Multichannel Synthetic Aperture Radar Data Research Articles

Nonlocal Model-Free Denoising Algorithm for Single- and Multichannel SAR Data

Among the large number of synthetic aperture radar (SAR) image despeckling approaches existing in literature, nonlocal (NL) filters have received a desirable boost. However, often NL approaches define the similarity criterion based on model assumptions, such as a fully developed speckle model. This assumption may not be verified in high-resolution images of urban environments. To address this issue, a standalone model-free despeckling framework is proposed in this article. The presented approach provides a generic framework for denoising a variety of SAR products, from a single-intensity/amplitude image to polarimetric and interferometric SAR data. In particular, the method is based on the empirical distributional similarity between the patch containing the pixel to be recovered and the patch containing a similar candidate pixel. To decide whether the patches follow a similar distribution, the Kolmogorov–Smirnov test is adapted. Finally, the restoration process aggregates the selected similar pixels based on their relative importance derived from their distribution similarities. To mitigate the blurring effect and preserve the resolution, the inhomogeneity of the ratio image is used to perform the bias reduction step. The designed generic despeckling filter was tested on different products of SAR data. The results show that the method proves to be an unbiased restoration approach and is able to preserve structures and textures. It works fully automatically and efficiently with single and multilook (and multichannel) images.

Scene Adaptive Phase Inconsistency Estimation for Multi-channel ScanSAR System

Operating multi-channel synthetic aperture radar (SAR) in ScanSAR mode enables an ultrawide-swath and high-resolution imagery. A critical step in multi-channel SAR data processing is to accurately estimate and compensate phase inconsistency between channels. However, the Doppler spectrum distribution of ScanSAR relies heavily on the characteristic of illuminated regions, which may make the traditional time-domain phase estimation method failure under nonuniform scenes. In order to ensure that the performance of phase estimation is robust with respect to the variation of scenes, an innovative scene adaptive phase inconsistency estimation method for the ScanSAR system is presented in this letter, which can estimate channel phase error more accurately by extracting scene-center frequency from received data. The performance of the presented approach is evaluated using real airborne multi-channel SAR data. Experimental results show that the modified phase inconsistency estimation method is more effective and adaptable to different scenes compared with the conventional 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.

A Novel Channel Inconsistency Estimation Method for Azimuth Multichannel SAR Based on Maximum Normalized Image Sharpness

For azimuth multi-channel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets and image defocusing, etc. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness. First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing normalized image sharpness, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of objective function. Finally, the ultimate image is formed through adding up phase compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method.

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.

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.

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data, but also take advantage of the polarization diversity and the aspect angle dependence information from multichannel SAR data. First, the traditional sparse representation-based classification is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Finally, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the data set we consider. Extensive experimental results on a high-fidelity electromagnetic simulated data set and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.

Detection and Imaging of Moving Targets With LiMIT SAR Data

Detecting moving targets in synthetic aperture radar (SAR) imagery has recently gained a lot of interest as a way to augment optical moving target detection and classification in adverse (e.g., cloudy) weather conditions. In this paper, we primarily focus on the problem of detecting and imaging moving targets in single-channel (or summed multichannel) SAR data. Single-channel-based methods provide the ability to do detection well below the normal minimum detectable velocity (MDV) of multichannel-based GMTI. This can be particularly important for small radar antennas, which tend to have high conventional GMTI MDV. We also show results for multiple channel geolocation after single-channel detection and imaging. The algorithms consist of the following steps. We first suppress the stationary scene by comparing noncoherent time-subimages. We then detect the movers by applying a set of possible motion corrections to the image and use a novel matched filter to detect the movers in this space. We can then image the moving targets using standard SAR focusing techniques and geolocate the movers using multichannel (if available) along-track interferometry. We demonstrate and evaluate our algorithms using data collected from the Lincoln Multimission ISR Testbed airborne radar system.

A Novel Approach to Moving Target Screening for UHF-Band SAR GMTI

Due to a long coherent processing interval, moving targets are severely smeared in the UHF-band synthetic aperture radar (SAR) imagery. This further results in a low signal-to-clutter-and-noise ratio, which might lead to an unacceptable false-alarm rate in multichannel ground moving target indication. A method of moving target screening is presented in this letter, which serves to determine whether the target detected by a constant false-alarm rate detector is a real moving target. An inverse omega-K algorithm is implemented, which can recover the Doppler phase history of any isolated target within a clutter-suppressed omega-K SAR image. The recovered data are again processed into a subimage by a simple range-Doppler algorithm. Then, the subimage is refocused by azimuth autofocus processing. The sharpness of the subimage will not change after refocusing if it only contains stationary targets; otherwise, the sharpness will significantly improve. We can eliminate a false moving target by detecting this change. The proposed method is demonstrated on simulated and real multichannel UHF-band SAR data.

