MTRC compensation in high-resolution ISAR imaging via improved polar format algorithm based on ICPF

The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with less training samples. Hence, the authors are interested in exploiting and modifying the U-net to enhance the P-band ISAR imaging. In this study, in light of the analysis of the effect of the ionospheric total electron content on the ground-based P-band radar echo signal, a novel ISAR imaging method is proposed for the ionospheric effect correction based on the modified U-net and polar formatting algorithm (PFA). The PFA is performed for the phase error coarse compensation. Then, the phase error fine compensation is exploited by the trained U-net. The proposed method can adapt the ionosphere disturbances and show high performance in imaging quality and computational efficiency. The simulation results show the effectiveness of the proposed method.

Migration through resolution cells (MTRC) happened in large rotation angle inverse synthetic aperture radar (ISAR) imaging can be effectively eliminated by Polar Format Algorithm (PFA) under the far-field condition. However, traditional PFA is sensitive to range error, so it is difficult to be implemented into engineering. Aimed at this problem, a modified polar format algorithm for ISAR imaging is proposed in this paper. Firstly, echo data is compressed to one-dimensional (1-D) range profiles, envelope alignment and phase autofocus processing are applied to compensate the translational motion, and the target motion model is converted into a turntable model. Next, the rotation center of the target should be obtained, and its envelope would be compensated to range zero. Then, two 1-D interpolation operations are applied to process data from polar sector support region to Cartesian rectangular grid. Finally, Fast Fourier Transform (FFT) operations are carried out to get well focused the ISAR image. The new method needs no range information, and therefore the image quality is not affected by the range errors. Theoretical analysis and simulation results verified the effectiveness of the algorithm proposed in this paper.

Inverse Synthetic Aperture Radar (ISAR) is generally used for classification of ship targets after detection and tracking by the Radar. There are various methods for high resolution 2D imaging of maritime targets in ISAR mode like Range Doppler Algorithm and Polar Format Algorithm. After the raw image is formed, it has to pass through the post processing algorithm suite before an acceptable image can be displayed to observer. This paper presents an efficient algorithm suite for the post processing of ISAR image.

In this work, we carry out the inverse synthetic aperture radar (ISAR) imaging of a wind turbine. ISAR is an established technique for examining the radar signatures of targets in relation to its geometrical features. ISAR imagery can often provide revealing information about the scattering phenomenology and any motion anomalies associated with the target. Summary form only given. The standard ISAR scenario assumes the target to be under constant rotational motion - which is already present in the relatively constant rotation of the turbine blades. Therefore, it is possible to collect the needed data from an operating wind turbine without any additional sensor movement.

This paper presents a method of unsupervised segmentation for Inverse synthetic aperture radar (ISAR) images. Firstly, a generalized multiresolution likelihood ratio (GMLR) is defined, which classifies different kinds of signals more accurately than classical likelihood ratio by fusing more and different signal features. For our ISAR image segmentation application, multiresolution stochastic structure inherent in ISAR imagery is well captured by a set of multiscale autoregressive (MAR) models. Secondly, good parameter estimates of GMLR can be obtained by estimating several mixture multiscale autoregressive prediction (MMARP) models using EM algorithm. Thirdly, considering the independence assumption of maximum likelihood estimation of parameter by EM algorithm and reduction of the segmentation time, we present the bootstrap sampling techniques applied above algorithm. Finally, Experimental results demonstrate that our algorithm performs fairly well.

A method to reduce the blurring effects of angular rotation for ISAR (inverse synthetic aperture radar) imagery is presented. The method uses the golden-section-search algorithm in conjunction with a polar reformatting technique to find that angle which best represents the angle of rotation of the target of interest. Minimum entropy calculations are performed for each ISAR image over each angle of rotation obtained from the golden-section-search algorithm. The angle which is found to display minimum entropy will then be designated theta /sub opt/ the optimum angle of rotation estimate for which the polar-reformatted image has minimal image distortion due to angular rotation. The technique was shown to perform well under simulated conditions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The authors describe recent research on ISAR (inverse synthetic aperture radar) signatures of ground vehicles at UHF. The method for representing the scattering characteristics of the vehicle is reviewed, and a computer code for predicting SAR and ISAR imagery is described. Computer simulated ISAR imagery is compared to imagery obtained from measured data. It is concluded that the scattering center model and associated computer prediction code have proved to be useful and versatile methods of simulating wideband SAR and ISAR signatures at UHF (and higher) frequencies. >

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.

