Synthetic Aperture Radar Raw Data Research Articles

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.

Doppler Factor in the Omega-k Algorithm for Pulsed and Continuous Wave Synthetic Aperture Radar Raw Data Processing

Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is the Omega-k algorithm, which operates in the two-dimensional frequency domain; therefore, in this paper, we used the Omega-k algorithm to produce SAR images and modify the Omega-k algorithm by adding the Doppler factor to improve the accuracy of SAR raw data processing obtained by the continuous wave and pulsed frequency modulated linear frequency modulated radar system from the surfaces of interest. On the other hand, for the case of unmanned aerial vehicle-borne linear frequency modulated continuous wave (LFM-CW) SAR systems, we added motion compensation to the modified Omega-k algorithm. Finally, the testing and validation of the developed Omega-k algorithm used simulated and real SAR raw data for both pulsed synthetic aperture and continuous wave radars. The real SAR raw data used for the validation of the modified Omega-k algorithm were the raw data obtained by the micro advanced synthetic aperture radar (MicroASAR) system, which is an LFM-CW synthetic aperture radar installed on board an unmanned aerial system and the raw data obtained by European remote sensing (ERS-2) satellite with a synthetic aperture radar installed.

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method.

Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

Synthetic Aperture Radar (SAR) is a well-established 2D imaging technique employed as a consolidated practice in several oil spill monitoring services. In this scenario, onboard detection undoubtedly represents an interesting solution to reduce the latency of these services, also enabling transmission to the ground segment of alert signals with a notable reduction in the required downlink bandwidth. However, the reduced computational capabilities available onboard require alternative approaches with respect to the standard processing flows. In this work, we propose a feasibility study of oil spill detection applied directly to raw data, which is a solution not sufficiently addressed in the literature that has the advantage of not requiring the execution of the focusing step. The study is concentrated only on the accuracy of detection, while computational cost analysis is not within the scope of this work. More specifically, we propose a complete framework based on the use of a Residual Neural Network (ResNet), including a simple and automatic simulation method for generating the training data set. The final tests with ERS real data demonstrate the feasibility of the proposed approach showing that the trained ResNet correctly detects ships with a Signal-to-Clutter Ratio (SCR) > 10.3 dB.

Enhanced range-doppler algorithm for SAR image formation

A Synthetic Aperture Radar (SAR) system is a powerful source of information due to its capability to operate day and night and in all weather conditions. It is essential for military and civilian purposes. Pulse-focusing is employed by the SAR system for achieving both long-range detection and fine-range resolution. The Range-Doppler Algorithm (RDA) is the most popular pulse-focusing technique for creating images from coherent SAR data. Pulse compression techniques for SAR signal processing targets seek to improve map resolution, lower peak power, and increase the Signal-to-Noise Ratio (SNR) of the detected object. This paper demonstrates an enhanced RDA algorithm that uses a modified pulse compression technique based on mismatched filter optimization where Quadratic Constrained Quadratic Program (QCQP) is used. The performance evaluation of the proposed method is performed using visual assessment and the standard image quality metrics peak-side-lobe-ratio (PSLR), impulse-response-width (IRW), and integrated-side-lobe-ratio (ISLR). Simulated and real SAR raw data are used for performance evaluation. The experimental results demonstrate the proposed method’s robustness, efficiency, and reliability in providing a higher-quality SAR image than the traditional RDA algorithm.

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.

Synergistic Use of Sentinel-1 and Sentinel-2 Based on Different Preprocessing for Predicting Forest Aboveground Biomass

