Multichannel Synthetic Aperture Radar Imaging Research Articles

A HRWS SAR Motion Compensation Method with Multichannel Phase Correction

The multichannel synthetic aperture radar (SAR) possesses the capability to acquire high-resolution, wide-swath SAR imagery, which has great potential for application. However, similar to traditional single-channel SAR systems, it suffers from imaging quality degradation due to motion errors. Many motion compensation algorithms have been used to improve the quality of single-channel SAR images, while fewer studies have been conducted on multichannel SAR motion compensation methods. The sub-image motion compensation method utilizes the single channel motion errors to perform multichannel motion errors compensation, considering that multiple channels have the same phase errors. To improve the quality of multichannel SAR imaging when multiple channel motion errors are inconsistent, this paper proposes a motion compensation method with multichannel phase correction for HRWS SAR. First, the method derives the phase errors estimation model via maximum sharpness to simultaneously estimate multichannel phase. Then, it compensates for the motion errors of all channels during backprojection imaging. The inconsistent motion errors of multiple channels can be compensated by estimating the phase errors of all channels, improving the image quality. The channel phase errors can be corrected while compensating for the motion errors. Moreover, the experimental results of point targets and complex scenes validate the effectiveness of the proposed method.

The Impact of Antenna Array Calibration Errors on MIMO and Multi-Channel Synthetic Aperture Radar Imaging

In radar imaging, any lateral resolution is either attained using antenna arrays or by synthetic aperture radar (SAR) principles. Recently, the combination of both approaches to a multi-channel SAR (MCSAR) has gained increased attention. However, antenna arrays are impaired by unknown calibration errors, particularly phase errors, amplification deviations, mutual coupling, and phase center position deviation of the antenna element. Since, first, established calibration procedures are often non-ideal and, second, the calibration parameters usually vary over time due to aging and changes in temperature, calibration errors distort the measurements during the radar operation. Therefore, current research focuses on finding new practical calibration procedures. However, hardly any research has been conducted to analyze the basic effects which erroneous calibration parameters have on the reconstructed image in MCSAR or MIMO radar. In this work, we analyze the effects of multiple calibration error types on MCSAR and MIMO radar imaging. Both measurement principles lead to periodic error patterns, resulting in deterministic distortions. The derived relations can be used to precisely predict the position and number of ghost targets caused by calibration errors for known measurement setups and targets. For this, no information about the actual calibration errors is required. Furthermore, worst-case distortion levels are provided for different bounded errors allowing rough estimates of the impact of different error types on the image's distortion level. The findings are validated via measurements using a 76 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,$</tex-math></inline-formula> GHz FMCW MIMO radar.

Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

Due to the low-cost and compact size, millimeter-wave radar is gaining increasing attention in automotive applications. Compared with the array imaging technique, synthetic aperture radar (SAR) takes advantage of high resolution, low system complexity, and is an important powerful tool in automotive radar imaging. In this paper, a high-resolution MIMO SAR imaging algorithm is proposed in the framework of Doppler-division multiplexing (DDM), where a novel multi-channel back-projection (BP) approach is presented for coherent multi-channel image integration. In the scheme, the DDM is applied on transmitter and Doppler filtering is employed to separate echoes of different transmitters on one receiver. Then the proposed multi-channel BP algorithm is divided into two parts. The first step corresponds to the BP SAR imaging on each channel, effectively dealing with the problem of near-field and wide-angle focusing. As follows, digital beamforming (DBF) and beampattern synthesis based on alternating direction method of multipliers (ADMM) is used for multi-channel SAR image coherent fusion. Experiments using real-measured data from an automotive radar system with a two-transmitter-eight-receiver array validate the effectiveness of the proposed algorithm.