Spaceborne squinted multichannel synthetic aperture radar data focusing

This study analyses echo properties of spaceborne squinted multichannel along the track synthetic aperture radar (SAR) data. The highly squint angle and the large transmitted pulse bandwidth lead to Doppler spectrum back folding in the spaceborne squinted case. The extended Doppler bandwidth in the squinted mode increases the processing difficulty of azimuth multichannel data reconstruction. According to echo properties of the spaceborne squinted mode, a new imaging approach for spaceborne squinted multichannel SAR data focusing is proposed. The key point of this imaging approach is azimuth multichannel data preprocessing to resolve the aliased Doppler spectrum caused by both azimuth sub-sampling in each azimuth channel and the highly squint angle. Afterwards, a modified range migration algorithm is taken for consequent equivalent monostatic spaceborne squinted SAR data focusing. Simulation results on point targets and distributed target are given to validate the proposed imaging approach.

On the Baseband Doppler Centroid Estimation for Multichannel HRWS SAR Imaging

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, Doppler centroid (DC) is essential for SAR focusing. However, conventional phase-dependent DC estimators for single-channel SAR systems face many problems in multichannel HRWS SAR systems because of the azimuth undersampling of each channel and channel mismatches in phase. To estimate the baseband DC, the spatial cross-correlation coefficients (SCCCs) of multichannel HRWS SAR signals are exploited, and a novel baseband DC estimator named SCCC method is proposed in this letter. Validation of the proposed method is demonstrated with the real airborne multichannel SAR data.

Optimum Signal Processing for Multichannel SAR: With Application to High-Resolution Wide-Swath Imaging

A new method for processing multichannel synthetic aperture radar (SAR) data to achieve desirable image characteristics is presented. The method is optimal because it is derived by minimizing a mean-square-error cost function and generalizes current methods for high-resolution wide-swath SAR signal processing. The proposed method is easily implementable, can support a wide range in the pulse repetition frequency (PRF), including cases with highly nonuniform spatial sampling, and is robust against PRFs where current projection techniques fail, cases where the PRF is ideally suited to clutter suppression. Point spread functions for the proposed algorithms are presented, and the theory and simulations are further corroborated by results using multichannel SAR data measured by RADARSAT-2. We demonstrate that, if RADARSAT-2 were able to illuminate a 250-km swath (300 km ground range), then, conceptually, the new method would be able to process the highly nonuniformly sampled data to provide an extremely wide mode at approximately 5-m azimuth resolution.

COMPLETE COMPLEMENTARY SEQUENCE CODING WAVEFORM BASED AZIMUTH MULTI-CHANNEL SPACE-BORNE SAR WITH ULTRA-LOW RANGE SIDELOBE RATIO PERFORMANCE

Sidelobes of strong targets substantially impact image quality of synthetic aperture radar (SAR) using linear frequency modulation (LFM) waveform, especially in urban areas. A novel space-borne azimuth multi-channel SAR scheme with ultra-low range sidelobe-ratio (RSLR) performance was proposed, employing complete complementary sequence (CC-S) coding waveform. The CC-S waveform was utilized to acquire ultra-low RSLR performance in range direction. Azimuth multi-channel scheme was introduced, to compensate reduction of efiective PRF due to employing CC-S, and to mitigate azimuth resolution lost resulted from strong azimuth weighting, in order to implement low side-lobe performance in both range and azimuth direction. The method for pre-processing the CC- S based multi-channel SAR data was proposed, which would both compensate receiving time difierence of sub-sequences and reconstruct azimuth spectrum of multi-channel SAR data. Furthermore, the corresponding image formation algorithm for accurately focusing raw data of the SAR system was also proposed. Computer simulation results were presented, which demonstrated the validity of the proposed SAR scheme and image formation algorithm.