In this paper, we propose and investigate a model based on asymptotic methods, designed to compute the radar cross section (RCS) of complex targets. A combined method of physical optics, geometrical optics, and the equivalent current method is applied to establish an effective backscattering analysis procedure. The scattering model includes shadowing effects, multiple bounce, and diffraction by edges. This model is used for RCS estimation and to generate inverse synthetic aperture radar (ISAR) raw data for imaging applications. We review the theoretical aspects and describe the proposed model and experimental setup in detail. Numerical results are provided and compared with the experimental results obtained in the anechoic chamber of ENSTA Bretagne to validate the approach through the computation of the RCS for canonical objects and a generic boat. Finally, a parallelepiped representation of the RCS is presented to be used for ISAR imagery, and the first results concerning the ISAR imaging of a generic boat are provided.

To address the challenge of achieving high-precision and ordered calibration of strong-scatter points in inverse synthetic aperture radar (ISAR) images, this paper proposes a collaborative framework that integrates YOLOv12-pose with Peak-Constrained Watershed (PCW). The method first employs the YOLOv12-pose model to produce an initial localization of scatter points. PCW is then applied to fine-segment individual points. Finally, a three-stage global optimal matching strategy is introduced to achieve high-precision fusion between index labels and their geometric positions. Experimental results on a microwave photonic radar ISAR dataset demonstrated that the proposed method achieved an average error of 1.89 pixels, with accuracy, recall, and F1 scores exceeding 95%. The approach significantly outperformed standalone YOLO, Mask R-CNN, and traditional SVM-based methods while maintaining label consistency and substantially improving precision and robustness for the recognition, localization, and tracking of strong scatter points in ISAR imagery.

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.

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.

ABSTRACTThe range alignment technique is extended and applied to full polarization processing technique using the fully polarized data for a target. By the maximum likelihood-based span, the fused high-resolution range profile (HRRP) is achieved. Then the range alignment is carried out on the fused HRRPs and the offsets are used to align the original HRRPs of each polarimetric channel. Applying this technique, the signal-to-noise ratio (SNR) of the HRRP is increased and the performance of the range alignment is improved in comparison with that of the conventional techniques using singly polarized data, and therefore the inverse synthetic aperture radar images of the target can be clearly obtained even in low SNR condition. Experiments from measured data under field conditions are presented to validate the proposed technique.

W-band inverse synthetic aperture radar (ISAR) imaging systems are very useful for automatic target recognition and classification due to their high spatial resolution, high penetration and small antenna size. Broadband linear frequency modulated wave (LFMW) is usually applied to this system for its de-chirping characteristic. However, nearly all of the LFMW generated in electronic W-band ISAR system are based on multipliers and mixers, suffering seriously from electromagnetic interference (EMI) and timing jitter. And photonic-assisted LFMW generator reported before is always limited by bandwidth or time aperture. In this paper, for the first time, we propose and experimentally demonstrate a high-resolution W-band ISAR imaging system utilizing a novel logic-operation-based photonic digital-to-analog converter (LOPDAC). The equivalent sampling rate of the LOPDAC is twice as large as the rate of the digital driving signal. Thus, a broadband LFMW with a large time aperture can be generated by the LOPDAC. This LFMW is up-converted to W band with an optical frequency comb. After photonic-assisted de-chirping processing and data processing to the echo, a high-resolution two-dimension image can be obtained. Experimentally, W-band radar with a time-bandwidth product (TBWP) as large as 79200 (bandwidth 8 GHz; temporal duration 9.9 us) is established and investigated. Results show that the two-dimension (range and cross-range) imaging resolution is ~1.9 cm × ~1.6 cm with a sampling rate of 100 MSa/s in the receiver.