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

One of the major threats to marine ecosystems is pollution, particularly, that associated with the offshore oil and gas industry. Oil spills occur in the world's oceans every day, either as large-scale spews from drilling-rig or tanker accidents, or as smaller discharges from all sorts of sea-going vessels. In order to contribute to the timely detection and monitoring of oil spills over the oceans, we propose a new Multi-channel Deep Neural Network (M-DNN) segmentation model and a new and effective Synthetic Aperture Radar (SAR) image dataset, that enable us to emit forewarnings in a prompt and reliable manner. Our proposed M-DNN is a pixel-level segmentation model intended to improve previous DNN oil-spill detection models, by taking into account multiple input channels, complex oil shapes at different scales (dimensions) and evolution in time, and look-alikes from low wind speed conditions. Our methodology consists of the following components: 1) New Multi-channel SAR Image Database Development; 2) Multi-Channel DNN Model based on U-net and ResNet; and 3) Multi-channel DNN Training and Transfer Learning. Due to the lack of public oil spill databases guaranteeing a correct learning process of the M-DNN, we developed our own database consisting of 16 ENVISAT-ASAR images acquired over the Gulf of Mexico during the Deepwater Horizon (DWH) blowout, off the west coast of South Korea during the Hebei Spirit oil tanker collision, and over the Black Sea. These images were pre-processed to create a 3-channel input image IM = {IO, IW, IV}, to feed in and train our M-DNN. The first channel IO represents the radiometric values of the original SAR Images, the second and third channels are derived from IO; in particular, IW represents the output of the wind speed estimation using CMOD5 algorithm (Hersbach et al., 2003) and IV represents the variance of IO that incorporates texture information and at the same time encapsulates oil spill transition regions. IM channels were split and linearly transformed for data augmentation (rotation and reflection) to obtain a total of 80,772 sub-images of 224 × 224 pixels. From the entire database, 80 % of the sub-images were used in the DNN training process, the remaining (20 %) was used for testing our final architecture. Our experimental results show higher pixel-level classification accuracy when 2 or 3 channels are used in the M-DNN, reaching an accuracy of 98.56 % (the highest score reported in the literature for DNN models). Additionally, our M-DNN model provides fast training convergence rate (about 14 times better on the average than previous works), which proves the effectiveness of our proposed method. According to our knowledge, our work is the first multi-channel DNN based scheme for the classification of oil spills at different scales.

Spaceborne Multichannel SAR Imaging Algorithm for Maritime Moving Targets

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Phase Bias Estimation and Imaging for High-Squint Multichannel SAR in Azimuth

High-squint multichannel synthetic aperture radar (SAR) in azimuth can overcome the constraint of minimum antenna area in the traditional single-channel classical SAR system, and thereby it would be much more flexible to achieve high-resolution and wide-swath (HRWS) imaging than broadside SAR. However, for the high-squint multichannel SAR imaging mode, the squint angle causes Doppler centroid varying with the range frequency, which can further result in much more complicated problem of the spectral folding after down-sampled with low PRF. Meanwhile, the Doppler centroid frequency is indispensable for the estimation of phase bias and unambiguous signal reconstruction among received channels. In this paper, the impact of high-squint angle on the aliased Doppler spectrum and Doppler centroid is analyzed in detail. Then, a novel approach to phase bias estimation as well as the underlying signal processing in the case of high-squint multichannel SAR imaging mode in azimuth is proposed. Simulation results validate the effectiveness of the proposed processing flowchart for high-squint multichannel SAR.

Multi-Channel SAR Imaging on Cruising Ships with Sub-Orbital Spaceplane

A multi-channel synthetic aperture radar (SAR) on board a spaceplane orbiting near the top of the atmosphere is proposed to acquire images of cruising ships. Low pulse repetition frequency (PRF) is required for high-resolution wide-swath (HRWS) imaging, leading to inevitable problems of azimuth spectrum aliasing (ASA) and azimuth Doppler ambiguity (ADA). In this work, we propose a phase matching technique to solve the ASA problem in restoring the azimuth spectrum. A multi-stage compressive-sensing (CS) technique is also proposed to solve both ADA and ASA problems. Five similar types of cruising ship are simulated to verify the efficacy of the proposed approach, at different levels of signal-to-noise ratio. Indices of geometry match, intensity match, and structural similarity are used to identify different ships from the acquired SAR images.

