Noise-Robust ISAR Imaging for GEO Targets Based on GAPSO-GRFT

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.

Translational motion compensation for maneuvering targets is a challenging step in inverse synthetic aperture radar (ISAR) imaging. On one hand, nonuniform translational motion and rotational motion of maneuvering targets result in nonlinear bending of the range profile and create high-order slow time phases. On the other hand, radar cross section (RCS) fluctuation of targets may cause a sparse aperture and, therefore, sharply weakens the correlation between the pulses. Under this condition, traditional methods of translational motion compensation are sometimes difficult to get ideal results. Therefore, we propose an effective and novel method to achieve translational motion compensation for maneuvering targets with sparse aperture. In this method, the translational and rotational motions are modeled as cubic polynomial and quadratic polynomial, respectively. Based on this model, we use the echo to construct a 1-D optimization to estimate all translational parameters simultaneously and use the root-mean-square prop-momentum gradient descent (RMSprop-MGD) algorithm to solve the optimization to maintain the accuracy of parameter estimation. Finally, we use those translational parameters to compensate for the translational motion. Since this method converts the multiparameter optimization into a 1-D optimization, we named it dimension compressed optimization (DCO). Experimental results of the measured dataset prove that the proposed method is effective for full-aperture echoes, random sparse echoes, and block sparse echoes. Moreover, this method is robust under a low pulse sampling rate and low signal-to-noise ratio (SNR) environment.

Accurate translational motion compensation is crucial for inverse synthetic aperture radar (ISAR) imaging. At low signal-to-noise ratio (SNR), the performance of nonparametric methods is declining because the correlation of adjacent range profiles is severely destroyed by the noise. Fortunately, parametric methods can still work well at low SNR. However, existing parametric methods are time-consuming, which decreases ISAR imaging efficiency significantly. So, a fast parametric translational motion compensation method is studied for low SNR scenes in the paper, which is based on modified Adam (Madam) algorithm. By changing the updating strategy for weights and learning rate in Adam, Madam algorithm can significantly accelerate the convergence when estimating the motion parameters. To further improve the efficiency, a new objective function by combining image contrast and image entropy is proposed. When applied into measured Yak-42 data at low SNR, the proposed method can image about 10 times faster than particle swarm optimization (PSO) method and 2 times faster than Adam method. Therefore, the proposed method is an efficient translational motion compensation method for low SNR ISAR imaging.

Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensation and estimation of the turning velocity of the target. State-of-the-art algorithms for motion compensation trade-off between computational complexity and accuracy. An alternative low complexity method is to use an additional sensor for tracking the target motion. In this work, we propose to exploit computer vision algorithms to identify the radar target object in the sensor field-of-view (FoV) with high accuracy. Further, we propose to track the target vehicle's motion through fusion of vision and radar data. Vision data facilitates the accurate estimation of the lateral position of the target and its lateral velocity which complements the radar's capability of accurate estimation of range and radial velocity. Through simulations and experimental evaluations with a monocular camera and Texas Instrument's millimeter wave radar, we demonstrate the effectiveness of sensor fusion for accurate target tracking for translational motion compensation and generation of high quality ISAR images.

With adoption of the hopped-frequency pulse waveform to inverse synthetic aperture radar (ISAR), a novel method for motion compensation in ISAR imaging, named the minimum waveform entropy method of parameter estimation, is proposed. A measure function, called the waveform entropy, is employed to measure the effects of target motion on both Doppler amplitude spectra and slant range profiles. The method can achieve optimal estimation of motion parameters of a target based on the minimum waveform entropy rule. In addition, the method converts a two-dimensional search for the velocity and acceleration of a target into two one-dimensional searches for the velocity and acceleration respectively, which greatly decreases computational cost, and conveniently realizes real-time processing. Simulation results indicate that the method can accomplish accurate estimation of motion parameters of a target with a good anti-noise performance, thus making it possible to image a target in clutter background.

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.