Forest aboveground biomass (AGB, Mg/ha) measurement is one of the key indicators for carbon storage evaluation. Remote sensing techniques have been widely employed to predict forest AGB. However, little attention has been paid to the implications involved in the preprocessing of satellite data. In this work, considering the areas of low forest AGB in our survey plots, we explored the implications of employing atmospheric correction and speckle filtering with Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) to predict forest AGB using multiple linear regression (MLR) and extreme gradient boosting (XGBoost). In the present study, the types of plots examined included oaks (Quercus spp.), Chinese firs (Cunninghamia lanceolata), and Masson pines (Pinus massoniana), and all of the plots were investigated. Specifically, the feature variables related to S1 (dual polarization and texture measures) and S2 (spectral bands) were modeled individually, and 16 feature sets, including different combinations of S1 and S2 based on different preprocessing measures, were established using MLR and XGBoost. The results show that speckle filtering and atmospheric correction marginally influenced the capacity of the S2 spectral bands, the SAR dual-polarization backscatter, and the SAR-based textural measures in predicting the AGB in our survey plots. The associations between the speckle-filtered and unfiltered SAR images and the S2 Top-of-Atmosphere and Bottom-of-Atmosphere products were considerably strong. Additionally, the texture models generally showed better performances than the raw SAR data. Ultimately, the groups that only encompassed the S2 spectral bands were the best-performing groups among the 16 feature sets, while the groups that included only S1-based data generally performed the worst.

Processing of Airborne Microwave Photonic SAR Raw Data With Inaccurate RSF

Due to system instability and other reasons, the actual range sampling frequency of the system may deviate from the ideal value for the microwave photonic synthetic aperture radar (SAR). This deviation may lead to severe residual range cell migration (RCM) and even range defocus after imaging, which can seriously affect the image quality. To resolve this problem, this paper proposes an airborne microwave photonic SAR imaging algorithm based on inaccurate system parameter estimation. Firstly, the algorithm estimates and compensates for the range spatial-variant motion error to eliminate the effect of this motion error on the remaining RCM and range defocus. Secondly, based on the minimum entropy criterion of the image, we use the optimization model to estimate the actual range sampling frequency (RSF). Finally, the existing wide-beam autofocus method is used to correct the azimuth spatial-variant motion error. The simulation data and the measured data processing results verify the effectiveness of the proposed method.

SAR Based Sea Surface Complex Wind Fields Estimation: An Analysis over the Northern Adriatic Sea

Nowadays, sea surface analysis and monitoring increasingly use remote sensing, with particular interest in Synthetic Aperture Radar (SAR). Several SAR techniques exist in literature to understand the marine phenomena affecting the sea surface. In this work, we focus on the Doppler Centroid Anomaly (DCA), which accounts for the Doppler shift induced by sea surface movements. Starting from SAR raw data, we develop a processing chain to elaborate them and output the surface velocity map using DCA. The DCA technique has often been presented in the marine literature for estimating sea surface velocity, but more recently it has also been used to detect near-surface wind fields. This paper deals with estimating the sea surface wind field using Doppler information and SAR backscatter, combined with wind information provided by ECMWF and geophysical wind and Doppler model functions. We investigate the application of the approach in the coastal area of the northern Adriatic Sea (Northeast Italy). The test site is interesting, both for its particular orography, as it is a semi-enclosed basin largely surrounded by mountains, and for its complex meteorological phenomena, such as the Bora wind. Results obtained combining SAR backscatter and DCA information show an improvement in wind field estimation.

Improved Analytical Formula for the SAR Doppler Centroid Estimation Standard Deviation for a Dynamic Sea Surface

The existing formulas for the synthetic aperture radar (SAR) Doppler centroid estimation standard deviation (STD) suffer from various limitations, especially for a dynamic sea surface. In this study, we derive an improved version of these formulas through three steps. First, by considering the ocean wavenumber spectrum information, a new strategy for determining the number of independent samples of the sea wave velocity field is adopted in the new formula. This is contrary to the method used in the existing formulas, where the number of SAR geometric resolution cells is taken as the number of samples assuming that adjacent SAR resolution cells are statistically uncorrelated. Second, the pulse repetition frequency and Doppler bandwidth are decoupled in the new formula, unlike in the existing formulas where they are unchangeably related to each other. Third, the effects of thermal noise and Doppler aliasing are jointly quantified in a mathematically exact manner instead of being treated separately, as in the existing formulas. Comprehensive SAR raw data simulations for the ocean surface show that the new formula has a better performance in predicting the Doppler centroid estimation STD than the existing formulas.

