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To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR). [ABSTRACT FROM AUTHOR]

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

As a new imaging framework, the rotating synthetic aperture radar (ROSAR) derives a synthetic aperture through antenna rotation instead of traditional linear platform motion. The rotational synthetic aperture is sensitive to high-frequency vibrations aboard helicopters. The vibration causes severe phase error in signal echoes and imaging degradation. Unlike traditional synthetic aperture radar (SAR) imaging where range-Doppler algorithm (RDA) maybe applied for autofocus to improve the imaging performance, vibration phase errors cannot be estimated via conventional imaging algorithms due to severe range and azimuth couplings. A new ROSAR imaging procedure is proposed to compensate the phase error resulting from high-frequency vibrations of helicopters. A vibration model of ROSAR signal echo is established. The double Doppler keystone transform (DDKT) is adopted to correct the range cell migration (RCM) induced by slant range history and vibration errors. Analytical echo expression with range-independent vibration phase error is also derived in greater details. The focused image can then be obtained via classical autofocus algorithms. The effectiveness of the proposed technique is sufficiently demonstrated through simulation studies. [ABSTRACT FROM AUTHOR]

This letter deals with the issue of clutter suppression with the helicopter-borne single-channel rotating synthetic aperture radar (SCRoSAR). Commonly, obtaining an synthetic aperture radar (SAR) image and implementing the ground moving target indication (GMTI) simultaneously is a challenging task for the single-channel SAR (SCSAR), since, the clutter background covers most of the slow-moving targets in the SAR image. In this letter, we propose a novel clutter suppression method via the time-Doppler chirp varying (TDCV) approach for the SCRoSAR system. A traditional RoSAR imaging algorithm and a modified RoSAR-TDCV imaging algorithm are employed to generate an original image and a TDCV image from the same set of data, respectively. Benefits from the characteristics of the TDCV approach, the position of moving targets will be shifting along the range direction in the TDCV image. Meanwhile, the position displacement of stationary clutter scatterers is neglectable. Therefore, via image cancellation, the clutter background can be significantly suppressed. With the proposed approach, the RoSAR system is capable of imaging the stationary objects and revealing the moving targets, simultaneously. The experimental results with simulated data demonstrate the validation of our proposed method. [ABSTRACT FROM AUTHOR]

A range-ambiguous clutter suppression approach based on extended azimuth phase coding (EAPC) is proposed to handle the range ambiguity of the multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system for ground moving target indication (GMTI) application. The echoes from different ambiguous range regions can be well separated in the transmit spatial frequency domain by properly designing the EAPC shift factor. In the sequel, a set of transmit filters are employed to extract the echoes of each ambiguous region independently. Then the azimuth deramp operation is applied to the extracted data to focus the target energy of the desired region while the residual target energy of other regions is still smeared due to the mismatched reference function. After this, the adaptive matched filtering algorithm is adopted to suppress the clutter and detect the moving target. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for range-ambiguous clutter suppression and ground moving target detection. [ABSTRACT FROM AUTHOR]

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments. [ABSTRACT FROM AUTHOR]

A rotating synthetic aperture radar (RoSAR) can image the environment in 360° on a stationary platform because it uses rotating antennas. The range-variant distortion in azimuth is evident in the RoSAR system, which degrades the imaging quality, particularly in high-resolution situations. In this letter, a new chirp $z$ transform (CZT) imaging algorithm is developed to remove the range-variant distortion in azimuth for helicopter-borne RoSAR systems. Based on a fourth-order approximation of the slant range model, a precise expression of the 2-D spectrum of the echo is derived via the series reversion method. The spatial-variant characteristics of the range cell migration term and the second range compression term are analyzed and compensated. Then, according to the analysis on the range-dependent output sample spacing variation in azimuth, which is caused by the RoSAR configuration, an efficient CZT is proposed to remove the range-variant distortion in azimuth. The experimental results with simulated data are provided to clearly demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

In the presence of range ambiguity, the synthetic aperture radar (SAR) systems suffer from image aliasing, which dramatically degrades the quality of SAR images. In this article, an easy-to-implement technique for range ambiguity suppression is investigated, which is based on phase coding in the transmit pulse dimension, referred to as pulse phase coding (PPC). By properly designing the PPC series, it is possible to discriminate the range ambiguous echoes from different range areas in the Doppler frequency domain. To further suppress the range ambiguous echo, a two-pulse cancellation (TPC)-based SAR imaging method is proposed, which improves the quality of SAR images in the presence of range ambiguity. The proposed two-pulse cancellation is performed followed by the azimuth compression. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Helicopter‐borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter‐borne ROSAR wavenumber‐domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA‐based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well‐focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter‐borne ROSAR imaging. [ABSTRACT FROM AUTHOR]

