Superresolution versus Motion Compensation-Based Techniques for Radar Imaging Defense Applications

Inverse synthetic aperture radar (ISAR) is an all-weather radar technique which may generate high-resolution images of noncooperative targets. The standard range-Doppler algorithm (RDA) is usually employed for image generation. However, the images obtained with RDA are usually blurred because of the relative motion between radar and target. As a consequence, motion compensation techniques should be used to improve the imagery quality. The range-instantaneous-Doppler (RID) technique based on time-frequency transforms has been proposed for obtaining a sequence of focused ISAR images without the need of using motion compensation techniques. However, in this letter, it is clearly shown that the migration of the target scatterers in slant range indirectly induces Doppler spreading of the scatterers' point spread function in each of the ISAR images obtained by the RID technique. This Doppler spreading means blurring. It is important to highlight that the migration in slant range may be caused not only by the radial component of the translational motion but also by the rotational motion. The application of motion compensation techniques prior to the use of the RID technique allows us to mitigate the Doppler spreading, as shown here both for simulated and live data acquired by a high-resolution coherent radar.

This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica.

Ecosystems provide several services for human well-being. The quality of these ecosystem services is among others affected by agriculture, the main land user in Europe. Thus, it is essential to provide information about land use in agricultural areas. In this research, land use classification of agricultural areas is carried out based on high-resolution Spotlight TerraSAR-X images (TSX-images) of two different polarisations (HH and VV). A stack of several radar images taken during the vegetation season is used for multi-temporal classification of land cover. The typical phenology of agricultural vegetation types and their individual phenological development during the year cause differences in the backscatter of the radar signal over time. Two different study areas are investigated, one in the North East of the city of Hannover, Germany called “Fuhrberger Feld” and one in the “Gorajec area” in the very South East of Poland. These two areas represent extremely diverse European regions with regard to agro-technological level, population density, cultivation form as well as geological and geomorphological conditions. Thereby, the radar signal backscatter for different regions is tested. Preliminary results show significant differences in the backscatter of crop types in SAR data of about 3 m, especially for grasslands, grain and broad-leaved crops. Furthermore the VV polarised radar signal has clearly lower backscattering for grains during summertime and for grasslands in general than for broad-leaved crops.

A single algorithm for various radar imaging applications is presented. The idea behind the proposed approach is that any motion between radar and target can always be modelled as a composition of translational and rotational components. Novel techniques to estimate and compensate these two motion components are discussed and also validated by application to real radar data for different targets (ships, aircraft, ground). By the translational-rotational-motion-compensation algorithm, good results can be achieved, especially in cases characterised by evident variation of the rotation rate during the coherent processing time for imaging. This usually happens in aircraft, ship or ground mover inverse synthetic aperture radar (ISAR) imaging as well as in squinted or forward-looking SAR. In any case, the efficiency and the accuracy of the algorithm presented for translational motion compensation might be the initial step of alternative radar imaging processing. The simplicity of the proposed algorithms makes them suitable and convenient for real-time application even for compact and low-weight radar systems to be fitted on board unmanned aerial vehicles (UAV) or other airborne platforms. The proposed concept is the result of the authors' decennial experience devoted to achieve a single, simple, robust, reliable and efficient signal processing algorithm for several real-time radar imaging applications.

The problem of radar imaging is considered in this paper. An automobile is used as a platform for radar system mounting. It produces some special requirements for the data acquisition and processing, including the necessity for motion compensation. A motion compensation technique is offered in the paper and applied to the real-life data. The results demonstrate the efficiency of the suggested processing technique.

Since most of vehicular radar systems are already exploiting millimeter-wave (mmWave) spectra, it would become much more feasible to implement a joint radar and communication system by extending communication frequencies into the mmWave band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE 802.11ad waveform to a vehicle for communications while the RSU also listens to the echoes of transmitted waveform to perform inverse synthetic aperture radar (ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs to accurately estimate round-trip delays, Doppler shifts, and velocity of vehicle. The proposed ISAR imaging first estimates the round-trip delays using a good correlation property of Golay complementary sequences in the IEEE 802.11ad preamble. The Doppler shifts are then obtained using least square estimation from the echo signals and refined to compensate phase wrapping caused by phase rotation. The velocity of vehicle is determined using an equation of motion and the estimated Doppler shifts. Simulation results verify that the proposed technique is able to form high-resolution ISAR images from point scatterer models of realistic vehicular settings with different viewpoints. The proposed ISAR imaging technique can be used for various vehicular applications, e.g., traffic condition analyses or advanced collision warning systems.