Sea State Characterization Using Experimental Synthetic Aperture Radar Raw Data in Two-Dimensions and the Modified Fractal Signature Method

This paper presents a novel method for the characterization of the sea state using a set of raw experimental Synthetic Aperture Radar (SAR) data in two dimensions, i.e., in the “fast time” and “slow time” directions (as explained in the text) and the so-called “Modified Fractal Signature” (MFS) method. That is, experimental SAR radar signatures in the above two dimensions (i.e., “raw data” in the time domain) were provided to our research group by the Norwegian Institute of Defence (FFI Institute), Norway, which we processed and analyzed using the MFS method in a novel way, as presented in detail in this paper. The numerical results obtained here show an easy categorization of the sea surface as “calm sea” or “turbulent sea”, thus establishing a very promising technique for the characterization of sea state in real time, as described in detail in the text.

Design and Implementation of Synthetic Aperture Radar (SAR) Field-Programmable Gate Array (FPGA)-Based Processor

Synthetic aperture radar (SAR) is a unique imaging radar system that is capable of obtaining high-resolution images by using signal-processing techniques while operating in all weather and in the absence of a light source. The potential of SAR in a wide range of applications has led to new challenges in digital SAR processor design. On-board storage of SAR raw data is often not practical for real-time applications. The design of digital SAR processors is always restricted by the available space of the carrier system, data transfer rate, payload capacity and on-board power supplies. As reported in the literature, although customized hardware solutions could offer the desired performance, they are not feasible for low-volume production. This research aims to design and develop an efficient digital SAR processor by using field-programmable gate array (FPGA) with the consideration of hardware resources, processing speed and precision. In this paper, a hardware implementation of an FPGA-based SAR processor is presented. The implementation and architecture of the proposed SAR processor are highlighted in this paper. A MATLAB-based SAR processing range-Doppler algorithm (RDA) was developed as the benchmark for the development of an SAR processor. The target device, Altera Stratix IV GX FPGA EP4SGX230KF40C2, was selected for the design and implementation of an FPGA-based SAR processor. Comprehensive evaluations of the performance of the proposed SAR processor in terms of precision, timing performance and hardware resource utilizations are also presented. The proposed FPGA-based digital SAR processor achieves optimum performance in processing SAR signals for image formation. Evaluation shows that the designed SAR processor is capable of processing SAR images with ±1% difference error as compared to SAR images processed by MATLAB. The results also show a reduction in hardware usage via the implementation of an FPGA-based FFT/IFFT coprocessor. These promising results prove that the performance of the proposed processor is satisfactory and the achieved processing time, as well as the power consumption of the processor, outperformed existing implementations.

A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.

SAR Imaging From Azimuth Missing Raw Data via Sparsity Adaptive StOMP

Synthetic aperture radar (SAR) raw data missing occurs when the radar is interrupted for various reasons during the work. Different solutions have been proposed to this problem. In recent years, with the continuous deepening of the research on compressed sensing (CS), it has also been fully utilized in solving the problem of missing data. When using traditional greedy algorithms to recover missing data, we need to know the sparsity, but it is often unknowable in practice. This letter proposes to apply the sparsity adaptive segmented orthogonal matching pursuit (SAStOMP) algorithm to the recovery of SAR missing data. Simulation results show that the proposed method can recover missing SAR data under the condition of unknown sparsity and can adapt to a wider range of threshold parameters. It has good recovery performance for periodic and nonperiodic missing SAR raw data, thus improving SAR imaging results.

SAR Raw Data Simulation for Fluctuant Terrain: A New Shadow Judgment Method and Simulation Result Evaluation Framework

Synthetic aperture radar raw data simulation (SAR-RDS) is beneficial to the SAR system design, signal processing method verification, and radar parameter optimization. Most SAR-RDS methods are based on the flat terrain assumption. However, the fluctuant terrain in real scene will induce severe SAR beam occlusion effect and produce radar shadow, leading to incorrect RDS results. Thus, a dynamic elevation angle interpolation (DEAI) algorithm is proposed for SAR shadow judgment by considering the actual SAR working process. The key of the proposed DEAI algorithm is the 1-D EAI and shadow visualization update, which avoids the problem that the existing methods cannot judge the shadow of partial areas due to the insufficiently refined mesh grid or the mismatch of the judgment model. Moreover, an evaluation framework named as joint image and signal criteria (JISC) is proposed from the perspectives of SAR imaging and signal processing results to objectively evaluate the SAR-RDS results and solve the problem that the existing evaluation methods cannot be compatible with fluctuant terrain. Finally, the numerical experiment verified our theoretical analyses.

