Inverse Synthetic Aperture Radar Signal Research Articles

FMCW ISAR Autofocus Imaging Algorithm for High-Speed Maneuvering Targets Based on Image Contrast-Based Autofocus and Phase Retrieval

From the perspective of radar signal processing, the pulse-Doppler radar signal model is mostly based on the stop-and-go model due to the short pulse propagation time. Unlike the pulse-Doppler radar, the frequency modulated continuous wave (FMCW) radar takes a long time in the process of signal transmission to echo reception. During this period, the target cannot be considered to be stationary during the acquisition of the entire sweep echo, so the stop-and-go assumption in the FMCW system is no longer valid, which means that the target's motion related to the fast time must be taken into account. In the inverse synthetic aperture radar (ISAR) imaging scenario of high-speed maneuvering targets, the frequency offset correction introduced by the target's motion becomes reasonably complicated that it is difficult to restore the usual ISAR signal processing. In this work, a new method based on phase retrieval and image contrast-based autofocus (ICBA) is proposed to address the complex problem of motion compensation for FMCW ISAR systems. To cope with the fast time-varying characteristics of the phase error and the higher number of phase terms relative to pulsed radars, the proposed algorithm performs fine compensation with higher accuracy, resulting in high-resolution and autofocus imaging. Experimental results demonstrate the effectiveness of the proposed method.

Iterative Optimization-Based ISAR Imaging With Sparse Aperture and Its Application in Interferometric ISAR Imaging

Recently, high-resolution inverse synthetic aperture radar (ISAR) imaging with sparse aperture (SA) data has attracted increasing attention. The theory of compressive sensing (CS) suggests that an unknown sparse signal can be accurately recovered by taking advantage of very limited samples. For ISAR images, the number of the resolution cells occupied by the strong scattering points of the target is usually much smaller than that of the resolution cells of the image plane, revealing the strong sparsity trait of the ISAR signal. This trait of ISAR signal creates the conditions for incorporating CS into high-resolution ISAR imaging. In this paper, a novel iterative optimization-based SA-ISAR imaging approach is proposed. First, the SA-ISAR signal model is established and the envelope alignment is executed on the one-dimensional range profiles of the SA data. Next, the gradient-based algorithm is exploited to recover the complete signals. Then, by iteratively performing the procedures of the envelope alignment and the signal recovery, the accuracy of signal recovery can be significantly improved and a high-quality ISAR image can be obtained. Ultimately, the extension of the iterative optimization-based SA-ISAR imaging to the three-dimensional (3-D) interferometric ISAR (InISAR) imaging is successfully implemented via the traditional ISAR imagery pair interferometric method. The experiments based on the measured and simulated data are carried out to validate the superiority of the novel algorithm.

High‐resolution distributed ISAR imaging by OMP method

The distributed inverse synthetic aperture radar (ISAR) technique can form multiple virtual sensors and has the potential to increase the cross-range resolution by exploiting the multi-channel echoes. In the existing imaging approaches, the echoes acquired from different virtual sensors are rearranged into an equivalent single-channel ISAR signal firstly. Then, the missing data between the observation angles of any two adjacent sensors is restored by interpolation. Thus, the echo with long observation time can be acquired. After the fast Fourier transform on this echo, the improved cross-range resolution image can be acquired. However, when the gaps are large, the recovered missing data is very inaccurate. Secondly, if the target rotates non-uniformly and the change of the observation angle is not a linear function with the slow time, the equivalent single-channel ISAR echo cannot be obtained by interpolation. Therefore, this study aims to avoid these drawbacks and get desired distributed ISAR imaging results. After sparse representation of the distributed ISAR echoes, the orthogonal matching pursuit algorithm is used to position the scatterers in every range bin and the final high-resolution ISAR image can be obtained. Simulations show the effectiveness of the proposed method to deal with aforementioned problems.

Two-Dimensional Blanket Jamming Against ISAR Using Nonperiodic ISRJ

Interrupted sampling repeater jamming (ISRJ) against inverse synthetic aperture radar (ISAR) attracts great attention in the past decades. Conventional ISRJ mainly produces multiple false targets by periodically sampling and retransmitting the intercepted radar signal at sub-Nyquist rates. However, the rapid development of the anti-ISRJ technique and the difficulty in guaranteeing the real-time reaction make it urgent to propose novel ISRJ-based jamming methods. In this paper, a blanket jamming method against ISAR is proposed by utilizing the unique property of the nonperiodic ISRJ. By nonperiodic interrupted sampling modulation on the intercepted ISAR signal in both fast- and slow-time domains, the target image will be covered by the square-shaped jamming strips spreading along both the range and azimuth direction. Then, the target can get shielded by the two-dimensional blanket jamming. Besides, the proposed method is more effective compared with the conventional blanket jamming method, such as the noise jamming due to the signal coherence. Simulations via the Yak-42 aircraft data demonstrate the validity of the proposed method.

