Multichannel Synthetic Aperture Radar Systems Research Articles

A 2-D Range Ambiguity Suppression Method Based on Blind Source Separation for Multichannel SAR Systems

A 2-D Range Ambiguity Suppression Method Based on Blind Source Separation for Multichannel SAR Systems

A Novel Method for Staggered SAR Imaging in an Elevation Multichannel System

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

A Novel Method for Interferometric Phase Estimation in Dual-Channel Cancellation

Multichannel SAR systems have grown rapidly over the past decade due to their powerful high-resolution and wide-swath (HRWS) capabilities. Because spatially separated channels also have the potential to suppress jamming, dual-channel cancellation is a general method that is effective regardless of the type of jamming signal. In this paper, the principle of dual-channel cancellation (DCC) is introduced, and several practical problems using DCC are also discussed. Moreover, this paper emphasizes interferometric phase estimation, which is the key to DCC. If the jamming-to-signal ratio (JSR) is high, the interferometric phase can be estimated accurately from the interferometry of two channel signals, but estimation becomes rather difficult when the JSR decreases. To solve the problem of interferometric phase estimation under a low JSR, a novel interferometric phase estimation method using cosine similarity is proposed in this paper. L-band airborne dual-channel SAR is performed to investigate the applicability of the method. The results not only prove that cosine similarity is an effective method for interferometric phase estimation, but also demonstrate the potential of DCC in the SAR anti-jamming processing.

A Multifeature TDAO-AIR Clutter Classification Approach for Inshore Ambiguity Identification and Suppression With Azimuth Multichannel SAR System

In the inshore region, the azimuth ambiguity of land clutter is the main factor, which reduces the clutter suppression performance for the multichannel synthetic aperture radar (SAR) system. To address this problem, we propose a multifeature tensor discriminant alternating optimization and affine invariant Riemannian (TDAO-AIR) classification approach. First, the correlation coefficient between channels is obtained to characterize the normalized SAR image amplitude. Next, multilook interferometric processing is performed to reduce the effect of Speckle noise and acquire multilook interferometric complex images. At the same time, we extract the correlation coefficient gradient and the multilook interferometric phase gradient in different spatial directions to characterize the spatial structure. Next, we construct a feature tensor (FT), including correlation coefficient between channels, multilook interferometric phase, and spatial structural gradient information for each range-Doppler unit (RDU). The TDAO algorithm is proposed to extract the core FT (CFT) by optimizing the objective function and getting the projection matrix. Then, the CFT is unfolded along the feature dimension, and the feature covariance matrix (FCM) is constructed for each RDU. With the AIR distance measure, the inshore region is divided into different clutter regions. Finally, in the azimuth ambiguous clutter region, independent and identically distributed (IID) training samples are selected to estimate the clutter covariance matrix (CCM), and robust adaptive clutter suppression is performed in the SAR image domain. The experiments on simulated and real measured data by TerraSAR-X demonstrate that the proposed method can accurately identify and suppress the azimuth ambiguity in the inshore region.

Doppler Centroid Estimation for Ground Moving Target in Multichannel HRWS SAR System

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, Doppler centroid (DC) is an essential parameter for spectrum reconstruction and image focusing. However, the conventional DC estimator faces many problems in moving target with multichannel SAR system, such as nonuniform data in azimuth, subsignal usage, and channel mismatch. To estimate the DC of moving target, the modified cross-correlation coefficient (MCCC) method for multichannel SAR system is proposed in this letter, which is especially suitable for nonuniform and undersampled data. Simulations with real spaceborne data demonstrate the effectiveness of this method.

