implementations of RDA processors.

Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.

Real-Time SAR Imaging Based on Reconfigurable Computing

Real-time synthetic aperture radar (SAR) image-processing systems on airborne or spaceborne platforms have constraints in terms of their power consumption, volume, and weight. In order to meet the requirements of these platforms and increase the flexibility of system application, this paper presents a SAR real-time imaging technology based on field-programmable gate array (FPGA) dynamic-reconfiguration technology. First, this technology is used to optimize the algorithm. The entire process flow is then classified into dynamic and static parts. The dynamic part implements numerical calculations, and the static part involves process control, data storage, and peripheral interface. Then, based on dynamic reconfiguration, the processing system is designed, including data interface, data storage, process control, and data operation. Finally, using flight data based on an unmanned aerial vehicle, the performance of the system is verified. The results indicate that the system needs to be run at a 200 MHz frequency for 85.9 s to process data with a size of 65536 ×65536. The system uses approximately 31.4% less resources and consumes 20% less power compared to the traditional method. These experimental results prove the effectiveness of the technology, laying the foundation for the design of high-performance real-time image-processing systems on airborne or spaceborne platforms.

A FAST APPROACH FOR THE LOG-CUMULANTS METHOD APPLIED TO INTENSITY SAR IMAGE PROCESSING

Abstract. Synthetic aperture radar (SAR) image processing and analysis rely on statistical modeling and parameter estimation of the probability density functions that characterize data. The method of log-cumulants (MoLC) is a reliable alternative for parameter estimation of SAR data models and image processing. However, numerical methods are usually applied to estimate parameters using MoLC, and it may lead to a high computational cost. Thus, MoLC may be unsuitable for real-time SAR imagery applications such as change detection and marine search and rescue, for example. Our paper introduces a fast approach to overcome this limitation of MoLC, focusing on parameter estimation of single-channel SAR data modeled by the G0I distribution. Experiments with simulated and real SAR data demonstrate that our approach performs faster than MoLC, while the precision of the estimation is comparable with that of the original MoLC. We tested the fast approach with multitemporal data and applied the arithmetic-geometric distance to real SAR images for change detection on the ocean. The experiments showed that the fast MoLC outperformed the original estimation method with regard to the computational time.

Kalman Filter Disciplined Phase Gradient Autofocus for Stripmap SAR

The phase gradient autofocus (PGA) and its improvements have been aimed to estimate the phase error exclusively from the phase of raw data. In this article, we introduced the Kalman filter (KF) into stripmap PGA (or phase curvature autofocus) by taking advantage of the continuous movement of the aircraft. The fundamental principle is to build a kinematic model and a measurement model to predict the phase curvature of the next subaperture, and to correct the measurement (phase curvature) of the current subaperture. The advantages of employing KF are as follows: 1) the inaccurate PGA estimation due to wrong target selection, serious phase error, or low signal-to-clutter ratio can be corrected by a well-maintained KF; 2) the prediction of the KF can be applied to the data of the next subaperture before phase estimation, to decrease the algorithm converge time, and to increase the estimation accuracy; and 3) KF disciplined PGA naturally fits the sequential processing needs and is capable of generating good phase error estimation in one execution. This helps real-time synthetic aperture radar (SAR) autofocus and motion compensation. The disciplining of the autofocus using KF is not restricted to PGA-based algorithm. It can be applied to other subaperture-based autofocus algorithms.

Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed.

Near real-time detection of abnormal vessels’ behaviours and vessel sourced pollution in Sri Lanka’s maritime zones: An investigation

The largest ecosystems on Earth are the oceans and it is essential to conserve and sustainably use these vital resources. Waste created by the activities on land and sea, leads to the destruction of the ecosystems. As Sri Lanka is an island in the Indian Ocean, with rights to a vast sea area that extends from the shores, it is mandatory to sustainably manage its marine environment. Regular, systematic reviews of the state of marine environment provide an understanding of the various aspects of marine environment change and the implications for sustainability. Space borne platforms provide Earth observation data collected by different types of sensors, at regular intervals for the management of Earth environment. Earth observation data acquired with Synthetic Aperture Radar (SAR) technology has become useful for the detection of marine pollution during the day and night, regardless of cloud covers. However, availability of near real-time data is important for prompt detection and response. Revisit time of a satellite is an important factor and satellite constellations are used to further improve revisit times, for example, Copernicus Sentinel-1 satellite constellation can provide SAR data with a high revisit time. The research aims to explore the use of near real-time Sentinel-1 SAR data with automated mechanisms, for information extraction that supports early detection of abnormal vessels’ behaviours and vessel sourced pollution in Sri Lanka’s maritime zones.