ISAR Imaging of a Rotating Asteroid Irradiated by Pulsar’s Electromagnetic Emission

Abstract The aim of the present study is imaging of moving objects, asteroids illuminated by continuous coherent wideband pulsar’s signals. As pulsars are located on more than thousands of light years from Earth, objects crossing pulsars’ emission beams are considered as second sources of electromagnetic waves, carrying object’s shape and velocity information that can be extracted by application pulsar emission-based inverse aperture synthesis. Inverse Synthetic Aperture Radar (ISAR) scenario, geometry and kinematics are analytically described. Models of pulsar signals and ISAR signals secondary emitted by asteroids are developed. White Gaussian noise of high level is added to the deterministic reemitted signal in order to approach the real signal scenario. Two-Dimensional (2D) Fourier transform for image extraction is applied. Special iterative noise removing procedure is suggested for asteroid’s image enhancement. To verify mathematical model and imaging algorithms numerical experiments are carried out.

A Deception Jamming Method Countering Bi- and Multistatic ISAR Based on Micro-Doppler Effect

Bi- and multistatic inverse synthetic aperture radar (ISAR) operate with spatially separated transmitting and receiving antennas. A deception jamming method countering bi- and multistatic ISAR is proposed in this paper based on the study of micro-Doppler effect. The jammer modulates the intercepted ISAR signals with added micro-Doppler information and retransmits them to the real target, which scatters the jamming signals to the radar receivers. Deceptive false-target images with interference bands in the cross-range direction will be induced by the jamming signals through the imaging process of radar receivers. Additionally, real-time movement features of the false-targets can be flexibly adjusted by changing the modulation parameters, which improves the fidelity of the false-targets. The equivalent number of looks (ENL) index is used to evaluate the jamming effects. Simulation results validate our theoretical analysis and show the effectiveness and practicability of our method.

Continuity pattern-based sparse Bayesian learning for inverse synthetic aperture radar imaging

This paper considers the problem of block-sparse recovery for two-dimensional inverse synthetic aperture radar (ISAR) imaging. According to the scatterer distribution of the target scene in ISAR image, the continuity pattern in both range and cross-range domains should be considered. Therefore, the sparsity of each grid cell is controlled by four neighboring hyperparameters and the relevance between neighboring coefficients is determined by coupling parameters, which are data-dependent, so the estimation is done adaptively by an expectation–maximization algorithm. To model the pattern dependencies among neighboring scatterers on range-Doppler domain, we develop the data-dependent coupling parameters method to capture continuity pattern of ISAR signals. Simulation results show that the proposed method can achieve improvement in terms of entropy, image entropy, and image contrast. Moreover, our algorithm effectively improves reconstruction of target scene in noiseless and noisy case compared with other methods.

ISAR imaging and cross‐range scaling of high‐speed manoeuvring target with complex motion via compressive sensing

For targets with extreme manoeuvres, inverse synthetic aperture radar (ISAR) imaging suffers from translational motion (TM), which is modelled as a one-dimensional (1D) phase error, and non-uniform rotational motion (RM), which is a multidimensional (MD) phase error that causes severe blurring in ISAR images. Full-aperture data collection is often unachievable because of interference with other radar activities, resulting in sparse-aperture (SA) data. In this study, the authors present a new framework for SA-ISAR imaging and cross-range scaling for manoeuvring targets based on compressive sensing. Instead of solving conventional optimisation problems constrained by a sparsity of signals, the proposed method utilises the sensing-matrix estimation technique for ISAR image reconstruction using parametric signal-model reconstruction. To do this, it looks for basis functions that best represent the behaviour of a sensing-dictionary matrix comprising the observed SA data. The sensing-matrix reconstruction is based on a modified orthogonal matching pursuit-type basis function-searching scheme. Finally, they generate a well-focused and scaled ISAR image from the recovered complete ISAR signal using the conventional Fourier transform after the removal of signals corresponding to 1D TM and MD RM phase errors. They utilise both simulated and real measured datasets to confirm the effectiveness of proposed method.

