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The conventional range resolution improvement (RRI) technique combines two spectra of two received pulses with linear frequency modulation (LFM) in the range-frequency domain. However, its application to frequency-modulation continuous-wave (FMCW) radar is not straightforward. This study proposes an RRI method for two FMCW radars using cross-track interferometry (CTI), which are placed at different positions along the cross-track axis. Through the combination of the deramped signals of the two FMCW radars with the RRI, an improved high-resolution range profile (HRRP) was obtained. Furthermore, the simulation and experimental results validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The classification of moving vehicles on roadways is an important application of intelligent transportation systems. For successful vehicle classification using radar, suitable feature extraction with high accuracy from the returned echoes of moving vehicles is necessary. In this paper, a novel framework for predicting the length of each moving vehicle is presented based on two different scenarios of the frequency-modulated continuous-waveform (FMCW) radar, i.e., the radar is stationary or moving. Different clutter reduction techniques were considered depending on the motion of the FMCW radar. Using clutter-free signals, moving vehicles are separated on a range-angle map, rather than on a range-Doppler (R-D) map, enabling the successful separation whether the vehicles are overlapping or not on the R-D map. Finally, the scattering center information across multiple frames, rather than a single frame, is fused to significantly increase the accuracy of the predicted length. The proposed framework was demonstrated through experiments in an actual road environment using a commercial FMCW radar. The results indicated a good match with the true lengths of the vehicles. [ABSTRACT FROM AUTHOR]

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs. [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]

To perform 3-D inverse synthetic aperture radar (ISAR) imaging through interferometric processing, all ISAR images should be correctly registered such that the same scatterer appears at the same position along both the range and Doppler directions. However, this condition generally does not hold in most situations because different radar view angles introduce additional angular motion errors that must be compensated. Hence, we herein propose a new ISAR registration method based on joint-channel phase difference (JC-PD) operations. From the result of the JC-PD function, JC-PD profiles are obtained, in which the locations and phases of the maximum peak contain constant and time-varying angular motions, respectively. Using our proposed method, correct ISAR registrations can be achieved in both the range and Doppler directions. In particular, compared to conventional Doppler registration methods, time-varying angular motions are more accurately compensated without the problem of cross-term phase interference. Through simulations and experiments, we verified that the proposed method yielded accurate ISAR registration results; hence, an excellent 3-D target reconstruction through interferometric ISAR imaging was demonstrated. [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]

The micro-Doppler phenomenon in the echo signal received from a ballistic target (BT) with micro-motion is commonly used to discriminate BTs such as warheads and decoys. The joint time-frequency (JTF) analysis of the echo signal has been considered as useful two-dimensional (2-D) information in BT discrimination, which generally requires a framework based on the processing of the 2-D JTF image with various conventional feature extraction techniques. However, these techniques are inefficient for time-critical BT discrimination task due to the complicated 2-D image processing. In this paper, we propose new echo signal models to formulate the fundamental difference between the micro-motions of warheads and decoys, leading to a novel BT discrimination framework via new feature extraction paradigm and multi-aspect fusion concept. The most attractive attribute of this framework is that it can provide substantial savings with regard to computational resources as well as robustness to noise. The experimental results illustrate that the proposed discrimination scheme shows considerable promise for application in real-time BT discrimination. [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]

An echo signal received from a cone-shaped target with micro-motion is composed of a linear mixture of individual signals radiated from multiple effective scatterers with the occlusion effect, resulting in difficulties in parameter estimation for ballistic target discrimination (BTD). To solve this, conventional methods have been based on the sophisticated signal decomposition techniques using a 2D joint time–frequency (JTF) image or a 2D radial-range (RR) history image; however, they are inefficient for real-time BTD due to complex 2D image processing. Therefore, we propose a new parameter estimation framework consisting of five stages: 1) a normalization step; 2) signal decomposition and data association using independent component analysis in the distributed radar network; 3) estimation of dynamic parameters using 1D micro-Doppler frequency trajectories; 4) restoration of 1D RR histories; and 5) estimation of geometric parameters using the restored 1D RR histories. In particular, ICA of stage 2 is more time-saving than the conventional mathematical model-based methods using the 2D JTF image due to signal decomposition using the 1D normalized echo signals. Moreover, in the stage 4, high-quality 1D RR histories can be restored in spite of using the 2D RR history image with low resolution, compared with the conventional methods using 2D RR history image of very high resolution. In the simulations, we observed that our proposed framework is capable of performing efficient parameter estimation for the real-time BTD. [ABSTRACT FROM AUTHOR]

