Range Migration Algorithm Research Articles

Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure.

Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.

Ultra-Wide band radar and its applications

This paper uses MATLAB software to demonstrate the performance of UWBSAR and conducts a comparative study with data obtained from conventional radar. It compares various antennas that support UWB, such as Vivaldi, MIMO, and monopole antennas, analyzed using SIMULINK. The paper discusses the design of UWBSAR to provide a comprehensive analytical picture of the processed images. The focus is on frequency domain analysis in general and the Range Migration Algorithm (RMA) in particular. The data obtained after signal processing is recorded to estimate the crossrange resolution, which is then compared with conventional SAR. The cross-range resolution estimated using UWBSAR is found to be lower than that of conventional radar, proving that UWBSAR is a better alternative for obtaining sharper images in short-range applications. High-quality images are reconstructed using a combination of UWB radar, SAR processing, and proposed algorithms to improve image quality. The investigation includes positive image generation to enhance sharpness and near-field imaging procedures. This paper also describes Ultra-Wideband (UWB) Synthetic Aperture Radar (SAR) and its application in operating at low frequencies to detect obscured targets beneath foliage. While it has obvious military applications, it also has civilian uses, such as in geophysical studies and weather forecasting. Several applications have been identified for both military and civilian environments.

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

3-D Millimeter Wave Fast Imaging Technique Based on 2-D SISO/MIMO Array

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms.

Accuracy Improvement of High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar Imaging with Low Pule Repetition Frequency

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method.

Quantum range-migration-algorithm for synthetic aperture radar applications

The 3D range-migration algorithm (RMA) and its 2D equivalent, the omega-k algorithm, are employed in a wide range of applications where reconstruction of synthetic aperture data is required, from satellite radar imaging of planets over seismic imaging of the earth crust, down to phased-array ultrasound and ultrasonic application, and recently in-line synthetic aperture radar for non-destructive testing. These algorithms are based on Fourier transforms and share their time-complexity. This limits highly-resolved measurement data to be processed at high speeds which would be advantageous for modern production feed lines. In this publication, we present the development and implementation of the RMA on a quantum computer that scales favourably compared to the time complexity of the classical RMA. We compare reconstruction results of simulated and measured data of the classical and quantum RMA. Hereby, the quantum RMA is run on a quantum simulator backend as well as on IBM’s Q System One quantum computer. The results show that real world applications and testing tasks may benefit from future quantum computers.

A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

A Fast 3-D Chirp Scaling Imaging Technique for Millimeter-Wave Near-Field Imaging

In this article, a novel 3-D imaging method based on the chirp scaling algorithm (CSA) is proposed for millimeter-wave near-field imaging. We deduce the theoretical formula of CSA applied to multiple-input–multiple-output (MIMO)/single-input–single-output (SISO) array imaging and then analyze the computational complexity and phase error. Compared to the classical Fourier-based algorithm, such as range migration algorithm (RMA), they can replace the redundant interpolation with the phase multiplication. Meanwhile, it can handle the large relative bandwidth system compared with frequency scaling algorithm (FSA). It greatly improves imaging speed and broadens the application scenarios. The rearrangement operation of the MIMO wavenumber spectrum is also accelerated by the sparse matrix multiplication. To further reduce the computation time, we perform the direct rearrangement of the MIMO wavenumber spectrum before phase compensation. It works well but also sacrifices part of the signal-to-noise ratio (SNR), so it may not be suitable for the weakly scattering targets. The simulation data of a 2-D uniform array with the large number of antennas and a published dataset are used to verify the effect of the proposed algorithm. The simulation and experiment results show that the proposed method can effectively improve the computational efficiency.