HRWS SAR Narrowband Interference Mitigation Using Low-Rank Recovery and Image-Domain Sparse Regularization

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

On-Ground RFI Mitigation for Spaceborne Multichannel SAR Systems Using Auxiliary Beams

Radio frequency interference (RFI) is becoming a major concern for future synthetic aperture radar (SAR) missions due to the increased user demand for frequency occupation in a number of applications. Each occurrence of interference introduces artifacts in the radar imagery, biasing the measurements and leading to erroneous results. In addition to conventional techniques, the use of multichannel SAR for RFI mitigation has been proposed, because its digital beamforming (DBF) capability allows for a spatial filtering of the received signals. Thereby, it becomes possible to remove RFI that arrives from a different direction than the SAR signal. Past publications on the topic presented highly flexible spatial filtering techniques. Those methods require either additional on-board processing or a substantial increase in the downlink capacity. This article shows that by slightly reducing the flexibility of the spatial filtering, DBF can be utilized for RFI mitigation without either drawbacks: the processing is performed on-ground after downlinking the data and the data volume remains manageable. This is achieved with auxiliary beams. Their concept and limitations are discussed in detail in this article and are supported with simulated RFI mitigation results. Furthermore, it is shown that the information collected with an auxiliary beam can also be used to filter the RFI signal when it is spatially nonorthogonal to the SAR signal.

MSAR-Net: A Deep Learning Based Classification Approach for Learning the Maritime Environment

We develop an end-to-end deep learning-based approach for the classification of complex maritime environments via multichannel synthetic aperture radar (MSAR) sensors. In particular, we introduce a novel convolutional neural network (CNN)-based multichannel structure that incorporates a divisive normalization technique that is critical for achieving consistent classification performance across maritime scenes. We evaluate the performance of our technique, called MSAR-Net, using datasets collected by the U.S. Naval Research Laboratory’s experimental airborne MSAR system. Our results demonstrate that MSAR-Net achieves consistently and substantially superior performance compared to conventional amplitude-based scene classification. Finally, though we focus on the specific (yet difficult) problem of maritime surveillance, MSAR-Net, introduced in this article, is a fundamental technique that can potentially result in significant performance improvements in a variety of multichannel surveillance and remote sensing applications.

A Two-Stage Echo Separation Scheme for Spaceborne MIMO-HWRS SAR System

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-width (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this paper, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross-correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne-ground echo separation scheme. This scheme is mainly divided into two-steps: the first step is a real-time SCORE-beamforming process on board to reduce the downlink data volume, and the second step is a bandpass-null steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, thus higher-resolution and wider-swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme.

An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR

Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm.

A two-step channel mismatch estimation and compensation method for distributed MC SAR

ABSTRACT A distributed multichannel synthetic aperture radar system refers to a multichannel synthetic aperture radar system with channels distributed and installed at different locations on the platform. It is a promising system for high-resolution wide-swath imaging. The channels of the distributed multichannel synthetic aperture radar are sparsely distributed and have long cross-track baselines. The range-variant envelope migration and phase mismatch in the echoes between different channels are introduced by the long cross-track baselines and affect the performance of azimuth signal reconstruction. The sparsity of channels results in the poor correlation of data between channels, so the time-domain cross-correlation method typically used in azimuth multichannel synthetic aperture radar cannot be used in distributed multichannel synthetic aperture radar to estimate channel phase error directly. In this paper, the gain-phase error, sampling time delay and slant range difference introduced by cross-track baselines are modelled uniformly as channel mismatches. Firstly, a two-step channel mismatch estimation and compensation method for distributed multichannel synthetic aperture radar is proposed. Secondly, a range-variant envelope migration compensation method based on the chirp scaling principle is given. Thirdly, this paper also presents the processing of data rearrangement based on the distribution of spatial sampling points to make the time-domain cross-correlation method suitable for distributed multichannel synthetic aperture radar. Point target simulation and scene simulation are provided to show the effectiveness of the proposed methods.

A Novel Sub-Image Local Area Minimum Entropy Reconstruction Method for HRWS SAR Adaptive Unambiguous Imaging

Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly sampled signal. However, various errors, especially amplitude, phase, and baseline errors, always significantly degrade the performance of the reconstruction methods. To solve this problem, in this paper, a novel sub-image local area minimum entropy reconstruction method (SILAMER) is proposed, which has favorable adaptability to the HRWS SAR system with various errors. First, according to the idea of image domain reconstruction, the sub-images are generated by employing the back-projection algorithm. Then, we proposed an estimation algorithm based on sub-image local area minimum entropy to obtain the optimal reconstruction coefficient and the compensation phase, which can greatly improve the estimation efficiency by using a local area of the sub-image as the input for estimation. Finally, the sub-images are weighted by the optimal estimated reconstruction coefficient and calibrated by the compensation phase to obtain the unambiguous reconstruction image. The experimental results verify the effectiveness of the proposed method. Noticeably, the proposed algorithm has two additional advantages, i.e., (1) it can perform well under the condition of low signal-to-noise ratio (SNR), and (2) it is suitable for the curved trajectory SAR reconstruction. The simulations verify these advantages of the proposed method.