An Efficient Multichannel SAR Channel Phase Error Calibration Method Based on Fine-Focused HRWS SAR Image Entropy

Azimuth multichannel synthetic aperture radar (SAR) is a useful approach to solving minimum antenna area constraint and realizing high resolution and wide swath (HRWS) imaging by multichannel signal reconstruction. However, channel phase error will significantly degrade the reconstruction performance and leads to azimuth ghost. This article presents an efficient minimum entropy channel error estimation method based on fine-focused SAR image. The multichannel SAR images are obtained by using Range-Doppler imaging algorithm applied for preprocessed data of each channel. Then, the reconstruction and the residual range cell migration and residual second range compression compensation are performed to achieve multichannel fine-focused SAR images. After overlapping these multichannel fine-focused SAR images and iteratively calibrating the channel phase error, fine-focused HRWS SAR image can be achieved. Therefore, the SAR image used in the channel error estimation process can directly obtain the fine focused HRWS SAR image after minimum entropy iteration without additional imaging operations. Compared with the minimum entropy method by using the coarse-focused SAR image based on azimuth deramp, redundant reconstruction, and imaging operations are avoided. Thus, error estimation and imaging processing procedures are optimized. Processing efficiency improvement are analyzed, and simulation and acquired data processing verify the effectiveness of the proposed method.

A Joint Azimuth Multichannel Cancellation (JAMC) Antibarrage Jamming Scheme for Spaceborne SAR

The azimuth multichannel synthetic aperture radar (SAR) increases the swath width by reducing the pulse repetition frequency (PRF), thus providing an effective scheme for realizing high-resolution and wide-swath (HRWS) imaging simultaneously. However, the quality of multichannel SAR images will suffer from electromagnetic jamming in the electronic antagonism environment. The existing channel cancellation methods not only require high positioning accuracy of the jammer and high PRF of the SAR system, but also remain periodic dark stripes, which reduce the readability of the SAR images. To solve these problems, this article proposes a scheme for multichannel SAR to locate the barrage jammer and suppress jamming signals. Firstly, by establishing the least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bm {L}}^{1}}$</tex-math></inline-formula> -norm optimization model on the echoes after channel cancellation, the location of the jammer in both azimuth and range directions can be accurately estimated. Then, based on the estimated jammer position, a joint cancellation filter is constructed to remove the jamming signal from the contaminated multichannel SAR data. And the reconstruction filter is designed according to the characteristics of the remaining signal, so that the SAR system reduces the requirement for high PRF. Finally, for the case of dark strips, this paper calculates the image amplitude equalization coefficient by deducing the azimuth modulation function, and a uniform HRWS SAR image without jamming is acquired. Results of simulated and measured data validate that the proposed method can effectively remove jamming signals and obtain the HRWS SAR images.

Estimating the velocity of moving targets in multichannel SAR images by fourier analysis and optimally-weighted velocity averaging

ABSTRACT A scheme is developed to estimate the radial velocities of moving targets in multichannel synthetic aperture radar (SAR) images. A moving target generally consists of multiple pixels. First, a radial velocity estimate is obtained from each of these pixels via the spatial Fourier analysis, which is more robust against the clutter than the interferometry method because the target signal and the clutter are separate in the spatial frequency domain. Then, the radial velocity estimate of the moving target is obtained by a weighted averaging of the radial velocity estimates, and the weight of each radial velocity estimate is designed to minimize the mean square error (MSE) between the estimate and the truth. This optimally weighted averaging scheme is also more accurate than the optimally weighted averaging scheme related to the interferometry method, because the weight of each radial velocity estimate is designed according to the intensity of the target signal instead of the intensity of the target signal and the clutter. The effectiveness of the proposed scheme is demonstrated by the experimental results of simulated and real SAR data.