Suppressing Moving Target Artifacts in Multi-Channel Stripmap SAR Images by Space–Doppler Filtering

Synthetic aperture radar (SAR) as a method for ground imaging by using a single antenna or a sensor array has widely found attraction for remote sensing applications and reconnaissance tasks. Independent of the particular sensor but inherent to the SAR processing, moving objects in the observed scene are imaged at incorrect positions and can appear in a smeared fashion. For countering these disturbing artifacts a joint spatial-spectral processing is proposed and analyzed in this paper that allows to suppress echoes from moving targets in multi-channel stripmap SAR data while preserving the echoes of the fixed scene. Results obtained with experimental data from an airborne system show the potential and limitations for a practical application of the presented method.

Motion parameter estimation of multiple ground fast-moving targets with a three-channel synthetic aperture radar

This study presents a novel motion parameters estimation technique for moving targets, including those with velocities beyond the Nyquist limit in multi-channel synthetic aperture radar data. The technique proposed in this study exploits the linear dependence between the fold factor of the cross-track velocity and the slope of the moving target trajectory in the range-compressed and azimuth time domain after performing Keystone transformation in the range frequency and azimuth time domain. The velocity ambiguity thus can be overcome. The along-track velocity can be retrieved from the chirp rate estimated by applying a new time-frequency analysis method called Keystone-Wigner transform. The algorithm of motion parameter estimation efficiently converts the two-dimensional (2-D) search into the two separate 1-D cases. The proposed method can robustly estimate the motion parameters of the fast-moving targets, which are illustrated both with simulated and real data.

Detection of Moving Targets by Focusing in UWB SAR—Theory and Experimental Results

Moving-target detection in ultrawideband (UWB) synthetic aperture radar (SAR) is associated with long integration time and must accommodate azimuth focusing for reliable detection. This paper presents the theory on detection of moving targets by focusing and experimental results on single-channel SAR data aimed at evaluating the detection performance. The results with respect to both simulated and real data show that the ability to detect moving targets increases significantly when applying the proposed detection technique. The improvement in signal-to-clutter noise ratio, which is a basic requisite for evaluating the performance, reaches approximately 20 dB, using only single-channel SAR data. This gain will be preserved for the case of multichannel SAR data. The reference system for this study is the airborne UWB low-frequency SAR Coherent All RAdio BAnd Sensing II.

Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using ldquoenergy functions.rdquo In order to address the task of the estimation of the model parameters, the expectation-maximization algorithm is combined with the recently proposed ldquomethod of log-cumulants.rdquo The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images.

Real extended scene and moving target multi-channel SAR raw signal simulation

A real extended scene and moving targets multi-channel Synthetic Aperture Radar (SAR) raw signal simulator accounting for Inertial Navigation System (INS) errors and antenna patterns is presented in this paper. INS errors are obtained by solving INS error differential equations with Runge-Kutta method. A high resolution SAR image is used to estimate the complex reflectance of real extended scene. Extended scene and moving target are simulated separately and then are superposed in time domain. The simulated multi-channel SAR data can be used for development of multi-channel SAR Ground Moving Target Indication (SAR-GMTI) and also can be used for development of SAR motion compensation.

Supervised fuzzy analysis of single- and multichannel SAR data

The paper proposes a new learning fuzzy classification for single and multichannel synthetic aperture radar (SAR) data. It consists of the fusion of a supervised learning fuzzy distribution estimator and an unsupervised learning fuzzy vector quantizer. The adaptive algorithm accommodates varying requirements and delivers classification results in near real time. In addition to the classification, the user gets the reliability of the classification. This knowledge can be used to fuse several sensor channels efficiently. Automatically, a rule base is developed to deliver the required information with the highest possible reliability. In the author's example, the channels of a full polarimetric SAR are used. However, the algorithm can be extended also to optic and infrared channels. The proposed fuzzy classification system forms one module of an adaptive remote-sensing system. A conceptual design of this system is given. System control relies on an expert knowledge base and allows automatic configuration of the system to the considered remote-sensing application. This will lead to an increased usefulness of remotely sensed data.

Ocean surface velocity estimation in multichannel ATI-SAR systems

Conventional along-track interferometric (ATI) synthetic aperture radar (SAR) systems derive the ocean surface velocity from an estimate of the phase difference between the SAR echoes received from two displaced phase centres with a single time-lag. A maximum likelihood algorithm is proposed to process multichannel SAR data from an array of phase centres with multiple time-lags. The new technique provides better estimation accuracy than the conventional technique, unambiguous velocity retrieval, and extended operational capability.