When a target undergoes nonuniform rotational motion (RM), conventional motion compensation (MOCOM) approaches for monostatic inverse synthetic aperture radar (ISAR) systems fail to correct the nonuniform RM error in bistatic radar systems; this failure is caused by nonlinear phase relationships between scatterers in bistatic ISAR (Bi-ISAR) systems. To address this problem, in this paper, we propose a new MOCOM framework for Bi-ISAR imaging of nonuniformly rotating targets. In the proposed method, a newly devised correction is performed to ensure successful RM compensation, followed by translational motion compensation. Because the phase relationships of the scatterers become virtually linear after this correction, nonuniform RM can be successfully converted into uniform RM. Namely, using the proposed method, translational and rotational motion errors are correctly removed, and focused Bi-ISAR images are obtained even for a target involved in nonuniform RM. Furthermore, for more effective use of Bi-ISAR images, a method to restore sheared Bi-ISAR images is exploited in the proposed framework. In the simulations and experiments, we determined that the proposed method can provide high-quality Bi-ISAR images for nonuniformly rotating targets. [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]

This paper proposes a new method to automatically recognize range profiles (RPs) and inverse synthetic radar (ISAR) images of targets flying in formation, despite overlapping of the RPs and ISARs in a single radar beam. On contrary to the existing methods to recognize by separating each target, the proposed method utilizes the combined RP and the combined ISAR image obtained by fusing the RPs and the ISAR images in a training database based on the flight direction of the targets. Experimental results using the measured data of five scale models prove the validity of the proposed method. [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]

High-quality solution-derived amorphous alumina (${a}$ -Al2O3) dielectric has been achieved with [Al $_{\textsf {13}}{(}\mu _{\textsf {3}}$ -OH ${)}_{\textsf {6}}{(}\mu $ -OH)18 (H2O)24](NO3)15(Al-13 nanocluster) as a precursor and a local structure-controllable activation process via deep-UV-induced photochemical activation. The synergetic combination of an Al-13 nanocluster precursor and high-energetic photochemical activation enables the formation of highly dense ${a}$ -Al2O3 thin films via an efficient dissociation and rearrangement of the nanocluster skeleton. The electrical characteristics of the nanocluster-based ${a}$ -Al2O3 thin films were investigated in terms of their operative electronic conduction mechanism by comparing conventional nitrate-based and vacuum-deposited films. From these results, it was found that the leakage current density of solution-processed ${a}$ -Al2O3 layers is largely affected by their precursor structures. Finally, to demonstrate the versatility of the high-quality nanocluster-based ${a}$ -Al2O3 dielectrics, carbon nanotube and metal-oxide thin-film transistors were fabricated on low thermal budget stretchable and rigid substrates, respectively. [ABSTRACT FROM AUTHOR]

In this study, we propose an efficient strategy to exploit cross-range resolutions (CRRs) of inverse synthetic aperture radar (ISAR) images to improve classification performance. The proposed method preprocesses an ISAR image of an unknown target using both the CRR of an unknown ISAR image, which can be obtained using the cross-range scaling algorithm, and that of the ISAR images in a training database (DB). Specifically, the proposed method adjusts the unknown ISAR image so that the CRR of the adjusted unknown ISAR image is identical to that of the ISAR images in the training DB. The adjusted unknown ISAR image is then directly used for the subsequent classification task, thus considerably improving classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cross-range scaling technique for inverse synthetic aperture radar (ISAR) images, by estimating the rotation velocity (RV) of a target. In the proposed method, the major axes of two consecutive ISAR images are determined using the Radon transform and its projection onto the angle direction. We then establish an appropriate cost function using the major axes. Finally, the RV is estimated using the bisection algorithm. Experimental results show that the proposed method can perform robust cross-range scaling in a short computation time, even in a low signal to noise ratio environment. [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]