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

High squint multichannel SAR imaging algorithm for high speed maneuvering platforms with small-aperture

For high speed maneuvering platforms, multichannel synthetic aperture radar (SAR) can realize wide-swath imaging more flexibly at a high squint. In this mode, the signal reconstruction and imaging is a challenging task because the direction of the channel array vector is time-variant and inconsistent with the radar velocity vector. In this paper, the properties of space time spectrum are analyzed in detail at first. It is found that the space time spectrum of the signal is irregular, in which the space time spectral lines are nonlinear and there is a massive Doppler spectrum shift. Therefore, a range-dependent signal reconstruction method based on space time spectrum correction is proposed to obtain the unambiguous Doppler spectrum. For wide-swath data processing, an improved Omega-K approach based on time domain spectrum compression is further proposed to obtain a well-focused image. A modified Stolt mapping is used to address the range variations of range cell migration (RCM). Subsequently, a time domain spectrum compression function is used to eliminate the time domain aliasing of small-aperture data without zero-padding. Simulation results and real data processing are presented to validate the proposed algorithm.

CV-GMTINet: GMTI Using a Deep Complex-Valued Convolutional Neural Network for Multichannel SAR-GMTI System

Motivated by recent advances in deep learning, a novel deep complex-valued convolutional neural network (CV-CNN)-based method is proposed for ground moving target indication (GMTI) in a multichannel synthetic aperture radar (SAR) system. The proposed method integrates the SAR-GMTI task into a blind inverse problem solved by a deep CV-CNN named CV-GMTINet. To take advantage of the amplitude and phase information of complex multichannel SAR images, both feature maps and network parameters are extended into the complex domain. The proposed CV-GMTINet is designed by adopting complex-valued residual dense blocks (CV-RDBs) to adaptively learn complex hierarchical features. The trained CV-GMTINet, as a GMTI processor, can be applied to complex multichannel SAR images to discriminate moving targets from stationary clutter and refocus the moving target images simultaneously. Experiments on TerraSAR-X data show that the proposed method achieves significant improvements over existing state-of-the-art GMTI methods in both detection performance and refocusing accuracy, especially for the slow-moving target and the moving target with only along-track velocity.

Ambiguities Suppression for Azimuth Multichannel SAR Based on ${L_{2,q}}$ Regularization With Application to Gaofen-3 Ultra-Fine Stripmap Mode

The azimuth multichannel synthetic aperture radar (SAR) technology is capable of overcoming the minimum antenna area constraint and achieving high-resolution and wideswath (HRWS) imaging. Generally speaking, the pulse repetition frequency (PRF) of the spaceborne multichannel SAR systems should satisfy the azimuthal uniform sampling condition, but it is sometimes impossible due to the limitation of radar system timing conditions, which is often referred as “coverage diagram.” For the Gaofen-3 system, the PRF of each channel at some beam positions is slightly less than that of uniform sampling in the dual-channel mode, leading to the nonuniform undersampling, hence, resulting the azimuth ambiguities in the recovered images. Although the ambiguous energy in Gaofen-3 images is not high in general, it is still noticeable amid surrounding weak clutters of strong targets. In this article, a novel multichannel SAR imaging method for nonuniform undersampling based on L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,q</sub> regularization (0 <; q ≤ 1) is proposed. By analyzing the reasons of azimuth ambiguities in the multichannel SAR system, the imaging model is established with emphasizing the difference from conventional single-channel SAR. Then, we combine the multichannel SAR data processing operators with the group sparsity property to construct the novel imaging method. The group sparsity property is modeled by the 2, q-norm, and the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,q</sub> regularization problem can be solved via sparse group thresholding function. It is shown that the proposed method can efficiently suppress the azimuth ambiguities caused by nonuniform undersampling. Simulations and Gaofen-3 real data experiments are exploited to verify the effectiveness of the proposed method.

Statistical Indices for Despeckling Evaluation in Multichannel SAR Images

With reference to the application of multichannel (polarimetric or interferometric) synthetic aperture radar (SAR) data, despeckling is a mandatory task in order to exploit fully the image information. Thanks to the long-standing studies, there exist several despeckling techniques that have shown to be powerful tools for filtering multichannel SAR images. Often, such techniques rely on the estimation of the covariance matrix. However, in the literature, the performance evaluation of the filtering operation, which represents a fundamental aspect, has been mainly addressed for a single intensity/amplitude SAR image. In this letter, we present two nonreference indices for the evaluation of the polarimetric/interferometric covariance matrix. The proposed quality indices assess the statistical similarity of the ratio between the original and filtered covariance matrices to the properties derived from the pure speckle model. The analyses both on simulated and real data show that the results of filter ranking by the proposed method are in agreement with the visual evaluation and are consistent with the common polarimetric quality measures.

