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Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished. [ABSTRACT FROM AUTHOR]

Moving target indication (MTI) is considered one of the most important applications of synthetic aperture radar (SAR) in military operations and traffic monitoring. Although many studies have focused on detectors based on the statistical clutter models, a mismatch between the measured data and the applied statistical model often leads to unreliable MTI results, particularly in terms of false alarm rates. To reduce the number of false alarms, we propose an efficient MTI scheme consisting of conventional MTI techniques (the displaced phase center antenna (DPCA) and the interferogram’s magnitude and phase (IMP) methods), which operate in dual-receive antenna (DRA) mode in an SAR system. These techniques are coupled with a new discrimination stage based on a new detection metric—weighted kurtosis. Using simulated and real measured data from TerraSAR-X, the proposed MTI scheme demonstrates robust performance in the reduction of false alarm rates with only a slight increase in the computation time compared with conventional schemes. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image registration is a process of geometrically aligning two or more remote sensing images, acquired at different times, from different viewpoints or from different sensors. To solve the problem that determines which transformation provides the most accurate match between two images, we propose a novel Tsallis entropy-based approach combined with a sequential search strategy to significantly reduce the computational complexity compared to the existing methods, while retaining excellent SAR registration performance. The Tsallis entropy can be considered as a kind of general version of similarity metric, depending on the order of Tsallis entropy. Thus, we use Tsallis entropy as a cost function to measure the degree of the focus of an average intensity projection profile of SAR image. The global optimum of the similarity metric should be reached if the reference and sensed images are correctly registered. The proposed method consists of coarse and fine registration steps, and each step is divided into two parts: range and azimuth domain processing. From the experimental results, we verify that the proposed method outperforms conventional methods in terms of computational complexity of the algorithm owing to the sensitivity of the cost function and efficiency of sequential search strategy. [ABSTRACT FROM AUTHOR]

Ground moving target imaging (GMTIm) is considered one of the most important applications of synthetic aperture radar (SAR). Phase modulation from a moving target’s higher- order movements severely degrades the focusing quality of SAR images, because the conventional SAR-GMTIm algorithm assumes a constant target velocity in high-resolution GMTIm with single channel SAR. To solve this problem, a novel SAR-GMTIm algorithm in the compressive sensing (CS) framework is proposed to obtain high-resolution SAR images with highly focused responses and accurate re-location. After taking direct action on data in the defocused region of interest (ROI) from the entire image scene, CS theory is used to decompose an SAR-GMTIm signal into a set of the polynomial basis functions to remove the various phase errors related to higher- order movements. A modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme is adopted to determine the motion parameter and reconstruct the sensing dictionary matrix. We can generate a refocused image of SAR-GMTIm from the complete SAR-GMTIm signal recovered using the proposed method. Finally, simulated and real measured SAR data are used to validate the effectiveness and superiority of the proposed method for SAR-GMTIm. [ABSTRACT FROM AUTHOR]

The detection of moving targets on synthetic-aperture radar images is one of the most popular approaches to monitor ground vehicles and ships on an ocean. Although the well-known displaced phase centre antenna (DPCA) or along track interferometry (ATI) has good detection performance, they often produce high false alarm rates. A new scheme, i.e. a moving target indicator (MTI) using the sequential combinations of DPCA and ATI to reduce the number of false alarms is suggested. The simulation results show that the proposed MTI scheme can provide a low false alarm rate, while maintaining good detection performance. [ABSTRACT FROM AUTHOR]

In this study, a novel autofocus method is presented to achieve high-resolution inverse synthetic aperture radar (ISAR) image reconstruction with limited measurements in the compressive sensing (CS) framework. In order to solve the CS reconstruction problem of ISAR images with sparsity, the CS coupled with Tikhonov-regularisation-based algorithm is devised as an effective focusing technique. The proposed method aims at compensating the phase errors induced by target motions, and can provide globally well-focused ISAR images. Simulation and experimental results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In general, conventional synthetic aperture radar (SAR) imaging algorithms focus SAR images for stationary targets. However, in the case of moving targets, the motion of the targets induces an undesired range variation which makes the obtained SAR images to be dislocated and defocused. Therefore, in this study, the authors propose a method that effectively focuses the SAR images for the moving targets and estimates their motion parameters with single-channel SAR system. The proposed method consists of the following three steps: (i) direct exploitation of the range variation from the echo signal, (ii) range cell migration compensation with the estimated range information, and (iii) Doppler compensation and parameter estimation. The simulation results demonstrate the robust performance and high computational efficiency compared to the conventional moving target imaging algorithms. [ABSTRACT FROM AUTHOR]