A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN) to focus the distorted inverse synthetic aperture radar (ISAR) image. In this paper, a coefficient estimator based on the artificial neural network (ANN) is firstly developed to solve the time-consuming rotational motion compensation (RMC) polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs). Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

ISAR geometry, signal model, and image processing algorithms

Inverse synthetic aperture radar (ISAR) technique provides high-resolution images of observed moving objects. However, shadow-occluded effects and noise, which results in lack of fractions of the object geometry, often corrupt ISAR images. To facilitate the image analysis and to solve the target identification problem in most practical applications additional image-processing procedures are strongly required. The aim of the present research is based on ISAR geometry and signal modelling to develop procedures improving quality of images and extract the silhouette contour line of the ISAR image, the main geometric parameter in the target recognition process. The problem is solved in two main stages. First, ISAR geometric parameters are described and expressions for linear frequency modulated signal modelling and imaging are derived. Second, image-processing algorithms for ISAR image improving and contour line extraction are developed. All stages of image-processing and reconstructing algorithms are illustrated by results of numerical experiments.

Fourier‐based ISAR imaging using 2D polynomials

Inverse synthetic aperture radar (ISAR) image formation is a problem that has been studied for many decades. Despite the effort made by many researchers and practitioners, it is still an open problem as it involves non-trivial aspects both in terms of geometry and scattering mechanisms. In this study, the authors introduce a new interpretation of classical ISAR signal modelling, based on two-dimensional polynomials, providing an alternative perspective that permits the in-depth investigation of the problem of ISAR image formation when using a Fourier transform-based approach. The interpretation of target motion compensation and image autofocus are connected through a mathematical formulation that explains in depth what can and should be compensated in the received signal prior to applying the Fourier transform and therefore forming the ISAR image.

Performance Analysis on ISAR Imaging of Space Targets

Usually, in traditional Inverse Synthetic Aperture Radar (ISAR) systems design and mode selection for space satellite targets, coherent integration gain in azimuth direction hardly can be analyzed, which depends on target’s motion. In this study, we combine the target orbit parameters to determine its motion relative to radar and deduce coherent integration equation in ISAR imaging to realize the selection of imaging intervals based on coherent integration, which can ensure the resolution in azimuth direction. Meanwhile, we analyze the influence of target orbit altitude to echo power and imaging Signal-to-Noise Ratio (SNR) that provides a new indicator for space observation ISAR systems design. The result of simulation experiment illustrates that with target orbit altitude increasing, coherent integration gain in azimuth direction of large-angular observation offsets the decreasing of imaging SNR in a degree, which provides a brand-new perspective for space observation ISAR systems and signal processing design.

An Improved Digital False-Target Image Synthesizer Method for Countering Inverse Synthetic Aperture Radar

Digital false-target image synthesizer (DIS) has been proposed as a deceptive jamming method for countering inverse synthetic aperture radar (ISAR) with an RF imaging false-target capability. However, to efficiently generate jamming signal in real time, a specific-designed generator is unavoidable for the implementation of this algorithm, where parallel and pipeline processing are both included because jamming signal is generated according to each range bin. This paper proposes an improved two-stage DIS method (T-DIS). The implementation of the T-DIS can be effectively simplified by separating the modulation process of a repeat jammer into two stages: the offline stage and the real-time stage. The modulation signal can be easily extracted from the modulation-matrix buffered in the offline stage. Only an element-by-element matrix multiplication between the modulation signal and the intercepted ISAR signal is needed in the real-time stage. In contrast to the conventional DIS method, the proposed algorithm exhibits simplicity and flexibility significantly, which reduces the computation time and hardware constraints. Experimental results of scattering model of the Yak-42 plane and real data illuminate the validity of the proposed method.

Measurement and Correction of the Ionospheric TEC in P-Band ISAR Imaging

It is commonly known that the ionosphere has significant effects on a low-frequency (particularly P-band) radar signal. It causes the degradation of the image quality in synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging systems. In this letter, we analyze the ionospheric effects on radar signals and find that the total electron content (TEC) is a key to the ionospheric effects. A method is proposed to evaluate the TEC from a received ISAR signal and to correct the ionospheric effects. Some real experimental results, using a ground-based P-band ISAR system to observe a space target in the ionosphere, are used to validate the proposed method.