Two novel phase noise filtering algorithms for interferometric synthetic aperture radar (InSAR) are presented in this paper. Aiming at the nonlocal high self-similarity existing in the InSAR phase, we establish the phase noise filtering formulations with the $l_0$-norm regularizer and the $l_1$-norm regularizer, respectively. Although these two original formulations are nonconvex, we attempt to solve them by successive upper bound minimization combined with dictionary learning method. Specifically, for the noise reduction formulation with the $l_0$-norm regularizer, we first divide the original problem into a series of decoupled subproblems. Second, we obtain the approximate subproblem, which is locally tight upper bound of each subproblem by using a majorization–minimization technique. Third, we compute the sparse parameter vector for each approximate subproblem, followed by a matrix form update for the dictionary. The three steps are tackled cyclically until a satisfying solution is attained. The noise reduction problem with the $l_1$-norm regularizer is handled in a similar approach. We also establish the computational complexities of these two methods and summarize their distinct performance. Simulation results based on both synthetic data and simulated InAR data show that these two new InSAR phase noise reduction methods have much better performance than several existing phase filtering methods. [ABSTRACT FROM PUBLISHER]

This paper investigates a new and special imaging mode, bistatic forward-looking high-speed maneuvering platform synthetic aperture radar, in which both platforms travel curvilinear trajectories with high velocities and accelerations. A thorough understanding of its new properties of motion, 2-D resolution, and echo signal coupling is given. Applicability of gradient method neglecting time dependence of gradient to this special configuration is addressed. Moreover, 2-D coupling characteristic of echo signal at different imaging distances is investigated. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) has been increasingly used in the synthetic aperture radar ground moving-target indication system, particularly for imaging the moving targets, which satisfy the sparse precondition of the CS method. However, efficient moving-target imaging is a key challenge for current CS methods, since the redundant basis brings heavy computation load. In this letter, by exploiting the geometry information of the defocused results, we present an efficient fractional Fourier transform (FRFT) to estimate the Doppler rate and image the moving targets by only two times FRFT rather than time-consuming searching operation. Then, the concept is extended into an efficient CS (ECS) imaging method by two bases consisting of two discrete FRFT matrices rather than the redundant basis. Simulations and real-data process are provided to demonstrate the effectiveness of the ECS method. The proposed ECS method can achieve accurate parameter estimation and imaging performance with low computational complexity. [ABSTRACT FROM PUBLISHER]

Efficient motion parameter estimation is a key challenge for moving-target imaging and localization in the synthetic aperture radar ground moving-target indication system. However, the existing methods suffer from ambiguities, complex realization, or heavy computation load of $O(MN)$. To solve these problems, we propose an efficient Radon transform (RT) and an efficient fractional Fourier transform (FRFT) to estimate the radial velocity and azimuth velocity. By exploiting the geometry information, we model a geometry relationship between the motion parameters and two transform angles of RT or FRFT. The matched motion parameters can be estimated by the geometry relationship of the mismatched results, and the computation complexity is reduced from $O(MN)$ to $O(2N)$ effectively. Additionally, the symmetry property can be used for clutter canceling. Simulated and experimental results demonstrate the validity of the proposed methods. Compared with conventional motion parameter estimation methods, the proposed methods are much more efficient in acquiring accurate estimation results. [ABSTRACT FROM PUBLISHER]

High-resolution synthetic aperture radar (SAR) imagery of a wide area of surveillance is a difficult large-data problem. In the past few years, researchers have applied compressive sensing (CS) to SAR, as it exploits redundancy in signals. To further extend the sparse problem from the vector to the matrix, a new theory called matrix completion (MC) has attracted much attention, which can complete a matrix from a small set of corrupted entries based on the assumption that the matrix is essentially of low rank. Inspired by this technique, a novel SAR imaging algorithm is proposed in this letter to deal with the undersampled data. After representing the data of a range cell as a matrix, the phase is compensated to keep the matrix holding the property of low rank. Subsequently, MC can be utilized to recover the full-aperture data in the new constructed matrix. Since the data are completely unsampled in the corresponding azimuth cells, the proposed method has effectively conquered the restriction of previous applications that each received channel must have a small number of samples. The final results in both simulation and real-data experiments show that the targets can be well focused even in the scenario of discarding a large percentage of the received pulses. Moreover, when compared with CS, the method is not required to design the complicated measurement matrix. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we describe an approach for Doppler ambiguity resolution. This approach adopts the fact that unwrapped Doppler centroid is a linear function of range frequency for a given antenna squint angle. On this basis, an alternative azimuth compression method is addressed and performed in range frequency domain to accumulate target energy in the azimuth direction. The resulting trajectories behave as some straight lines with the average slope proportional to absolute Doppler centroid. This slope can be well estimated by image-contrast methods. Theoretical analysis and real measured SAR data show that the proposed approach works well in high- and relatively low-contrast scenes. [Copyright &y& Elsevier]