ShipDeNet-20: An Only 20 Convolution Layers and <1-MB Lightweight SAR Ship Detector

Existing most deep learning-based synthetic aperture radar (SAR) ship detectors have huge network scale and big model size. Thus, to solve these defects, we propose a lightweight SAR ship detector “ShipDeNet-20” with 20 convolution layers and < 1 MB (0.82 MB) model size. We use fewer layers and kernels, and depthwise separable convolution (DS-Conv) to ensure ShipDeNet-20’s lightweight attribute. Moreover, we also propose a feature fusion module (FF-Module), a feature enhance module (FE-Module), and a scale share feature pyramid module (SSFP-Module) to compensate for the raw ShipDeNet-20’s accuracy loss. Experimental results on the open SAR ship detection data set (SSDD) reveal that the accuracy and speed of ShipDeNet-20 are both superior to the other nine state-of-the-art object detectors. Finally, detection results on another two wide-region SAR images show ShipDeNet-20’s strong migration ability. ShipDeNet-20 is a novel SAR ship detector, built from scratch, lighter than others by tens even hundreds of times, helpful for real-time SAR application and future hardware transplantation.

Processing of Circular-Scanning SAR Data Using Deramping-Based Imaging Approach

With the circular-scanning mode, we can obtain real-time synthetic aperture radar (SAR) images of wide areas including both sides of the nadir line, which considerably benefits the terrain matching procedure. However, due to the continuous rotation of the antenna beam, the azimuth Doppler bandwidth of the circular-scanning SAR data will increase noticeably, which in turn will lead to the azimuth spectrum aliasing phenomenon. Existing approaches for the azimuth processing of the circular-scanning SAR data are based on the subaperture technique. However, when the pulse repetition frequency (PRF) is limited, these approaches result in the loss of azimuth resolution. In this letter, a new circular-scanning approach based on the deramping operation is proposed. The deramping operation could solve the problem of spectrum aliasing and the loss of azimuth resolution can be avoided. The performance of the proposed imaging approach is demonstrated by point targets simulation and real circular-scanning SAR data processing results.

From Theory to Application: Real-Time Sparse SAR Imaging

In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method.

Fast Barycentric-Based Back Projection Algorithm for SAR Imaging

The paper presents a novel fast barycentric-based back projection (BP) algorithm for radar imaging using the synthetic aperture radar (SAR) technique. The innovation in the proposed method is in the determination of the reverse virtual synthetic aperture beam for each point of the observed radar scene and in classifying which portion of raw radar data must be considered to complete imaging processing. This approach allows the classical well-known BP algorithm to be significantly accelerated, which is one of the most computationally demanding methods in SAR imaging. The proposed barycentric-based BP method gives the opportunity to control the resolution of the processed image by changing the width of the calculated beam. Moreover, the described solution is still highly parallelizable such as the classical BP algorithm. The solution can be adapted to a real-time SAR system with modern high-end computational units such as graphical processor units (GPUs) that support parallel computing. In this paper, the BP algorithm is clearly described with its barycentric-based modification. The proposed method is compared with a classical BP algorithm based on the measurement of simulated and real data.

Efficiency balanced matrix transpose method for sliding spotlight SAR imaging processing

Matrix transposition is a very critical operation in synthetic aperture radar (SAR) imaging systems. This study presents an improved corner turning memory solution for real-time SAR imaging processing. Based on the sub-matrix three-dimensional mapping method, the efficiency of matrix transposing is greatly increased. However, in sliding spotlight mode, a novel SAR imaging mode, the amount of raw data is increased too large to directly use the above method. In this study, a joint address mapping strategy for two pieces of double data rate 3 synchronous dynamic random-access memory is used to meet the needs of the amount of data and make up for the lack of original method. The experimental results show that this method can get an improved and balanced two-dimensional access efficiency for sliding spotlight mode SAR imaging processing.

Sequential Estimation of Dynamic Deformation Parameters for SBAS-InSAR

The synthetic aperture radar (SAR) interferometry (InSAR) has been developed for more than 20 years for historical surface deformation reconstruction. In particular, the onboard Sentinel-1/A/B satellite, newly planned NASA-ISRO SAR (NISAR), and Germany Tandem-L will continue to provide unprecedented SAR data with an increased number of acquisitions. However, processing of real-time SAR data has been experiencing challenges regarding the InSAR deformation parameter estimation over a long time with the small baseline subsets (SBAS) InSAR technology. We use sequential adjustment for the estimation of the deformation parameters, which uses Bayesian estimation theory under the least square criteria to inverse long time-series deformation dynamically. Finally, both simulated and real Sentinel-1A SAR data verify the performance of the sequential estimation. It can be regarded as an effective data processing tool in the coming era of SAR big data.