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. To address this problem, phase nonlinearity can be used to select imaging frames where target 2-D motion is dominant enough to obtain focused ISAR images. Conventionally, adaptive joint time frequency (AJTF) and particle swarm optimization (PSO) algorithms are performed to estimate the phase signals of prominent scatterers. These estimates are used to determine phase nonlinearity. However, such estimation algorithms undergo multi-dimensional optimizations, which yield significant increases in computational complexity for ISAR frame-selections. For more practical use of AJTF- and PSO-based frame-selections, we introduce a method to improve the computational efficiencies of the conventional methods. To this end, in the proposed method, a simple phase unwrapping-and-least square fitting method is used as an alternative to the AJTF and PSO methods. Because PU-LSM can provide reliable phase estimates quickly without complex optimization procedures, the proposed method is an improvement over AJTF- and PSO-based methods, especially in terms of computational efficiency. In simulations using ideal point scatterers, our proposed method performs well, in terms of frame-selection ability and computational efficiency. Furthermore, experimental results using real radar echoes validated the robustness and efficacy our proposed frame-selection method. [ABSTRACT FROM PUBLISHER]

Conventional approaches to inverse synthetic aperture radar (ISAR) imaging of a non-uniformly rotating target are not optimal in terms of reconstructed image quality and/or computation time. Motivated by the problems of conventional approaches, we introduce an efficient autofocus chain for ISAR imaging of a non-uniformly rotating target. The most distinct feature of the proposed autofocus chain is found in the processing sequence to compensate for the phase error induced by the translational and rotational motion (RM) of the target. In contrast to conventional approaches, RM compensation is first implemented in the proposed autofocus chain immediately after range-alignment. Next, further focusing of the ISAR image is performed by estimating the residual phase errors, providing globally well-focused ISAR images. From the experimental results using real data sets, we can conclude that the proposed autofocus chain is highly efficient in forming ISAR images of a non-uniformly rotating target in terms of both image quality and computational efficiency. [ABSTRACT FROM PUBLISHER]

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted much attention because of its potential to overcome the certain limitations of monostatic ISAR imaging. However, range and cross-range scaling and bistatic distortion correction are essential for the efficient use of a bistatic ISAR (Bi-ISAR) image in their applications. In this paper, we introduce a novel range and cross-range scaling technique and bistatic distortion correction method for the restoration of a sheared (Bi-ISAR) image. The proposed method is composed of five stages: 1) extraction of the prominent scatterer using the range-bin selection method; 2) determination of the coefficients of the phase-history data in the selected range bin; 3) estimation of rotation center of the target using the optimization procedures via the particle swarm optimization algorithm; 4) range and cross-range scaling of (Bi-ISAR) image; and 5) restoration of the bistatic distortion that caused the sheared (Bi-ISAR) image. Finally, the proposed method can generate a well-scaled and restored (Bi-ISAR) image. An outstanding advantage of this method is that it requires no prior information about the bistatic configuration. Several examples are presented to validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

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]

This paper proposes a new scheme for improving the classification performance of inverse synthetic aperture radar (ISAR) images. The proposed scheme utilizes the trace transform to gather abundant information from an ISAR image, regardless of the spatial distribution of the target response in the ISAR image. In simulations using ISAR images with various spatial distributions, the proposed scheme substantially improved the classification performance compared with existing methods, which are highly vulnerable to spatial variation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new and efficient inverse synthetic aperture radar (ISAR) autofocus technique by introducing eigenimages to boost the speed of the traditional autofocus algorithms. First, a preprocessing step is applied to mitigate the noise components in the received data. Then, we perform an eigen-decomposition of the covariance matrix of the range-aligned data, and generate the signal eigenimage obtained by deriving the Fourier transform of a small number of eigenvectors corresponding to the dominant eigenvalues. Finally, traditional autofocus methods are combined with the proposed signal eigenimage rather than the original ISAR image to eliminate image blurring due to phase errors. The proposed method can significantly lower the computational complexity of the traditional autofocus methods because the dimensionality of the signal eigenimage is considerably smaller than that of the ISAR image. Despite the low dimensionality of the signal eigenimages, the proposed scheme provides well-focused ISAR images that are comparable to those of the traditional autofocus methods in terms of image focal quality. Several simulations and experimental results using measured data of an actual flying aircraft are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data. [ABSTRACT FROM AUTHOR]