The purpose of this study is to investigate the dosimetric accuracy of prostate SBRT when motion is considered. To account for target movement, motion compensation and gating techniques were investigated with PTV margins reduced to 2 mm. To allow for dosimetric measurements a Delta4 phantom, Gafchromic film, and Hexamotion motion platform were utilized. Four motion files were utilized that represent a range of motions. Analysis of measured prostate motions for fifteen patients was performed to ensure detected motions were similar to those previously reported and motion files utilized were suitable. Five patient plans were utilized to allow for the effects of MLC and target motion interplay to be investigated. For both motion compensation and gating techniques, plans were delivered to the stationary phantom and for each of four motion types with/without compensation/gating enabled. Using a 3%, 2 mm and 80% threshold gamma criteria, film measurements had an average pass rate of 80.5% for uncorrected deliveries versus 96.0% for motion compensated deliveries. For gated techniques average pass rates increased from 89.9% for uncorrected to 94.8% with gating enabled. Measurements with the Delta4 arrays were analyzed with a 3%, 2 mm and 10% threshold dose. An average pass rate of 83.8% was measured for uncorrected motions versus 94.8% with motion compensation. For the gated technique an average pass rate of 87.2% was found for uncorrected motions versus 96.9% with gating enabled. These results show that very high gamma pass rates are achievable when motion compensation or gating techniques are applied. When target motion is not accounted for shifts up to 5 mm in planned versus delivered isodose distributions were found. However, when motion compensation, or gated techniques were applied, much smaller differences between planned and delivered isodose distributions were found. With these techniques dose delivery accuracy is greatly improved, allowing for PTV margins to be reduced.

The high orbit height leads to the ultralow signal-to-noise ratio (ULSNR) of geosynchronous (GEO) targets, so it is necessary to increase the coherent processing interval (CPI) to improve the coherent accumulation gain. The ultra-long CPI (ULCPI) causes wide rotational angles and complex signal modulation, which poses a serious challenge to conventional inverse synthetic aperture radar (ISAR) imaging algorithms. Moreover, the high orbit height and high-speed motion of GEO targets make the “stop-and-go” model no longer applicable, and intra-pulse motion errors must be taken into consideration. To address the problems, this article proposes a high-resolution ISAR imaging algorithm with joint intra-pulse and inter-pulse high-order motion compensation (JIPHOMC) and sub-aperture images fusion for GEO targets at ULCPI. In this technique, ULCPI is divided into several sub-apertures. Then, with respect to the motion compensation in sub-apertures, we innovatively propose a JIPHOMC algorithm with particle swarm optimization (PSO), which can correct the complex spatial-time-variant (STV) motion errors caused by wide rotational angles and high-speed motion, and eliminate the 2-D high-order coupling. By utilizing image overall constraints, JIPHOMC avoids signal source decomposition processing, thereby boosting algorithm efficiency and robustness. For the sub-aperture images obtained from different perspectives, we develop a sub-aperture image fusion algorithm (SAIF) based on non-negative matrix factorization (NMF) of structured weighted sparse enhancement, which can ensure the integrity of the target structure and enhance the image signal-to-noise ratio (SNR), so as to achieve high-resolution ISAR imaging of GEO targets at ULCPI. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data corroborate that the proposed algorithm outperforms traditional ISAR imaging methods for GEO targets at ULCPI.

Inverse synthetic aperture radar (ISAR) imaging needs a long coherent processing interval (CPI) to obtain high cross-range resolution. However, the Doppler frequency is time-varying for a maneuvering target, which will produce blurred ISAR image in a long CPI. In this article, we focus on super-resolution ISAR imaging during an adaptive short CPI by using sequential sparse recovery. To enhance the performance of SSL0, a regularized SSLO (Re-SSLO) and an entropy-based stopping rule are presented. Besides, KT-based MTRC compensation, SVD-based dictionary whitening and rotation correlation-based cross-range scaling are introduced into the processing scheme. The superiority of the proposed method is validated by the experimental results based on simulated data.

ISAR imaging of target with complex motion associated with the fractional Fourier transform

To improve the safety of the railway system, the obstacle sensing radar system for railway is of great interest. High resolution radar can provide more information of the target, hence is suitable for the application in railway system. In this paper, we investigate the high resolution detection in railway radar. A double threshold generalized likelihood ratio test (GLRT) detector, which is robust over different scattering density, is proposed for the detection. The threshold selection strategy is also given to guarantee the constant false-alarm rate (CFAR). The proposed algorithm is verified by an experimental radar system whose bandwidth is 1GHz.

