Based on the authors' 20 years' research work on Inverse Synthetic Aperture Radar (ISAR) imaging of moving targets and non-cooperative target recognition, this book provides readers with knowledge of various algorithms of ISAR imaging of targets and implementation with MATLAB. It introduces basic principles of radar backscattering, radar imaging, and signal analysis. It describes the characteristics of radar returns from targets, how to produce well-focused ISAR images of moving targets, and what features that can extracted from ISAR images. Also introduced are several important algorithms for ISAR image formation, ISAR image auto-focusing, and applications of ISAR imaging to air targets, sea vessels and ground moving targets. Examples of ISAR imaging of ground moving targets, air targets, and sea vessels are discussed in detail.
Other keywords: refocusing moving target; ISAR target feature extraction; signal processing; FMCW ISAR; polarimetric ISAR; bistatic ISAR; ISAR autofocus algorithm; ISAR motion compensation; ISAR imaging
The aim of this introductory chapter is to cover some fundamental concepts on ISAR and to highlight some characteristics that make these systems unique, especially related to SAR. Although SAR and ISAR share the same underlining concept of forming a synthetic aperture, they are substantially different in the way they process the radar received signal and generate a focused image of a target. Moreover, some basic concepts such as Doppler effect and image resolution have been introduced along with that of motion-induced radar imaging. Detailed signal modeling and image formation algorithms have been left out since they deserve focused attention, and they will be treated in the next few chapters.
Inverse synthetic aperture radar (ISAR) is different from synthetic aperture radar (SAR) because the former has a larger antenna aperture based on target motion while the radar remains stationary and generates a high-resolution image of moving targets. In this chapter, we will introduce principles related to how ISAR is capable of generating such a high-resolution image of a moving target: ISAR scattering model, ISAR signal waveforms, ISAR image projection plane, point spread function, ISAR image processing, and bistatic ISAR.
In this chapter, we will introduce the most commonly used ISAR range-Doppler image formation. However, if the Doppler spectrum generated by a rotating target has severe time variation, ISAR range-Doppler image will become smeared in the Doppler domain. In these cases, in addition to conventional rotational motion compensation (RMC) methods such as the polar formatting algorithm (PFA), the time-frequency-based image formation can be used. We will introduce the time-frequency image formation algorithm. For better display of two-dimensional (2-D) ISAR imagery, we suggest a useful windowing and zero-padding method for suppressing sidelobes.
As introduced in Chapter 3, inverse synthetic aperture radar (ISAR) data are arranged in a 2-D matrix, where the number of range cells is in its row and the number of pulses is in its column, or vice versa. To reconstruct an ISAR range-Doppler image, we first take range compression to obtain ISAR range profiles, which is a sequence of consecutive range profiles over the coherent processing interval (CPI). Range compression is usually performed through a matched filter. From the range profiles, we can see motion of targets; that is, targets appear at different positions in different range profiles. Then, motion compensation will be carried out, including translational motion compensation (TMC) and rotational motion compensation (RMC). Finally, after removing translational motion and rotational motion, by taking the Fourier transform along the number of pulses (slow-time domain), an ISAR range-Doppler image can be formed. In this chapter, we will introduce ISAR motion compensation methods, including the cross-correlation, range centroid, and minimum-entropy methods for range alignment and the minimum variance, Doppler centroid, phase gradient, and entropy methods for phase adjustment. Many motion compensation algorithms were developed for solving the image-smearing problem [1-12]. TMC includes range alignment and phase adjustment or phase correction. Range alignment is accomplished by tracking the movement of a prominent scatterer with strong peak in range profiles. This is called the coarse range alignment, which allows the prominent scatterer to be sorted into the same range cell across the range profiles. The accuracy of the alignment is limited by the range resolution cell. However, only the coarse range alignment is not sufficient for removing phase drift errors in the range profiles. Consequently, a suitable phase adjustment procedure must be carried out to remove the residual phase errors and drifts.
Autofocus allows us to generate images with better quality by automatically adjusting the image focusing parameters. For radar imaging, autofocus means to automatically correct phase errors based on collected radar returns from targets. Inverse synthetic aperture radar (ISAR) motion compensation includes range alignment and phase adjustment. The phase adjustment process is for removing the residual translation error on phase terms. The phase errors are the causes of image defocusing. If a phase adjustment algorithm is based solely on the radar data itself, this is called the autofocus algorithm.ISAR autofocus methods can be parametric and nonparametric. Parametric method uses a parametric model of the radar-received signal. Image contrast-based autofocus (ICBA) and entropy-based autofocus algorithm belong to the parametric method . The prominent point processing (PPP) algorithm and the phase gradient autofocus (PGA) algorithm belong to the nonparametric ISAR autofocus method .
