Airborne Synthetic Aperture Radar Research Articles

An Integrated Raw Data Simulator for Airborne Spotlight ECCM SAR

Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors or electronic countermeasures (ECM) and suffer from motion measurement errors. In order to design and analyze SAR systems while considering such threats and errors, an integrated raw data simulator is proposed for airborne spotlight electronic counter-countermeasure (ECCM) SAR. The raw data for reflected echo signals and jamming signals are generated in arbitrary waveform to achieve pulse diversity. The echo signals are simulated based on the scene model computed through the inverse polar reformatting of the reflectivity map. The reflectivity map is generated by applying a noise-like speckle to an arbitrary grayscale optical image. The received jamming signals are generated by the jamming model, and their powers are determined by the jamming equivalent sigma zero (JESZ), a newly proposed quantitative measure for designing ECCM SAR systems. The phase errors due to the inaccuracy of the navigation system are also considered in the design of the proposed simulator, as navigation sensor errors were added in the motion measurement process, with the results used for the motion compensation. The validity and usefulness of the proposed simulator is verified through the simulation of autofocus algorithms, SAR jamming, and SAR ECCM with pulse diversity. Various types of autofocus algorithms were performed through the proposed simulator and, as a result, the performance trends were identified to be similar to those of the real data from actual flight tests. The simulation results of the SAR jamming and SAR ECCM indicate that the proposed JESZ is well-defined measure for quantifying the power requirements of ECCM SAR and SAR jammers.

A Novel Polarimetric Channel Imbalance Phase Estimation Method Based on the Rotated Double-Bounce Backscatters in Urban Areas

Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we contribute to proposing a new and convenient method for estimating the polarimetric channel imbalance phase at the transmitter and receiver, which can be used for both quad-pol and dual-pol SAR systems. We found a brand-new reference object in the urban area scene, namely the effective dihedrals. A statistical calculation method was proposed correspondingly, which obtained an effective estimation of the channel imbalance phases. The theoretical explanation of the proposed method was consistent with the statistical phenomena presented in the experiments. The technique was illustrated and verified through C-band SAR images, including GaoFen-3 (GF-3) data and Sentinel-1 data. The technique was also validated and successfully applied in airborne SAR data of P, L, S, C, and X bands. The estimation error could be within 7° when crosstalk items were less than −30 dB. The method realizes a fast and low-cost dual-polarization phase imbalance estimation and provides a new technical approach to supplement the traditional tropical-rainforest-based quad-pol system calibration. The method can be conveniently applied to the monitoring of polarization distortion parameters, ensuring good polarization SAR data quality.

Comparative Analysis of Single-View and Multi-View Airborne SAR Positioning Error and Course Planning for Multi-View Airborne SAR Optimal Positioning

Airborne synthetic aperture radar (Airborne SAR) can accurately locate key targets and regions by using the flight parameters of the aircraft and the relative position information between the aircraft and the target which can be obtained from the airborne positioning and orientation system (POS). In the course of flight, the aircraft will deviate from the ideal flight path due to atmospheric turbulence, which results in the calculation deviating from the actual target position. In order to improve the target positioning accuracy, it is necessary to study the influence of aircraft motion error on the target positioning error. This study discusses the positioning accuracy of single-view airborne SAR from the perspective of linear Range-Doppler algorithm (RDA), and deduces the multi-view airborne SAR positioning error transfer model based on the multi-view airborne SAR positioning model. Based on these, we analyze the main factors that affect the positioning accuracy of the two positioning methods in detail and quantitatively reveal the mechanism by which the multi-view airborne SAR positioning method can improve the target positioning accuracy compared with the single-view airborne SAR positioning method; we also solve the problem of course planning for multi-view airborne SAR optimal positioning. The research results can provide theoretical support for the analysis of factors influencing positioning error and the positioning error correction of airborne SAR.

The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic‐Boreal Methane Emissions

AbstractAreas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under‐represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ∼1.5 m deep and can be detected with synthetic aperture radar (SAR). To assess the importance of lake emergent vegetation (LEV) zones to landscape‐scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open‐water methane flux. First, we use Uninhabited Aerial Vehicle SAR data from the NASA Arctic‐Boreal Vulnerability Experiment to map LEV in 4,572 lakes across four Arctic‐boreal study areas and find it comprises ∼16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20–25]%, 1.0 [0.8–1.2]%, and 7.0 [5.0–12]% of lake areas in the Peace‐Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18–25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane‐relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic‐boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

TECHNOLOGY ON HIGH-ACCURACY DEM EXTRACTION FROM AIRBORNE INTERFEROMETRIC SAR

Abstract. Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.