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

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

Analysis and Compensation for Systematical Errors in Airborne Microwave Photonic SAR Imaging by 2-D Autofocus

In the area of synthetic aperture radar (SAR), the ultra-high resolution is always a continuous pursuit for researchers. To realize ultra-high resolution, microwave photonics (MWP) technology is an excellent solution since it has advantages of low transmission loss, ultra-wide bandwidth (UWB), etc. However, with the improvement of resolution, new problems arise as the impact of more systematical errors becomes non-negligible. For example, the synchronization error between the transmit channel and the mixing channel, unknown system delay during signal reception, and fluctuation of frequency for UWB signals. These factors will bring degradation of the focusing quality of SAR images together with the motion errors and the trajectory measurement errors, not only cause residual range cell migration (RCM) and azimuth phase error (APE), but also higher-order phase error along the range frequency dimension. In this paper, we discuss the adverse influence of the above factors and give a detailed analysis of the 2-D phase error of the coarse focused image processed by range migration algorithm (RMA). Then, to compensate for the above unknown systematical errors, a 2-D autofocus method is proposed and the effectiveness is validated by experiments on both simulated and real data.

Efficient range migration algorithm for near-field MIMO array imaging

This paper proposes an efficient range migration algorithm (ERMA) for near-field multiple-input multiple-output (MIMO) array-based microwave imaging. For the usual imaging algorithms implemented in the spatial frequency domain for MIMO arrays, the multi-dimensional Fourier transforms (FTs) are first performed with respect to transmitters and receivers, respectively, to transform the echo data in the spatial domain to those in the spatial frequency domain. After performing the matched filtering and the Stolt interpolations usually in a five-dimensional (5-D) domain for common two-dimensional (2-D) planar MIMO arrays, the high dimensional data needs to shrink into a three-dimensional (3-D) domain. The processing in a 5-D domain is time-consuming and requires huge memory storage. The proposed algorithm moves the dimension reduction in advance of the matched filtering and interpolations. To do this, we first perform data completion in respect of the undersampled data. A phase calibration method is employed to preserve precision. In so doing, the higher dimensional matched filtering and interpolations are eliminated, which results in a significant increase in imaging speed. Besides, the ERMA can enhance the resolution of the imaging results, because the dimension reduction discards the convolution between the transmit and receive spectra. Numerical simulations are demonstrated to verify the efficacy and performance of ERMA.

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete.

In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

Walk-through millimeter wave imaging testbed based on double multistatic cross arrays.

A walk-through millimeter wave imaging testbed using double multistatic cross arrays is presented. The imaging testbed consists of a vector network analyzer, a power amplifier, a low-noise amplifier, 36 SP6T electrical switchers, and double homemade multistatic cross arrays placed on both sides of the imaging area. The imaging algorithm based on the range migration algorithm is deduced, and the imaging performance is analyzed. The metallic ball experimental results show that the imaging resolution is close to the theoretical value, and demonstrate the imaging feasibility of the testbed working in mutual mode.

Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation

In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

Fourier Transform of SAR Data Cube and 3-D Range Migration Algorithm

To develop a 3-D imaging synthetic aperture radar(SAR) algorithm, a theoretical expression for a Fourier transform of data cube is required. In this article, we introduce a function representing the Fourier transform of 3-D SAR data cube. The function is derived using the method of stationary phase and similar to the one used for 2-D data matrix. For verification and evaluation, a 3-D range migration algorithm using this function for bulk compression is examined. The simulation results based on the parameters of an experimental terahertz indoor SAR testbed show that the algorithm can well focus the 3-D image and, hence, verify the derived function. The image quality assessments such as spatial resolutions and peak sidelobe ratio are used to further evaluate the function and the algorithm. The measured results are compared with the ones provided by a backprojection algorithm.

Phase-Based Range-Enhanced Millimeter-Wave Imaging Technique for Multistatic Planar Array

A millimeter-wave (mmW) range enhancement imaging technique for multistatic planar arrays is presented. The echo in an active mmW imaging system that contains both amplitude and phase information, and the latter can be potentially exploited for improving imaging performance in the range direction. With the help of a two-dimensional (2-D) phase unwrapping scheme, and the multi-input-multi-output range migration algorithm (MIMO-RMA) for a multistatic planar array, along with the requisite pretreatment processes such as target extraction and phase error correction for the reconstructed image region, a reconstructed image having a finer range profile could be achieved. The underlying principle of the phase information exploitation to achieve finer range distance information is discussed, the range enhancement imaging implementation steps are outlined, and the necessary pretreatment processes in simulation and experiment are introduced. The efficiency of the proposed approach is analyzed theoretically and demonstrated experimentally. Numerical simulation and experimental results show that the reconstructed image using the new range enhancement imaging technique contains the promised finer range distance information.