For synthetic aperture radar (SAR) imaging, the compressive sensing (CS) coupled with total variation (TV)-based algorithm is known as an effective focusing technique using under-sampled dataset. However, the performance of the CS-TV method can be degraded by drawbacks of TV in terms of noise sensitivity and computational efficiency. In this paper, a novel approach to CS-SAR imaging is proposed based on improved Tikhonov regularization (ITR) coupled with an adaptive strategy using iterative reweighted matrix to solve the CS reconstruction problem of SAR images with sparsity. The proposed method can provide different degrees of performance of SAR autofocus with changes to the value of certain parameters of ITR. The proposed scheme outperforms conventional CS-based methods with respect to image quality, noise robustness, and computational complexity of the algorithm owing to the additional sensitivity of the proposed objective function. From the simulation results, we verify that the proposed autofocus method is highly efficient in forming SAR images from non-uniformly under-sampled dataset in terms of both image quality and computational efficiency. [ABSTRACT FROM AUTHOR]

We propose a novel class separability measure for radar signals. To evaluate the discriminatory power of the radar signals, the proposed method first either calculates the correlation coefficients between two radar cross sections (RCSs), linearly shifts one‐dimensional (1‐D) high‐resolution‐range profiles (HRRPs), or rotates two 2‐D inverse synthetic aperture radar (ISAR) images. Then, it uses the correlation coefficient when the two radar signals are best aligned. Next, the proposed method obtains new correlation‐ based discriminant matrices (CDMs) using the correlation coefficients. The mean value of the CDMs precisely represents the discriminatory power of the radar signal. Our experimental results show that the proposed method can accurately measure target separability. [ABSTRACT FROM AUTHOR]

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted considerable attention because of its potential for overcoming certain monostatic ISAR imaging limitations. However, translational motion compensation (TMC) and rotational motion compensation (RMC) are the most fundamental requirements for both bistatic ISAR (Bi-ISAR) and monostatic ISAR imaging to obtain the high levels of image resolution desired. Moreover, range and cross-range scaling (RCRS) and bistatic distortion correction (BDC) are essential functionalities for the efficient use of Bi-ISAR images in applications. In this paper, a novel approach to sparse aperture (SA) Bi-ISAR imaging is presented for simultaneously performing TMC, RMC, RCRS, and BDC on maneuvering targets based on compressive sensing. Instead of solving conventional optimization problems constrained by the sparsity of the signals, the proposed method utilizes sensing matrix estimation technique for Bi-ISAR image reconstruction using parametric signal model reconstruction by looking for the basis functions that best represent the behavior of sensing dictionary matrix comprised of the observed SA data. The reconstruction of the sensing matrix is based on a modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme. Finally, we generate a focused and scaled Bi-ISAR image from the complete Bi-ISAR signal recovered using the proposed method. Several simulation results reveals that the proposed method is very efficient in forming Bi-ISAR images of high-speed maneuvering targets in terms of the Bi-ISAR signal reconstruction accuracy. [ABSTRACT FROM AUTHOR]

For a synthetic aperture radar (SAR) system, the individual beam pattern pointing and shaping due to amplitudes and phase settings in transmit/receive modules (TRMs) are mainly used in the elevation direction over the total range of incidence angles. The criteria for the optimized antenna elevation beam pattern are profoundly linked to the overall SAR system performance requirements. In particular, a high sidelobe level (SLL) in the antenna pattern leads to a high range ambiguity-to-signal ratio (RASR), which degrades the quality of the SAR image. RASRs can be controlled by appropriate antenna SLL suppression at defined positions in the elevation pattern. This paper focuses on the improvement in the SAR system performance using an effective technique for optimizing antenna pattern synthesis. The desired antenna patterns can be synthesized referring to the optimized antenna mask templates using the newly devised cost function and improved particle swarm optimization (IPSO). Even though there are some defective TRMs in array phased antennas, one can regenerate an optimal pattern as close as possible to the desired one, owing to the proposed cost function and IPSO. Moreover, this paper provides a new perspective on RASR performance, which can be analyzed in SAR images, via the full-chain process from antenna pattern synthesis to SAR image formation. In addition, this is the first study to consider the problem of antenna pattern optimization with element failure, in terms of spaceborne SAR imaging, as well as SAR system performance. The simulations conducted in this study show that the optimized pattern obtained via the proposed technique can provide good SAR system performance, resulting in high-quality SAR images, even with defective TRMs. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS) method to more effectively use Bi-ISAR images in their applications. For this, we propose a method to estimate the effective rotation velocity of a target in a Bi-ISAR imaging system, and restore a linear-geometry distortion that yields a sheared shape of Bi-ISAR images. In the simulations, we observed that our proposed method is capable of performing robust and precise Bi-ISAR CRS. [ABSTRACT FROM AUTHOR]