An Improved Airborne Multichannel SAR Imaging Method With Motion Compensation and Range-Variant Channel Mismatch Correction

To obtain a high-resolution and wide-swath image, the azimuth multichannel technique has been widely used in synthetic aperture radar (SAR) systems to overcome the contradiction between the wide swath and high pulse repetition frequency. For a high image quality, channel mismatch correction is an essential step in the multichannel SAR data imaging. However, in the case of airborne multichannel SAR, motion errors will severely degrade the performance of channel mismatch correction. To deal with this problem, this article proposes an improved airborne multichannel SAR imaging method with motion compensation, and range-variant channel mismatch correction. First, motion errors are compensated based on resampling and phase compensation. Then, the time-delay and constant gain-phase errors between channels are estimated and corrected, followed by the range-variant phase error correction based on a novel range-down-sampling method, which reduces the influence of motion errors on the channel mismatch correction significantly. Finally, simulated and real data processing results are used to demonstrate the effectiveness of the proposed method.

A Novel Approach to Doppler Centroid and Channel Errors Estimation in Azimuth Multi-Channel SAR

Multi-channel synthetic aperture radar (SAR) in azimuth can overcome the minimum-antenna-area constraint of the conventional SAR in high-resolution and wide-swath (HRWS) imaging. However, the SAR system suffers from amplitude and phase mismatch among channels and nonideal antenna pattern, which will result in azimuth ambiguity and ghost targets in the final image. Therefore, taking the nonbandlimited signal and channel errors into account, a practical azimuth ambiguity-to-signal ratio (AASR) model of multi-channel SAR system is established. Meanwhile, the baseband Doppler centroid (DC) frequency related to channel errors also has an influence on image quality. Then, an effective method is proposed to calculate the baseband DC frequency according to the jumping points of the channel phase errors estimate. Subsequently, considering the effect of azimuth antenna pattern (AAP), a corresponding relationship between the ideal steering vectors and the signal subspace from the decomposing covariance matrix is established. After that, based on the uniqueness of the signal subspace and the correct corresponding relationship, an accurate method is proposed to estimate the channel phase errors by minimizing the minimum mean square error (MMSE) of the signal subspace. Finally, an accurate multi-channel SAR imaging diagram is shown to effectively mitigate the azimuth ambiguous energy caused by channel errors. Simulation and real data experiments, including four channel airborne SAR data with a bandwidth of 210 MHz and the Chinese Gaofen-3 dual receiving channel (DRC) spaceborne SAR data, validate the effectiveness of the proposed calibration method, particularly in low signal-to-noise ratio (SNR).

A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model

Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.

A Channel Calibration Method Based on Weighted Backprojection Algorithm for Multichannel SAR Imaging

In the multichannel synthetic aperture radar (SAR) systems, unavoidable channel errors will significantly degrade the performance of the ambiguity suppression. To address this problem, a channel phase error estimation method is proposed in this letter. First, the multichannel echo model and the weighted backprojection algorithm are introduced. The channel phase errors are then estimated by maximizing the image intensity using the gradient descent method. The simulation results confirm that the proposed method has higher accuracy and robustness than the orthogonal subspace method. The airborne SAR data acquired by a four-channel strip map C-band airborne SAR system demonstrate the feasibility of the proposed method.

Narrow‐band interference suppression method in multichannel SAR based on beamforming technique and sparse recovery

The problem of multichannel synthetic aperture radar image recovery in the presence of sidelobe narrow-band interference (NBI) is addressed. The NBI steering matrix is accurately estimated with the interference signal received in the passive mode. A beamforming technique is then utilised to minimise the energy loss of the target echoes and suppress NBI to a satisfactory level. By establishing a linear model for the signal with interference removed, the radar image is reconstructed via sparse recovery. The experimental results demonstrate that the proposed method can both suppress NBI effectively and generate high resolution images.