Fourier-Based Near-Field Three-Dimensional Image Reconstruction in a Multistatic Imaging Structure Using Dynamic Metasurface Antennas

Due to physical layer compression, and consequently, the failure to produce uniform radiation patterns, it is not possible to develop fast Fourier-based image reconstruction algorithms using the raw measurements collected from metasurface antennas. An effective solution in the literature is a sub-wavelength sampling of the aperture. However, this solution is currently limited to a panel-to-probe model which requires a mechanical raster scan. On the other hand, existing works are based on time-division multiplexing. This means that only a single transmit/receive channel is active in each time slot. In this paper, we introduce a panel-to-panel model in a multistatic structure. Based on this model, two pre-processing (for two different individually measured signal and combined measured signal (CMS) scenarios) are derived to convert the raw measurements into the space-frequency domain. Then, by using the output data from the pre-processing stage and according to the configuration of the introduced imaging system, the range migration algorithm is adapted. The importance of the proposed solution for the CMS scenario is that, for the first time, it demonstrates the capability of using dynamic metasurface antennas diversity to achieve simultaneous data acquisition. The performance of the proposed approach is compared with state-of-art works in terms of reconstructed image quality and computational complexity using numerical simulations and analytical discussions.

Frequency Domain Image Reconstruction for Imaging With Multistatic Dynamic Metasurface Antennas

Dynamic metasurface antennas (DMAs) have recently been introduced as a computational imaging (CI) platform that offers significant advantages over conventional imaging systems. Such antennas are able to produce tailored radiation patterns that are later used to encode the scene’s information into a few measurements. However, since the signal is compressed, image reconstruction in the frequency domain using the conventional range migration algorithm (RMA) cannot be directly applied to CI-based apertures synthesized with DMAs. More sophisticated algorithms need to be developed to overcome this limitation, which transfers the complexity of such systems to the software layer. This paper proposes a new multistatic DMA RMA (MD-RMA) technique that achieves decompression of the compressed signals collected from multistatic DMA apertures and processes the decompressed data in the Fourier domain followed by a multistatic-to-monostatic conversion. Simulation results verify that the proposed approach can produce high quality 3D radar images, which alleviates the need to mechanically move the aperture. Moreover, since the image reconstruction is fully conducted in the frequency domain, leveraging the fast Fourier transform (FFT) algorithm, the complexity of the algorithm, and thus the execution time, is significantly reduced in contrast with conventional spatial domain algorithms.

Joint Physics and Data Driven Full-Waveform Inversion for Underground Dielectric Targets Imaging

To better reconstruct underground targets based on ground-penetrating radar (GPR) data, this paper proposes a joint physics and data driven full-waveform inversion (PDD-FWI) scheme. This scheme combines a physics-based non-iterative approach and a data-driven deep neural network (DNN) to reconstruct target location, shape and permittivity accurately. Firstly, the normalized range migration algorithm (RMA) is introduced to extract the target contour and location information, which not only improves the robustness of the proposed scheme, but also ensures adaptability to different GPR equipment. Then, the GPR dielectric target reconstruction network (GPRDtrNet) is developed based on the improved U-net structure, including reducing network layers and adding multi-scale additive spatial attention gates and skip-connection structures. Compared with previous DNN-based reconstruction methods, GPRDtrNet has the advantages of small data requirement, high accuracy, strong generalization and noise tolerance. Finally, the simulated and real dataset containing kinds of targets is constructed to train and test GPRDtrNet. The results show that the proposed method can reconstruct underground dielectric targets accurately with high robustness and noise tolerance.