Self-calibration of a phased antenna array is required when distortions in the array are not known a priori and cannot be measured, or when turbulence in the propagation medium perturbs the radiation field. Two types of self-calibration procedures are discussed that have proved successful in experimental high-resolution two-dimensional microwave radar imaging. Each extracts information from the backscattered reradiation field to deduce a compensating weight vector for the phased array antenna. The first depends upon the presence of a strong reflector in the field of view of the transmitter. The second calibrates the array from correlation estimates of wavefront samples in the array. The basic algorithm in each group is described, along with two more sophisticated algorithms in the latter class. Two-dimensional radar images of airplanes are shown with resolution comparable to human vision. The performance of each algorithm and comparisons between them are illustrated by these images of targets and by simulation experiments.©1993 John Wiley & Sons Inc

Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped blocks concerning the azimuth-dependent residual errors. However, image domain post-filtering approaches, such as precise topography- and aperture-dependent motion compensation algorithm (PTA), have difficulty of robustness in declining, when strong motion errors are involved in the coarse-focused image. In this case, in order to capture the complete motion blurring function within each image block, both the block size and the overlapped part need necessary extension leading to degeneration of efficiency and robustness inevitably. Herein, a frequency domain fast back-projection algorithm (FDFBPA) is introduced to deal with strong azimuth-variant motion errors. FDFBPA disposes of the azimuth-variant motion errors based on a precise azimuth spectrum expression in the azimuth wavenumber domain. First, a wavenumber domain sub-aperture processing strategy is introduced to accelerate computation. After that, the azimuth wavenumber spectrum is partitioned into a set of wavenumber blocks, and each block is formed into a sub-aperture coarse resolution image via the back-projection integral. Then, the sub-aperture images are straightforwardly fused together in azimuth wavenumber domain to obtain a full resolution image. Moreover, chirp-Z transform (CZT) is also introduced to implement the sub-aperture back-projection integral, increasing the efficiency of the algorithm. By disusing the image domain post-filtering strategy, robustness of the proposed algorithm is improved. Both simulation and real-measured data experiments demonstrate the effectiveness and superiority of the proposal.

Sparse radar imaging seeks to provide high-performance radar imaging technique for observed scenes by using L 1/2 regularization method. This article surveys our systematic explorations and innovative practices for tackling such problem. The key contributions include a new sparse radar imaging theory based on L 1/2 regularization theory, a new sparse radar imaging model based on echo simulation operator, a new sparse radar imaging system evaluate approach based on 3D phase diagram. The synthetic aperture radar (SAR) imaging can be formulated as a L 1/2 regularization problem, which optimizes a quadratic error term of radar imaging. The method can implement SAR imaging effectively at lower sampling rates than Nyquist rate and produce higher quality images with reduced sidelobes. Echo simulation operator is constructed to generate SAR raw data by means of inverse of matched filter-based imaging procedure, instead of the exact observation matrix. The echo simulation operator based model forms a new radar imaging method, which reduces the computational complexity and memory occupation to the same order as matched filter imaging algorithm, and hence, makes L 1/2 regularization reconstruction of large-scale considered scene become possible. 3D phase diagram is employed to analyze and evaluate the performance of sparse radar imaging system. In 3D phase diagram, success rates of signal recovery can be derived from the statistics of relative errors between the ground truth and the recovered signals. The success rates for each combination of SNR, sparsity and sampling ratio are provided to form a three-dimensional phase diagram. The trend of success rates is presented accurately by taking advantage of 3D phase transition boundary. We design the sparse radar prototype and conduct a series of airborne experiments, which demonstrate the applicability and potentials of the sparse radar imaging. 3D phase diagram is used to evaluate the relationship between reconstruction performance and signal-to- noise ratio. The experimental results verity the effectiveness of the L 1/2 regularization method and the feasibility of 3D phase diagram. The sparse radar imaging based on L 1/2 regularization has promising application prospects. Firstly, it could be generalized to deal with 3D-SAR imaging, which achieves better flexibility for system design and super-resolution capability of height dimension for reconstruction property. Then, we review some of the most popular techniques to exploit sparsity, for recovering high-dimensional matrices and higher order tensors from compressive measurements, and further emphasize the broad application prospects of higher order compressive sensing in 3D-SAR imaging.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.