Two-dimensional superresolution multiple-input multiple-output and inverse synthetic aperture radar imaging based on spatial frequency ambiguity resolving

Combining multiple-input multiple-output (MIMO) radar and inverse synthetic aperture radar (ISAR) techniques can reduce the number of antennas used and shorten the radar integrated time compared with the single-channel ISAR for the same cross-range resolution. In existing MIMO-ISAR processing, the echoes of different sensors are rearranged into an equivalent single-channel ISAR signal. A new method without echo rearrangement is proposed for two-dimensional (2-D) MIMO-ISAR imaging. A 2-D frequency estimation algorithm based on Unitary ESPRIT and projection transformation is used to obtain the spatial and Doppler frequencies of scatterers, and a high cross-range resolution can be achieved. The relationship between the two frequencies is exploited to resolve the ambiguity of spatial frequency. The analysis and simulation results show that, compared with the existing method, the proposed method can decrease the relative rotation angle (or integrated time) required for imaging. Thus, this method is more suitable for imaging targets with limited rotation or high maneuvering.

Improved compressed sensing for high-resolution ISAR image reconstruction

For inverse synthetic aperture radar (ISAR), an ISAR signal in the cross-range direction has the characteristic of sparsity in the azimuth frequency domain. Due to this property, a Fourier basis is adopted as a kind of sparse basis, and high cross-range resolution imaging is achieved by using the compressed sensing (CS) method. However, the Fourier expanding for signal with finite length will result in energy leaking and spectrum widening. As a result, the Fourier basis cannot obtain the optimum sparse representation for signals of unknown frequencies in most cases. In this paper, we present an improved Fourier basis for sparse representation of the ISAR signal, which is constructed by frequency shift and weighting of the Fourier basis and available to obtain the robust recovery performance via CS. Simulation results show that the improved CS method outperforms conventional CS method that uses the Fourier basis.

Comparison of parametric sparse recovery methods for ISAR image formation

A parametric sparse representation model of the inverse synthetic aperture radar (ISAR) signal has been proposed recently, and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate. Based on the parametric sparse representation model, several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image. In this paper, four parametric sparse recovery algorithms are compared mainly in three aspects: the accuracy of the rotation rate estimation, the ISAR image quality and the computational load. Numerical examples are presented to show the advantages and disadvantages for each method.

Adaptive Sparse Recovery by Parametric Weighted L$_{1}$ Minimization for ISAR Imaging of Uniformly Rotating Targets

It has been shown in the literature that, the inverse synthetic aperture radar (ISAR) echo can be seen as sparse and the ISAR imaging can be implemented by sparse recovery approaches. In this paper, we propose a new parametric weighted L1 minimization algorithm for ISAR imaging based on the parametric sparse representation of ISAR signals. Since the basis matrix used for sparse representation of ISAR signals is determined by the unknown rotation parameter of a moving target, we have to estimate both the ISAR image and basis matrix jointly. The proposed algorithm can adaptively refine the basis matrix to achieve the best sparse representation for the ISAR signals. Finally the high-resolution ISAR image is obtained by solving a weighted L1 minimization problem. Both numerical and real experiments are implemented to show the effectiveness of the proposed algorithm.

ROTATIONAL MICRO-MOTION MODULATED JAMMING FOR COUNTERING ISAR BASED ON INTERMITTENT SAMPLING REPEATER

The intermittent sampling repeater jamming (ISRJ) can only produce fake point scatterers in the down-range, while the time- varying frequency induced by rotational micro-motion dynamics blurs the image in the cross-range. This paper focuses on the combination of the above two methods to counter the inverse synthetic aperture radar (ISAR). Based on the signal models of coherent ISAR signal processing, principles of the jamming are derived. The key jamming parameters and the selection criteria are determined by two steps. Finally, the validity of the proposed algorithm is demonstrated using the numeric simulations and simulations based on the measured data.

Scaling the 3-D Image of Spinning Space Debris via Bistatic Inverse Synthetic Aperture Radar

In 3-D inverse synthetic aperture radar (ISAR) imaging of spinning space debris, the image obtained via the available algorithm is modified by a scaling factor. Determined by the angle between the spinning vector and the radar line of sight, this factor cannot be estimated by a monostatic radar in a short imaging interval. This letter derives the bistatic ISAR (Bi-ISAR) geometry and signal model for 3-D imaging of space debris. Then, a 3-D image scaling algorithm is introduced based on the connections between the mono- and bistatic echoes of the same scatterer. Numeric simulations have proved the validity of the proposed algorithm.