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

Abstract: For the sake of the robustness to ground moving targets indication (GMTI) for synthetic aperture radar (SAR) in heterogeneous environment, a new approach using multi-channel and multi-look SAR images is presented. This approach has been shown to attain improved suppression of monostatic clutter over its counterpart (the multi-channel and single-look algorithm). In this approach, the joint data vector is utilized which has robustness to image coregistration and deleterious system errors in practice. Consequently, a series of radial velocity estimation approaches are presented. The estimation accuracy can be improved by the multi-look algorithms. Experimental airborne SAR data illustrates the effectiveness of the proposed GMTI schemes. It is shown that, compared with conventional single-look algorithms, the multi-look approach can obtain better clutter cancellation performance and more accurate velocity estimation. [Copyright &y& Elsevier]

The image of ground maneuvering targets may be defocused due to the Doppler ambiguity, high-order range migration (RM), and Doppler frequency migration (DFM) caused by the target's complex motions in a synthetic aperture radar (SAR) system. To settle these problems, an efficient algorithm based on discrete polynomial-phase transform (DPT), keystone transform (KT), and matched filtering processing (MFP) is presented for ground maneuvering target refocusing and motion parameter estimation in this paper. Firstly, the DPT is applied to transform the cubic phase into the quadratic phase and simultaneously eliminate the quadratic RM, cubic RM, and quadratic DFM. Furthermore, the Doppler ambiguity containing Doppler center blur and Doppler spectrum ambiguity is also dealt with effectively by introducing a very small fixed lay time after DPT operation. Then, the KT is performed to correct the linear RM. After that, the matched filtering function related to the target's equivalent third-order coefficient is constructed to compensate for the residual linear DFM in the range-time and slow-time domain. Lastly, a well-refocused image of the maneuvering target can be acquired, and the target's motion parameters can be estimated effectively. The proposed algorithm has high computational efficiency and possesses favorable refocusing performance and motion parameter estimation precision. Simulation and real data processing results prove the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

It is a challenge to realize wide swath imaging due to the conflict between Doppler ambiguity and range ambiguity for hypersonic vehicle (HSV) radar. In addition, there are many conditions requiring the forward-looking imaging. In a forward-looking synthetic aperture radar (SAR) system, left–right ambiguity arises, since two symmetrical targets have the same Doppler frequency magnitude. After selecting an appropriate pulse repetition frequency (PRF) to avoid Doppler ambiguity, we only need to solve the range ambiguity and left–right ambiguity. To handle these issues, this paper proposes an approach to resolve the range ambiguity and left–right ambiguity using the frequency diverse array (FDA). With the range-angle-dependent property of the transmit steering vector, FDA can distinguish the range ambiguous echoes in the spatial frequency domain. By performing transmit beamforming after range compensation, the echo from the desired range region can be extracted from ambiguous echoes. Then, the back projection (BP) algorithm is used to achieve imaging. Next, the echoes of all channels are processed by two receive beamformers, which are designed for the right and left sides, respectively. With the aforementioned procedures, an unambiguous image can be obtained. Simulation results have verified the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A new approach is addressed for Doppler ambiguity resolution. This method exploits the fact that, in compressed range time and special scaled azimuth time domain, all the trajectories exhibit the same slope, which is proportional to the Doppler ambiguity number. The method can directly estimate the ambiguity number without a priori knowledge on the baseband Doppler centroid. It is confirmed that this approach is robust to the partially exposed strong targets in azimuth, ground slowly moving targets and the motion variation of platform in realistic scenarios. Results of real measured data processing show that this method works well in medium- to high-contrast scenes and can basically (not always) provide the right estimate in low-contrast scenes. [ABSTRACT FROM AUTHOR]