ISAR image reconstruction from periodically gapped data

Inverse synthetic aperture radar (ISAR) imaging is typically useful when there is a need to classify, recognize, or identify a moving target of interest. In fact, an ISAR image highlights two-dimensional (2-D) geometric features of a target, which can provide indications of target's type, size, and other salient information. Such information can be then used for target classification, recognition, and identification. In this chapter, we will provide five case studies on applications of ISAR imaging. Such case studies are chosen to diversify the type of application and to give additional indications about how to interpret ISAR images. Specifically, in the first case study (Section 12.1), we show an example of ground-based ISAR images of a noncooperative sailing ship. The ISAR techniques used to form the ISAR images are the range-Doppler (Chapter 3) and the ICBA (Chapter 5) techniques. Different time windows are chosen to show the effects of the coherent processing interval (CPI) length on the ISAR image. Since the radar is ground based, this represents a case where the ISAR image is formed by exploiting only the sea surface induced target's motions. The second case study (Section 12.2) refers to a scenario where the radar is carried by an aircraft and the target is a ship at sea. In this case, both the target and the platform motions concur to the ISAR image formation. Also in this case, the range-Doppler and ICBA are used to form the ISAR image sequence. The third case study (Section 12.3) concerns dual ground-based/satellite ISAR imaging of a noncooperative sailing ship. In this experiment two radars are used to image a sailing ship at the same time. Two techniques are here used to form the ISAR images: a refocus technique (see Chapter 8) to form the satellite ISAR image; and a range-Doppler with ICBA to form the ground-based ISAR image. In this case study, we show how a dual system is able to form ISAR images of the same target with different target's views and projections. This is a simple case of multiple-perspective ISAR images, which may be looked at as a way to improve a target's classification and recognition [1]. The fourth case study (Section 12.4) shows ISAR images of four aircrafts obtained by processing data acquired with a ground-base radar. This case study may be regarded as the classic ISAR imaging example. Four different airplanes have been selected to show how their characteristics are mapped onto ISAR images. Such characteristics can then be used by classifiers for recognition purposes.

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.

Inverse synthetic aperture radar (ISAR) imaging technology is a powerful tool to distinguish between targets with different types. The high-resolution radar image acquisition is an important basis for further automatic target recognition. In the high-frequency radar band, since the spatially collected received signals have strong sparsity in the image domain (Fourier domain), they can be downsampled and restored by corresponding norm regularization framework. When the target is electrically large, its contributions are equal to several point scatterers [also called scattering centers (SCs)]. So that the imaging process can be regarded as the summation of the point scatterers impulse response, the convolution process, and deconvolution method, the reverse process is adopted to extract the SCs from the image, where the number of SCs reflects the sparseness of ISAR image. As for the reconstruction method, while L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization is widely used, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (0 <; q <; 1) regularization has proved to be a more sparse regularization framework and achieves better performance in reconstructing a signal with a lower down-sampling rate. This article implements L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization into the ISAR image reconstruction 2 under down-sampling. The ISAR image is generated by patch integration formula from the physical optics method. The sparsity of the ISAR image is estimated through the appropriate deconvolution process CLEAN. Given the sparsity estimated, and compared with the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization, the experiment of two complex geometry targets validates the reconstruction precision and convergence and lossless recovery probability of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> regularization.

In this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback–Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images’ databases demonstrate that the proposed approach leads to an improvement in the recognition rate.

Combining traditional inverse synthetic aperture radar (ISAR) imaging, interferometry technique and compressed sensing (CS), we studied the problem of interferometric ISAR imaging with sparse aperture. Based on the elaborate analysis of smoothed l0 algorithm in CS, we proposed joint smoothed l0 algorithm which is capable of maintaining coherence between ISAR images of different radars under sparse aperture, thus it could be applied to interferometric ISAR imaging. Besides, we discussed the relationship between sparsity degree and the precision of interferometric ISAR imaging result. Simulation results validate the effectiveness of proposed algorithm and rationality of our conclusions.

In this paper, we analyze the effect of roll, pitch and yaw rotations on inverse synthetic aperture radar (ISAR) imaging of small craft. An ideal ISAR image of a target with regular motion can be derived from the image projection plane and the radar line-of-sight (LOS) unit vector. Roll, pitch and yaw rotations can induce time-varying Doppler shifts that can be analyzed through the rotation matrix and the effective rotation vector. We use a commercial small craft ISAR data accompanied with roll, pitch and yaw data collected by the Trimble Tans Vector system with 4 GPS antennas to study the effect of rotational motion on ISAR imaging. For a target with regular motion, perturbations of roll and pitch motions may make for image blurring if conventional motion compensation is used. In this case, advanced imaging algorithms, such as the polar reformatting algorithm, that compensate the perturbations may improve the image.