Sub-Nyquist sampling is a common means of interfering with inverse synthetic aperture radar (ISAR) imaging, which has been validated for the feasibility of 1-D compressive sensing (CS) ISAR imaging. In order to overcome the high computational complexity of 1-D CS-based ISAR imaging, the coupled 2-D CS algorithms are utilized to achieve ISAR imaging directly which has been proposed recently. In this paper, sub-Nyquist sampling jamming signals are utilized to induce multiple false-target ISAR images by using the coupled 2-D-CS algorithm. The jamming signals, which are formed by the intercepted radar signals under the sub-Nyquist sampling theorem and scattered by moving targets, are collected by ISAR to establish a 2-D super resolution model, and the finally reconstructed ISAR images are induced and show that the deceptive false-target images retaining the real target information. Simulated trails and real data of a Yak-42 plane are used to validate the correctness of the analyses and the finally well-focused false-target images greatly support the effectiveness of the sub-Nyquist sampling idea in the countermeasures of ISAR with coupled 2-D CS.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as the high maneuvering airplanes and fluctuating ships with oceanic waves, the azimuth echo signals can be modeled with cubic chirps after translational motion compensation, and then, the azimuth focusing quality will be deteriorated by the time-varying chirp rate. In this paper, a parameter estimation method of cubic chirps is proposed based on the discrete chirp Fourier transform (DCFT), which is generated from DCFT for quadratic chirps. Several properties of DCFT for cubic chirps are derived, and we show that the modified DCFT (MDCFT) is more appropriate to deal with the practical applications (e.g., ISAR imaging) than the original DCFT. Therefore, we put forward the imaging algorithm based on MDCFT, and then, simulation results confirm the validity of the proposed algorithm.

With the development of multi-functional radar systems, inverse synthetic aperture radar (ISAR) imaging with sparse-aperture (SA) data has drawn considerable attention in the recent years. Motion compensation and imaging are among the most significant challenges that SA-ISAR imaging frequently faces. In this paper, we focus on the autofocus scheme, in which a modified eigenvector-based autofocus method is proposed. In the method, different weights are endued to different range cells according to their signal-to-noise ratios (SNRs). Using the weights, the contribution from the range cells with high SNR is enhanced, yielding accuracy improvement in phase error estimation. What is more is that to improve the estimation precision, an iterative scheme is introduced. Experimental results show that the proposal is not only robust to severe noise but also applicable to ISAR imaging with different SA patterns. Detailed comparisons are given in order to show the superiorities of the proposal in phase adjustment for ISAR data.

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.

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.

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.

Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the radar echo is intentionally or occasionally under-sampled, which defocuses the ISAR image by introducing considerable interference, and deteriorates the performance of signal decomposition for the removal of m-D effects. To address this issue, this paper proposes a novel m-D effects removed sparse aperture ISAR (SA-ISAR) imaging algorithm. Note that during a short interval of ISAR imaging, the range profiles of the main body of target from different pulses are similar, resulting in a low-rank matrix of range profile sequence of main body. For the range profiles of the micro-motion parts, they either spread in different range cells or glint in a single range cell, which results in a sparse matrix of range profile sequence. From this perspective, the low-rank and sparse properties are utilized to decompose the range profiles of the main body and micro-motion parts, respectively. Moreover, the sparsity of ISAR image is also utilized as a constraint to eliminate the interference caused by sparse aperture. Hence, SA-ISAR imaging with the removal of m-D effects is modeled as a triply constrained underdetermined optimization problem. The alternating direction method of multipliers (ADMM) and linearized ADMM (L-ADMM) are further utilized to solve the problem with high efficiency. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithm.

Motion compensation of high-speed moving target is a challenging task because of its time-varying orientation and rotation velocity. A novel approach for inverse synthetic aperture radar (ISAR) imaging is presented for both target translational motion and rotational motion nonuniformity compensation. The basic idea is to perform frequency tracking to every individual scatterer via a polynomial fitting technique. After maximizing the projection of one-dimension range alignment to polynomial function in range plane, the Doppler frequency drift of the strongest scatterer in the range cell is automatically tracked out and the multiple prominent point processing scheme is implemented to eliminate both the translational motion error and rotational motion nonuniformity. Further the orientation velocity can be estimated. Finally, a simulation example is provided to demonstrate the performance of the proposed approach.

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.