Ground Moving Target Indication for the Geosynchronous-Low Earth Orbit Bistatic Multichannel SAR System

In this article, an effective clutter suppression and moving target imaging approach is proposed for the geosynchronous-low earth orbit (GEO-LEO) bistatic multichannel synthetic aperture radar (SAR) system, which is robust for the fast moving target with Doppler centroid ambiguity. For the GEO-LEO bistatic multichannel SAR system, the characteristic of baseline with azimuth invariant in Doppler Fourier transform domain is not tenable. It is difficult to implement clutter suppression by the unified baseline compensation in azimuth Doppler Fourier transform domain. Fortunately, we discover that the baseline can be approximately regarded as a constant in chirp Fourier transform domain for the GEO-LEO bistatic multichannel SAR system. Hence, the corresponding baseline compensation can be achieved in the azimuth chirp Fourier transform domain for the clutter. With an orthogonality vector of clutter, the clutter can be well suppressed and the moving target is extracted. After that, the moving target imaging approach is developed with the baseband Doppler centroid estimation and Doppler ambiguity number estimation. Finally, the theoretical investigations and the proposed approach in this article are validated by some experiments, where the experiments for clutter suppression and experiments for fast moving targets are included. Especially, an experiment with real SAR scattering scene is involved. In addition, some discussions for blind velocity targets are presented.

Radial Velocity Estimation of Ships on Open Sea in the Azimuth Multichannel SAR System

The azimuth multichannel synthetic aperture radar (SAR) system can meet the requirements of high resolution and wide swath (HRWS) simultaneously, which overcomes the constraint of the traditional single-channel SAR. However, for a moving target illuminated by the azimuth multichannel SAR system, its radial velocity will lead to ambiguous components and mislocation in the image. Therefore, the radial velocity estimation plays an important role in improving the image quality and moving target relocation, especially for large ships on the open sea. However, as the pulse repetition frequency of a single channel is less than the Doppler spectrum, the traditional velocity estimation methods working in the image domain are out of action. This article suggests an idea that the issue of velocity estimation is converted into that of the linear phase errors estimation combining the linear fitting method, and it is assumed that the target has been already detected before applying the velocity estimation algorithms. To estimate the linear phase errors, two algorithms operating in the Doppler domain are introduced and compared, namely the subspace-based method and the modified frequency correlation method. The advantages of the proposed approaches are free from iteration operation and high accuracy. Besides, the effectiveness of the methods is demonstrated via simulation data and GaoFen-3 real data from ultra-fine stripmap mode. Finally, this article analyzes the velocity estimation accuracy of the two methods and the influence of channel imbalance through substantial experiments.

Sparse Multi-Channel Synthetic Aperture Radar Based Motion Induced Distortion Correction and Classification of Maritime Scenes

Multi-channel Synthetic Aperture Radar (MSAR) in the maritime environment has proven to be an effective technique for improved image fidelity and scene characterization. Past demonstrations of MSAR-based methods for improvements such as motion induced distortion correction and image classification were performed using a uniformly spaced array of phase centers. In this paper, we derive novel extensions of these techniques to the case of sparse and non-uniformly spaced phase center configurations and demonstrate their effectiveness for a variety of platforms and sensor arrangements. We establish general techniques for correcting motion induced distortions, establish theoretical bounds of performance, and empirically validate our techniques on data derived from our airborne MSAR system. We then use these images to characterize the structure of the maritime scene using our MSAR-based classification technique. Our imaging and classification techniques are amenable to efficient implementation on diverse hardware platforms and sensor arrangements, and thus can find ready application in practical multichannel radar systems.

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.