ABSTRACT The enhanced shooting and bouncing ray (SBR) technique based on the bistatic approximation, which is called the one-shot inverse synthetic aperture radar (ISAR) technique, has been devised to accelerate the conventional monostatic SBR (CM-SBR) technique, which is suitable for inverse synthetic aperture radar (ISAR) imaging. However, because of the bistatic approximation, this method results in the distortion of ISAR images. Therefore, to obtain an accurate image, radar returns should be obtained from the monostatic configuration. This paper describes a new method, the ray-travel map (RTM), which accelerates the CM-SBR technique while simultaneously considering both the CM-SBR and the ISAR configuration. Then, we demonstrate that the proposed method can significantly enhance the speed of CM-SBR, while providing accurate distortion-free ISAR image. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1848-1855, 2016 [ABSTRACT FROM AUTHOR]

ABSTRACT This study evaluates and compares the reconstruction accuracy of sparse recovery algorithms (SRAs) when we reconstruct a two-dimensional (2D) inverse synthetic aperture radar (ISAR) image from a gapped radar-cross-section (RCS) dataset. To analyze the influence of the gap on the reconstruction accuracy, we adopted a relative error (RE) in a 2D image domain, and evaluated the reconstruction accuracy of each method by calculating each RE value against the rate of missing data in the gap. The accuracy of the basis pursuit denoising method was better than that of other methods considered in this study, especially when we reconstructed the ISAR image from the gapped RCS dataset when signal-to-noise ratio was low. Consequently, the relaxed constraint rather than the strictly equality constraint for the SRA is more adequate for SRA applied to radar imaging. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:1249-1255, 2015 [ABSTRACT FROM AUTHOR]

In this study, we propose a modified polar mapping classifier (M-PMC) with an efficient preprocessing scheme to classify synthetic aperture radar (SAR) targets from the public release database for moving and stationary target acquisition and recognition. M-PMC is evaluated under various SAR extended operating conditions (EOCs), which are designed to reflect real-world battlefield scenarios. Experimental outcomes from various EOCs show that M-PMC has good generalization capabilities and outperforms conventional classifiers. [ABSTRACT FROM AUTHOR]

We propose new features and a redundancy-free feature selection scheme for discriminating targets from clutter in high-resolution synthetic aperture radar imagery. These were found to be very useful for discriminating targets from clutter and well combined with various classical discriminative features. If a feature set with a larger dimension is used, its redundancy may increase. In order to reduce this redundancy, a rank-based feature selection scheme is devised. Our discriminating features and feature selection algorithm are evaluated using the moving and stationary target acquisition and recognition (MSTAR) data set. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method that solves the problem of inverse synthetic aperture radar image cross-range scaling by estimating the rotational velocity (RV) using the expansion–rotation–scale relationship between two range–Doppler (RD) images. This method is composed of three steps. The first step is preprocessing to construct 2-D Fourier transform images and initial polar-mapped images. In this step, two RD images are 2-D Fourier transformed to avoid the necessity of finding the rotation center; then, the transformed images are polar mapped with identical polar grids to convert rotation into translation in the \theta-direction only. The second step is a coarse search that finds the angular shift that provides the maximum correlation between two polar images. The angular shift found is used as the initial relative rotation angle (RA), and the initial relative scaling factor (RSF) is calculated using the RV which is equal to the angular shift divided by the time delay between the images. The third step is the optimization of the relative RA using the Nelder–Mead approach, with the RSF updated using the relative RA derived during each iteration. In simulations using a Mig-25 aircraft, composed of ideal point scatterers, and the measured data from a Boeing 747-400, the targets were properly rescaled in the range–cross-range domain due to the accurate estimation of the RV. [ABSTRACT FROM AUTHOR]