Novel thermal treatment model to decontaminate airborne SARS Cov‐2 virus for residential and commercial buildings

In the COVID‐19 pandemic, control of airborne virus transmission is exceptionally challenging as it is attached to suspended particles in the air and stays for an extended time. Air contaminated with airborne viruses holds a substantial risk for household transmission. In this study, a novel thermal treatment system is modeled based on porous heating for the decontamination of airborne SARS‐Cov‐2. The model includes an air heating domain, insulated chamber, buffer tank and heat exchanger. The airborne SARS‐Cov‐2 is decontaminated when passing through a porous heat pipe and the insulated chamber for an anticipated dwelling period of more than 5 min at 105°C and further stored in a buffer tank for natural cooling. The obligatory decontaminated air is allowed in the residential space under ambient conditions passing through a heat exchanger. The numerical investigation of the porous pipe model at different L/D ratios with altered porosities aims to establish the best‐performing porous domain. Besides this, the buffer tank is intended to maintain buffer storage of the treated air and significant natural cooling before passing to the heat exchanger. A solar PV module is proposed to meet the prerequisite energy requirements of the equipped devices.

The cost-effectiveness of standalone HEPA filtration units for the prevention of airborne SARS CoV-2 transmission

ObjectiveAirborne infection from aerosolized SARS-CoV-2 poses an economic challenge for businesses without existing heating, ventilation, and air conditioning (HVAC) systems. The Environmental Protection Agency notes that standalone units may be used in areas without existing HVAC systems, but the cost and effectiveness of standalone units has not been evaluated.Study designCost-effectiveness analysis with Monte Carlo simulation and aerosol transmission modeling.MethodsWe built a probabilistic decision-analytic model in a Monte Carlo simulation that examines aerosol transmission of SARS-CoV-2 in an indoor space. As a base case study, we built a model that simulated a poorly ventilated indoor 1000 square foot restaurant and the range of Covid-19 prevalence of actively infectious cases (best-case: 0.1%, base-case: 2%, and worst-case: 3%) and vaccination rates (best-case: 90%, base-case: 70%, and worst-case: 0%) in New York City. We evaluated the cost-effectiveness of improving ventilation rate to 12 air changes per hour (ACH), the equivalent of hospital-grade filtration systems used in emergency departments. We also provide a customizable online tool that allows the user to change model parameters.ResultsAll 3 scenarios resulted in a net cost-savings and infections averted. For the base-case scenario, improving ventilation to 12 ACH was associated with 54 [95% Credible Interval (CrI): 29–86] aerosol infections averted over 1 year, producing an estimated cost savings of $152,701 (95% CrI: $80,663, $249,501) and 1.35 (95% CrI: 0.72, 2.24) quality-adjusted life years (QALYs) gained.ConclusionsIt is cost-effective to improve indoor ventilation in small businesses in older buildings that lack HVAC systems during the pandemic.

Enhanced compressed sensing autofocus for high-resolution airborne synthetic aperture radar

The limited accuracy of the navigation system leads to deviations between the real and measured trajectories in airborne synthetic aperture radar (SAR) which results in residual motion (phase) errors and seriously degrades the image quality. The residual errors are usually corrected by using autofocus methods. In this paper, we investigate the autofocus for a high-resolution airborne SAR system which employs stepped-frequency chirp (SFC) signal to achieve high range resolution. As a promising waveform to improve radar range resolution, the SFC signal is widely realized in practical systems. We characterize the backscattering from the scene, phase errors, and receiver noise with a linear matrix-based formulation. In addition, we propose a compressed sampling method that is directly applied to the scene and received signal to provide a sparse raw dataset. We describe the autofocus problem as a constrained optimization problem and propose a new compressed sensing (CS)-based autofocus method to solve it. This method iteratively estimates the phase error and reconstructs the high-resolution SAR image. The image reconstruction is based on the proximity algorithm which applies the shrinkage proximal operator to solve the problem in a computationally efficient manner. We validate the effectiveness of the proposed method using both simulated and real SAR images. Based on visual results and a variety of metrics, the proposed method outperforms the classical autofocus approaches. Also, it provides better or comparable results, with much lower computational complexity, in comparison to that of the recent CS-based autofocus methods.

Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling

The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are used. This paper presents a semi-empirical method to retrieve soil moisture over bare agricultural fields, based on effective roughness modeling, and applies it to a series of L-band fully-polarized SAR backscatter and bistatic scattering observations. The main advantage of using effective roughness parameters is that surface roughness no longer needs to be measured in the field, what is known to be the main source of error in soil moisture retrieval applications. By means of cross-validation, it is shown that the proposed method results in accurate soil moisture retrieval with an RMSE well below 0.05 m3/m3, with the best performance observed for the cross-polarized backscatter signal. In addition, different experimental SAR monostatic and bistatic configurations are evaluated in this study using the proposed retrieval technique. Results illustrate that the soil moisture retrieval performance increases by using backscatter data in multiple polarizations simultaneously, compared to the case where backscatter observations in only one polarization mode are used. Furthermore, the retrieval performance of a multistatic system has been evaluated and compared to that of a traditional monostatic system. The recent BELSAR campaign (in 2018) provides time-series of experimental airborne SAR measurements in two bistatic geometries, i.e., the across-track (XTI) and along-track (ATI) flight configuration. For both configurations, bistatic observations are available in the backward region. The results show that the simultaneous use of backscatter and bistatic scattering data does not result in a profound increase in retrieval performance for the bistatic configuration flown during BELSAR 2018. As theoretical studies demonstrate a strong improvement in retrieval performance when using backscatter and bistatic scattering coefficients in the forward region simultaneously, the introduction of additional bistatic airborne campaigns with more promising multistatic SAR configurations is highly recommended.

A robust statistic‐aided edge detector for SAR images based on RUSTICO

Speckle noise exists inherently due to the special imaging mechanism of synthetic aperture radar (SAR), which is a challenge for edge extraction. The traditional methods failed to make full use of statistical characteristics of SAR images to detect the edges, which results in the loss of weak edges. To address this issue, combined with the robust inhibition-augmented curvilinear operator, this letter proposes a novel edge detector based on the ratio of local statistics (ROLSS) for SAR images. In this letter, the proposed detector achieves the balance between edge resolution and speckle suppression through the proposed intensity calculation method. By taking advantage of image statistic characteristics, the proposed ROLSS edge detector can draw complete continuous edges of many shapes areas and textures without any post-processing, such as edge thinning, extension, and so on. Experiment results show that the proposed detector yields more accurate edge maps with fewer false and missed pixels, and it holds robustness both in the speckle-polluted simulated image and the real X-band airborne SAR image.

Parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA.

This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 × 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing.

Virtual Synthetic Aperture Radar Target Based on a Miniaturized Monostatic Digital Delay Transponder

The ice penetrating capability of low-frequency radar systems may offer a unique potential for the detection, localization, and guidance of exploratory ice-penetrating probes for cryospheric applications. As these probes exhibit a comparably small radar cross section (RCS), reliable detection, especially in high clutter surroundings, such as ice sheets, is prevented. As a solution, a miniaturized monostatic digital delay transponder is developed and tested regarding its functionality with an airborne synthetic aperture radar (SAR) sensor.

Motion Compensation/Autofocus in Airborne Synthetic Aperture Radar: A Review

Air- and spaceborne synthetic aperture radar (SAR) can provide a large number of high-resolution images for microwave remote sensing applications, such as geoscience and climate change research, environmental Earth system monitoring, and precision agriculture. Unlike spaceborne SAR, the flight trajectory of airborne SAR may deviate from its ideal straight course due to the influence of atmospheric turbulence, resulting in significant motion errors in radar raw data. If a standard imaging algorithm is directly used to process the data, the image focusing quality will be significantly degraded. Therefore, to improve image focusing quality, one typically needs to integrate standard imaging algorithms with an effective motion compensation (MoCo)/autofocus technique. In this article, we present a comprehensive overview of MoCo/autofocus methods for airborne SAR to articulate related research and promote further development.