Pulse RFI Mitigation in Synthetic Aperture Radar Data via a Three-Step Approach: Location, Notch, and Recovery

In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique—i.e.,the singular value thresholding (SVT) algorithm—and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach.

Dually Supervised Track-Before-Detect Processing of Multichannel Video SAR Data

Track-Before-Detect (TBD) algorithm has been used to track weak moving target shadow in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multi-channel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution Range-Doppler (RD) spectra are used, and the clutter in RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum.

Time-Domain Notch Filtering Method for Pulse RFI Mitigation in Synthetic Aperture Radar

Synthetic aperture radar (SAR) often shares spectrum with other systems, such as radio, TV, cellular network, and so on, which are likely to produce radio frequency interference (RFI). Pulse RFI, which hinders SAR signal processing and image interpretation severely, is a common form of RFI and cannot be neglected. The simple and easy-to-implement frequency-domain notch filtering (FNF) method has been widely used to mitigate narrowband pulse RFIs. However, a well-known problem with abnormal sidelobe effect, which is caused by missing spectrum gaps due to the notch operation, is aroused when using FNF. In this letter, a novel time-domain notch filtering (TNF) is proposed. In the proposed method, pulse RFI occurrences are detected and notched by a simple log-ratio operator in a pulse by pulse manner. Then, missing-data iterative adaptive approach (MIAA) is performed to recover the notched signal to avoid ghosts. Experimental results via simulated and real L-band airborne SAR raw data validate the performance of the proposed method.

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Synthetic Aperture Radar (SAR) target classification is one of the largest branches of SAR image analysis. Despite the remarkable achievements of deep learning-based SAR target prediction algorithms, current object recognition algorithms are limited in terms of military applications. Acquisition and labeling of SAR target images are time-consuming and cumbersome. Obtaining adequate training data is also challenging in many cases. Deep learning-based models are always susceptible to overfitting because of insufficient training data. This limitation prevents them from being widely used to classify SAR targets. To overcome the problem of insufficient sampling and to learn more accurate representations for SAR image recognition, we propose a two way input of SAR images into a dual stream of DCNN. Concatenating two input SAR images representations was done using the restricted raw SAR data in order to extract the integral features from the 2 input SAR images representations for classification. The proposed methodology addressed the problem of insufficient sample in SAR target classification and improved classification accuracy without overfitting. Experimental results confirmed that the proposed method is effective in addressing the problem of insufficient sample in SAR target classification. This technique can be integrated into any SAR classification model based on convolutional neural networks (CNNs). The model MCDS-CNN results in a 99.1% recognition accuracy. Despite the limited availability of SAR image data from MSTAR, this approach provides good recognition results.

Retrieval of Ocean Surface Radial Velocities With RADARSAT-2 Along-Track Interferometry

This article provides a detailed presentation of the dual-aperture synthetic aperture radar (SAR) data processing scheme for the retrieval of ocean surface radial velocities.This scheme includes processing of raw SAR data, coregistration of along-track interferometric samples, magnitude and absolute phase calibration, and coherent averaging (multilooking). Several approaches for the absolute phase calibration are provided and compared. Some of the attempted approaches can potentially be used over open ocean (i.e., in scenes that do not contain any land). Main goal of attempting different approaches for the absolute phase calibration was to determine their relative performance, and determine the potential feasibility of some approaches over open ocean. The data processing scheme is applied to a RADARSAT-2 dual-channel MODEX-1 acquisition over a section of the Florida Current. For the dataset used in this study, different absolute phase calibration methods yielded similar radial velocity estimates, with relative mean and RMS differences within approximately 0.1 m/s. Estimates from SAR ATI were also compared to estimates from NASA’s OSCAR dataset. Comparison of visually identified currents showed close spatial overlap between estimates from the two sources. The RMS difference was found to be approximately 0.30 m/s. This difference can be attributed to the physical and temporal differences between the estimates.