The most widely used model for Inverse Synthetic Aperture Radar (ISAR) signal processing and image reconstruction is the point scatterer model. Instead, a line scatterer model is proposed and used in this paper for estimating significant states of a target, as well as to reconstruct a good image of the target. The line scatterer model is shown to contain information regarding the inclination angle and length of the target. Simulation studies for an aircraft show that the model also provides a good reconstructed image of the aircraft. It is shown that the line scatterer model offers a unified approach to the state estimation and imaging of a target.

Inverse synthetic aperture radar (ISAR) imaging of multiple warhead targets in the same antenna beam is a big challenge when they are overlapping in the same range bin. Existing methods are usually based on single target motion compensation and they can't get clear ISAR image of multiple targets. This paper presents a new algorithm for ISAR imaging of multiple targets based on Fractional Fourier Transform (FrFT) CLEAN method to separate signals of different targets. Firstly, signal model of multiple targets is given. Then analyzed each target has different Doppler history, the signals of a certain target in a range bin correspond to a batch of multicomponent liner frequency modulation (LFM) signals. Theoretical analysis and computer simulations show the proposed method can generate the images from multiple target echoes effectively.

Motion compensation (MOCOMP) is a key procedure in inverse synthetic aperture radar (ISAR) imaging because the accuracy of estimated parameter has a strong influence on the imaging quality. Generally, the backscattered signal of a moving target is sampled in fast time dimension, which can be approximated as the combination of multiple Chirp signals with a proper Chirp rate. Compared with the Fourier transform, the fractional Fourier transform (FrFT) performs better compression property due to its unique energy focus ability to Chirp signals. An improved Cross-correlation method based on FrFT for parameter estimation is presented in this paper. It employs the correlation between range profiles compressed by FrFT to enhance the quality of parameter estimation for ISAR applications. The method takes good balance between accuracy and complexity, and is robust to noise. Simulation results show that the proposed method outperforms the conventional Cross-correlation Method in terms of ISAR translational MOCOMP.

Inverse synthetic aperture radar (ISAR) images of a windmill are presented. In-situ measurement data of an 18- blade windmill are collected using an ultra-wideband radar. The measured signatures are compared to simulation results based on physical optics. The backscattering phenomenology is examined in the sinogram, spectrogram, and ISAR image domains. In addition, a composite image is generated from multiple ISAR snapshots to reveal the entire windmill structure.

In inverse synthetic aperture radar (ISAR) imaging, translation compensation should be done before range-Doppler imaging process, and range alignment is the first step for translation compensation. In order to remove the limitation of integer range bin and align the echoes precisely for ISAR range alignment, combining with the advantage of array signal processing at fractional unit delay compensation, we propose a novel range alignment method based on linear transmitting/receiving (T/R) array. Firstly the ISAR imaging system is modeled as a linear T/R array. Then based on the snapshot imaging model of linear T/R array, range alignment is accomplished by wave path difference compensation which is transformed into the phase difference compensation in frequency domain between adjacent array elements. The phase difference compensation consists of integer range bin alignment and decimal time delay compensation which is implemented by the phase rotation's estimation and compensation in frequency domain. Finally, the results of simulation data and real radar data are provided to demonstrate the effectiveness of the proposed method.

In order to obtain focused inverse synthetic aperture radar (ISAR) images, an accurate translational motion compensation is required. The phase adjustment step corresponds to fine compensation and must be properly designed. The authors introduce the Renyi entropy for autofocusing ISAR images. The Renyi entropy of order α is a generalisation of the standard Shannon entropy. When α tends to be the unity, the Renyi entropy tends to be the Shannon entropy. Here, we demonstrate that minimising the Renyi entropy for α = 2 is equivalent to maximising the contrast for ISAR autofocusing. Furthermore, it is also shown that maximising the peak value is equivalent to minimising the Renyi entropy for α tending to infinity. On the other hand, the authors propose to minimise the Renyi entropy with α = 0.5 to reconstruct an accurate ISAR image. Simulated data have been used to verify that, in terms of mean squared error, the proposed method with α =0.5 outperforms other autofocusing algorithms such as the method based on contrast maximisation or the one based on the minimisation of the standard Shannon entropy. The method has also been applied to real data.

In this paper, performance efficiency analysis of compressive inverse synthetic aperture radar (ISAR) imaging for a complex, electrically large target is presented. In this context, physical optics (PO) is used as a high-frequency scattered field prediction technique and compressive sensing (CS) theory is applied to ISAR imaging by exploiting the sparse point scatterer structure of the target. Results prove that CS provides high resolution and accuracy in ISAR imaging even under the Nyquist sampling rate and linearly decreases computational time.

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.

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.