Statistical synthesis of aerospace radars structure with optimal spatio-temporal signal processing, extended observation area and high spatial resolution

Using the statistical theory of optimization of radio engineering systems the optimal method of coherent radar imaging of surfaces in airborne synthetic aperture radar with planar antenna arrays is developed. This method summarizes several modes of terrain observation and it is fully consistent with current trends in the development of cognitive radars with the possibilities of radiation pattern restructuring in space and adaptive reception of reflected signals. Possible modifications of the obtained optimal method for the operation of high-precision airborne radars with a wide swath are presented. The idea is to create a theoretical basis and lay the foundations for its practical application in solving a wide range of issues of statistical optimization of methods and algorithms for optimal spatiotemporal signal processing in cognitive radar systems for the formation of both high-precision and global radar images. To implement the idea, the article highlights the concept of statistical optimization of spatio-temporal processing of electromagnetic fields in on-board cognitive radar systems, which will be based on the synthesis and analysis of methods, algorithms and structures of radar devices for coherent imaging, the study of limiting errors in restoring the spatial distribution of the complex scattering coefficient, the synthesis of optimal feedback for receiver and transmitter adaptations in accordance with a priori information about the parameters of the objects of study, the area of observation and the existing sources of interference. Objective is to develop the theory and fundamentals of the technical implementation of airborne radar systems for the formation of high-precision radar images in an extended field of view from aerospace carriers. Tasks. To reach the objective it is necessary to solve following tasks:– formalize mathematical models of spatiotemporal stochastic radio signals and develop likelihood functional for observation equations in which the useful signal, receiver internal noise and interference radiation of anthropogenic objects are random processes;– to synthesize algorithms for optimal processing of spatio-temporal stochastic signals in multi-channel radar systems located on aerospace-based mobile platforms;- in accordance with the synthesized methods, to substantiate the block diagrams of their implementation;– obtain analytical expressions for the potential characteristics of the quality of radar imaging and determine the class of probing signals and space scanning methods necessary to perform various tasks of radar surveillance;‒ to confirm some of the theoretical results by simulation methods, in which to reveal the features of the technical implementation of aerospace remote sensing radar systems.

Estimating Significant Wave Height from SAR with Long Integration Times

Synthetic aperture radar (SAR) is an important means of estimating significant wave height with obvious advantages of all-day, all-weather, high resolution and wide swath coverage. At present, the estimation methods of significant wave height are based on visible ocean waves in SAR images. However, due to the characteristic of long integration time for low-frequency SAR (such as P-band, L-band), the ocean waves are usually invisible in SAR images. In addition, in the case that there are multiple wave systems, significant wave height of only one wave system can be estimated for the reason that only a blurred wave system can be observed in SAR images. In order to solve the above two problems, a method of estimating significant wave height from SAR with long integration times is proposed in this paper. Firstly, each ocean wave system is refocused from single-look complex (SLC) data, respectively. Then, without any additional processing, the 180° ambiguity of wave propagation direction is removed based on the optimum focus setting. Finally, significant wave height is estimated in combination with azimuth cutoff, wavelength and propagation direction of ocean waves. This method is applied to two airborne SAR field data with long integration times. One case is that ocean waves are invisible in SAR images, the other is that there are two wave systems on the real ocean surface, but only one is visible in the SAR images. The results show that the proposed method can estimate significant wave height in the cases of invisible ocean waves and multiple ocean waves. The estimation results of significant wave height are compared with the European Centre for Medium-Range Weather Forecast (ECMWF) data, and the error is basically stable within 0.2 m, which verifies the effectiveness of the proposed method.

Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric and morphological properties. In addition, SAR acquisitions are weather-independent, providing a major advantage over optical imaging during periods of cloud cover. Most often, vegetation properties are measured using vegetation indices, including simple ratios of backscatter intensities. However, this approach can miss the scattered wave purity from vegetation targets. The scattered wave characteristic is an essential parameter as it is susceptible to the complex random structure of vegetation. In this study, we propose a novel dual-polarimetric SAR vegetation descriptor based on the co-pol purity component of the wave. We use this descriptor within a semi-empirical vegetation model to estimate soil moisture. It is noteworthy that this proposed method to retrieve soil moisture uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Therefore, the proposed method has the potential for operational scale monitoring applications at a global scale. This study validated different crop types (viz. canola, oats, forage, maize, soybeans, sugar beet, wheat, winter wheat, winter barley) over two test sites using airborne SAR data. The Root Mean Square Error (RMSE) values for soil moisture are within the range of 4.3% to 7.7% with the Pearson correlation coefficient r greater than 0.6. This novel descriptor will facilitate the operational use of dual-polarimetric GRD SAR data for vegetation monitoring and soil moisture estimation.The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

A Fast Algorithm for High Accuracy Airborne SAR Geolocation Based on Local Linear Approximation

Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar, which can work in any weather conditions and is often used for target positioning. Airborne SAR has the advantages of flexible scanning geometry and high spatial resolution compared with Spaceborne SAR, but the quality of SAR image and positioning accuracy is affected by air turbulence and mechanical vibration. In practical applications, it is vital to improve Airborne SAR’s geometric positioning accuracy without ground control points or other reference image information. When positioning a single SAR image with long-range, the Earth surface model is often used as an additional equation to solve the Range-Doppler (RD) model. However, the Earth surface equation is difficult to solve the target height accurately. This study proposed an Airborne SAR target positioning methodology based on a local linear approximation of the Earth surface equation. The proposed local Linear RD (L-RD) model establishes a linear constraint of the local elevation, and the iterative algorithm converges rapidly. To further improve the efficiency of geolocation, we propose a method to directly obtain the initial geolocation used for the iteration based on the geometric relationship. An iterative framework is also proposed to enhance positioning accuracy when DEM is available. The experimental results show that the proposed method can significantly improve the positioning accuracy and reliability of Airborne SAR positioning without control points compared to other methods.

C-band and L-band AirSAR image fusion technique using anisotropic diffusion

Image fusion is the process of amalgamation of essential information from multiple images into a single image resulting in a higher resolution image. It is an essential step of image augmentation in satellite imagery. Various frequency bands hold feature information related to a certain frequency range. Image fusion enables to superpose of co-registered images captured from different sensors to yield a better image with features from both the source images. Image fusion helps to get a more detailed single image than multiple images with different features. In our paper, we have presented an anisotropic diffusion-based fusion method for C-band and L-band AirSAR(Airborne Synthetic Aperture Radar) images. We found that the anisotropic diffusion method works better than old pansharpening techniques and works much faster and involves much fewer resources than Convolutional Neural Networks.

Blind NCS-Based Autofocus for Airborne Wide-Beam SAR Imaging

The improvement in azimuth resolution and/or the increase in the instantaneous beam width can result in a wide-beam autofocus problem in airborne synthetic aperture radar (SAR). Because of the assumption of central-beam approximation, the typical two-step motion compensation (MoCo) method and its extensions are unsuitable for the autofocus of wide-beam SAR imaging. In addition, existing MoCo algorithms for airborne wide-beam SAR imaging require high-precision inertial navigation data. To address these problems, we propose a wide-beam autofocus algorithm based on a blind nonlinear chirp scaling. First, based on the two-step MoCo method, the wide-beam autofocus in this study was modeled as a multiparameter optimization problem for an optimal overall imagery quality. Subsequently, we combine the well-known parametric and nonparametric methods in the SAR autofocus community to convert the multidimensional optimization problem into a two-dimensional optimization problem. Furthermore, we observe that this two-dimensional optimization problem can be further converted into two one-dimensional optimization problems, which can significantly increase the efficiency and robustness of autofocus processing. In addition, the proposed method is a full-aperture algorithm for wide-beam SAR autofocus, which can avoid the problems of image stitching and phase discontinuity compared with well-known subaperture autofocus algorithms (e.g., SATA, PTA, and FD). The processing results of the simulated and measured data verified the effectiveness of the proposed method.

2-D Spatially Variant Motion Error Compensation for High-Resolution Airborne SAR Based on Range-Doppler Expansion Approach

Motion errors are inevitable in real-world scenarios and introduce significant phase errors in airborne synthetic aperture radar (SAR) imaging. Generally, these errors consist of the cross-coupling and spatially variant components. Cross-coupling errors can usually be eliminated by motion compensation (MoCo), whereas the latter are seldom addressed, which deteriorate the imaging qualities, especially for the high-resolution cases. To solve the problem, a novel approach based on 2-D range-Doppler expansion is proposed. First, an accurate range equation of the aircraft is obtained based on the inertial navigation system (INS) data. Then, the range-Doppler expansion corresponding to the slant range and Doppler centroid are performed, by which the echo signal is decoupled into two spatially variant parts in range and azimuth directions. Finally, the chirp-z transforms (CZTs) are employed to remove the range and azimuth spatial variations introduced, respectively, by the cross-track and along-track errors. Different from the conventional methods, our approach can greatly decrease the cross-coupling and spatially variant effects brought by motion errors in high-resolution cases. Computer simulation and real data experiments demonstrate the effectiveness of the proposed approach.