A Novel Motion Compensation Scheme for 2-D Multichannel SAR Systems With Quaternion Posture Calculation

The displaced phase center antennas in azimuth and digital beamforming (DBF) in elevation are two state-of-the-art techniques for achieving high-resolution wide swath (HRWS) imaging in the multichannel synthetic aperture radar (SAR) systems. However, due to the atmospheric turbulence, airborne HRWS-SARs inevitably suffer trajectory disturbances, which will consequently defocus the SAR image. Although various motion compensation (MoCo) methods have been proposed, they are mostly designed for the traditional single-channel SAR and, therefore, ignore the channel-dependent posture error. The posture motion error will introduce residual motion and time-variant channel errors, which not only defocuses the image but also causes azimuth ambiguity and degrades the SNR improvement of the final images of the azimuth multichannel and DBF-SAR, respectively. To solve these problems, a novel MoCo scheme for 2-D multichannel SAR systems is proposed. The posture error is described and calculated in matrix form through the use of quaternions. Then, the posture error is transformed into the translational motion error for each receiving channel and is subsequently compensated precisely. To address the residual aperture-variant motion error, a modified aperture-dependent MoCo is integrated into the proposed scheme. Simulations and airborne experiments, including processing the data of a C-band SAR system with four azimuth channels and an X-band DBF-SAR system with 16 elevation channels, have been implemented to validate the effectiveness of the proposed MoCo scheme.

A novel phase bias estimation method for multichannel HRWS SAR system in azimuth based on RDS analysis

ABSTRACT Multichannel synthetic aperture radar (SAR) systems in azimuth can realize high-resolution and wide-swath (HRWS) imaging, which has attracted intensive research interests. The azimuth ambiguities caused by non-uniform sampling can be suppressed by digital beamforming technique. However, the presence of inevitable channel errors in the multichannel SAR system deteriorates the performance of ambiguity suppression. Based on the analysis of the characteristics of the multichannel SAR echoes, it was found that phase bias among channels can cause jumping in the reconstructed Doppler spectrum (RDS). This letter presents a novel data-based phase bias estimation method for HRWS SAR system. With the assumption that the splice of echo energy inside the Doppler bandwidth is continuous, there is no jumping in the RDS. The estimation of phase biases in the multichannel HRWS SAR system can be translated into the optimization solution issue. Simulation experiments and real data processing results validated the effectiveness of the proposed method.

Velocity Estimation of Moving Targets in Multi-channel SAR images by Optimally-weighted Velocity Averaging of Multiple Pixels

ABSTRACT A scheme is proposed for the radial-velocity estimation of moving targets in multi-channel SAR systems. A moving target consists of multiple pixels in SAR images, generally. The radial velocity of each pixel can be estimated from its interferometric phase. The radial velocity of the moving target is then obtained by averaging the radial-velocity estimates of its multiple pixels. In the averaging, the weight of each radial-velocity estimate is designed to minimize the mean-square-error (MSE) between the estimate and the truth. This scheme has a higher estimation accuracy than those conventional schemes, which take the radial-velocity estimate of one pixel as the final radial-velocity estimate of the moving target. The experimental results of both simulated and real data show the effectiveness of this scheme.

Quadratically Constrained Ambiguity Suppression Algorithm for APC/Multichannel SAR Systems With Nonuniform Spatial Sampling

The azimuth phase coding (APC) technique is known for its very low implementation complexity and its effectiveness for point and distributed ambiguities in conventional synthetic aperture radar (SAR) systems. In recent years, as an extension, the APC technique has been briefly discussed for multichannel SAR systems. However, the properties of the APC technique are no longer guaranteed in the multichannel SAR systems based on the digital beamforming (DBF) on-receive, and only a slight APC gain in the suppression of the range ambiguity can be obtained. In this article, we first provide a more thorough analysis for an APC-multichannel SAR system with respect to a uniform pulse-repetition frequency (PRF). Then, the APC/multichannel SAR system with nonuniform spatial sampling is briefly discussed, and an improved reconstruction approach based on a quadratically constrained optimization model is proposed to increase greatly the APC gain with respect to existing multichannel reconstruction algorithms. This proposed approach allows the minimization of the range ambiguity with a given azimuth-ambiguity constraint. In particular, for some specific PRFs, the proposed method permits a cancellation of the odd-order range ambiguity. Finally, simulation experiments are performed to verify the advantages and effectiveness of the proposed approach.