In the previous chapters, we have dealt with the geometrical aspects of inverse synthetic aperture radar (ISAR) imaging, which have led to a theoretical approach of the problem of forming electromagnetic (EM) images of noncooperative targets using high-resolution radars. Nevertheless, real-world data are corrupted by noise, and clutter and targets usually undergo complex motion, which cannot easily be modeled or predicted. Moreover, other effects such as limited resolution or high sidelobe levels (SLLs) may strongly reduce the effectiveness of ISAR imaging in classification and recognition. In this chapter, we will introduce such problems and provide both classic and recent solutions to them.
ISAR imagery is quite different from optical images. Thus, feature extraction from 2-D radar imagery is more complicated. In this chapter, we introduced two distinct instances of feature extraction in ISAR: 2-D feature extraction from a normal ISAR range-Doppler (or cross-range) image; and target motion feature extraction from micro-Doppler signatures. For 2-D feature extraction, image feature extraction in computer vision (e.g., the model-based method, morphology method, template matching, and machine learning) may be used. In Section 7.1, we introduced how to estimate the size and orientation of a target from its range and cross-range extents and how to estimate the target rotation parameters based on the estimated target dimension. In Section 7.2, we introduced how to extract target motion features showing in ISAR images, which are extracted from ISAR range profiles instead of 2-D ISAR imagery. Time-varying micro-Doppler signatures provide information that allows the estimation of various kinematic features. We demonstrated these with helicopter rotor blades and simulated walking human data.
In this chapter we will introduce the problem of refocusing synthetic aperture radar (SAR) images of moving targets by treating the problem as an ISAR one. SAR processors are designed to form radar images at very high resolution. Nevertheless, they are based on the assumption that the illuminated area is static during the synthetic aperture formation [1]. As a consequence of such an assumption, the existing SAR image formation algorithms are unable to focus moving targets and leading to blurred and displaced images of an object that is not static during the synthetic aperture formation. In the existing SAR literature, many attempts have been made to compensate for the target's motion and therefore to form focused images even in the presence of moving targets [2,3]. Nevertheless, such attempts are based on some assumptions on the target's motion that limits the effectiveness of such algorithms to some extent. On the other hand, as detailed in the first chapters of this book, inverse synthetic aperture radar (ISAR) techniques do not base their functioning on the assumption that the target is static during the synthetic aperture formation. Instead, they exploit, at least partly, the target's own motions to form the synthetic aperture. Although ISAR techniques do not make use of a priori information about the target's motion, some other constraints apply to the ISAR image formation. Such constraints may include the image size, the achievable crossrange resolution, and the fact that the imaging system performance is not entirely predictable. Nevertheless, despite such constraints, ISAR imaging provides a more robust and flexible solution to cases of targets undergoing complex motions, such as pitching, rolling, and yawing ships. A functional block scheme is represented in Figure 8.1 that aims at describing a detection and moving target refocusing system [4]. As shown in the system depicted in Figure 8.1, targets are detected directly in the SAR image domain. A sub-image cut around the target is then selected and used to form a refocused image of the target using ISAR processing.
FMCW radar systems have a simple architecture and thus are lightweight, low-power, and low-cost. Range information from an FMCW radar is obtained by the measurement of its beat frequency between the transmitted and the received signals, which can be simply performed using the fast Fourier transform (FFT). In principle, FMCW radar is capable of measuring targets at range extremely close to the radar transmit and receive antennas. Also, the range resolution of the FMCW radar is determined by the frequency bandwidth of the FMCW signal. Thus, using wideband FMCW signal, the range resolution can be very high. Another advantage of the FMCW system is its rectangular shaped power spectrum, which is desirable in low probability of intercept (LPI) radars. FMCW radar was first used in radio altimeters and has been successfully used in shortrange applications, such as automotive radars. However, the major disadvantage of FMCW radar is the coupling between the transmitter and the receiver, which limits the dynamic range of the FMCW radar for the use as a general-purpose synthetic aperture imaging system. After improving the dynamic range, the capability of the FMCW radar for synthetic aperture radar (SAR) imaging has been demonstrated. In this chapter, we will focus on the use of FMCW radar to form inverse synthetic aperture radar (ISAR) images of noncooperative targets.
Bistatic inverse synthetic aperture radar (B-ISAR) imaging can theoretically be enabled in all bistatic radar configurations [1]. In this chapter, we will analyze the effects of bistatic geometry on the ISAR image formation. We will also study and understand B-ISAR imaging and how to implement it to overcome some limitations of monostatic ISAR, such as geometrical limitations, imaging of stealthy targets and to enable applications such as exploitation of bistatic synthetic aperture radar (SAR) systems, multistatic ISAR imaging, and passive ISAR imaging.
Polarimetric synthetic aperture radar (Pol-SAR) has been widely used for classifying natural and man-made targets.In this chapter,we will (1) outline a framework for polarimetric inverse synthetic aperture radar (Pol-ISAR) imaging by defining the geometry and by introducing suitable signal models, (2) introduce image formation algorithms that use the fully polarimetric information contained in the received signal, and (3) discuss how to interpret and represent fully polarimetric ISAR images.
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.
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