Interferometric Inverse Synthetic Aperture Radar Imaging Research Articles

Robust Interferometric ISAR Imaging With UAMP-Based Joint Sparse Signal Recovery

Sparse Bayesian learning (SBL) has found successful applications in interferometric inverse synthetic aperture radar (InISAR) imaging, especially in the presence of limited number of pulses or when using sparse apertures. SBL-based InISAR algorithms have been proven to be significantly superior to Fourier transform-based ones. However, the existing SBL-based algorithms are slow due to their high computational complexity. Moreover, there is also much room to improve in terms of imaging performance. In this work, leveraging the approximate message passing with unitary transformation (UAMP), we propose an InISAR imaging algorithm named UAMP-JSR (joint sparse recovery), which is much faster and delivers notably higher imaging accuracy than the existing SBL-based algorithms. Specifically, we develop a type-2 joint sparse model (JSM-2) for InISAR imaging and formulate it as a two-layer multiple measurement vectors (MMV) joint sparse problem. Based on a factor graph representation, the message passing techniques are used to efficiently solve this problem, which leads to the UAMP-JSR algorithm. Results based on extensive simulations and experiments based on the real data collected by the Pisa Radar (PIRAD) demonstrate the effectiveness and superiority of the proposed algorithm compared to existing algorithms.

Interferometric ISAR Imaging of Space Targets Using Pulse-Level Image Registration Method

Interferometric inverse synthetic aperture radar (InISAR) imaging is capable of retrieving three-dimensional target information from multi-channel inverse synthetic aperture radar (ISAR) images obtained by adjacent radars or antennas. The InISAR imaging of space targets is more difficult, compared with that observes short range targets with small radar systems. Considering the practical constraints when observing space targets with multiple large radars, this paper establishes a flexible InISAR imaging system which allows for non-perpendicular baselines and observation of targets under squint mode. Based on this, the generalized formulas for calculating scatterers' coordinates from measured interferometric phases are derived. Moreover, a pulse-level image registration method based on joint translational motion compensation is proposed to recover the coherence between multi-channel ISAR images, under condition of inconsistent pulse delays at different radar channels. With the proposed method, promising InISAR imaging results are obtained via two simulation experiments. The first experiment assumes ideal point scattering of the target, which verifies the high accuracy and computational efficiency of the proposed ISAR image registration method; the second experiment considers more practical target scattering using facet target model and physical optics algorithm, which demonstrates the possible deviation in InISAR imaging result caused by scatterer's anisotropic scattering and residual error of translational motion compensation. We believe that the derivation and simulation results in this paper would be helpful reference for practical InISAR imaging experiments.

Multilook Polarimetric 3-D Interferometric ISAR Imaging

This article introduces polarimetric 3-D interferometric inverse synthetic aperture radar (ISAR) imaging process using multiple phase centers via a spatio-sensor multilook algorithm. This approach enables one to take effective advantage of polarimetric scattering mechanisms in 3-D target representations, which may improve target classification and identification. In addition, the multilook algorithm enhances the accuracy of height estimation in noisy conditions. The 3-D interferometric ISAR (InISAR) imaging process is validated using the backhoe synthetic data released by the Air Force Research Laboratory.

Multi-static InISAR imaging for ships under sparse aperture

This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation. Firstly, a multi-static configuration is utilized to solve the coherent processing interval (CPI) problem caused by the slow-speed motion of ship targets. Then, we realize signal restoration and image reconstruction with the alternating direction method of multipliers (ADMM). Furthermore, we adopt the interferometric technique to produce the three-dimensional (3D) images of ship targets, namely interferometric inverse synthetic aperture radar (InISAR) imaging. Experiments based on the simulated data are utilized to verify the validity of the proposed method.

A novel approach for squint InISAR imaging with dual-antenna configuration

The image distortion arises in the Interferometric Inverse Synthetic Aperture Radar (InISAR) three-dimensional (3D) imagery when the radar works in squint-mode. Existing squint InISAR imaging methods suffer from high computational complexity and poor imaging quality due to the vertical Coordinates Transformation (CT) and iterative operations for rectifying the 3D image distortion. To address this issue, we propose an imaging method for squint InISAR with two antennas. Firstly, the cross-range scaling is used to obtain initial horizontal coordinates of the target. Then, the interferometric phase is used to obtain the altitude coordinates of the target, and hence a 3D image of the target is formed. Finally, the CT is applied to rectify the 3D image distortion by correcting the acquired horizontal and vertical and altitude coordinates of the target. The dual-antenna configuration reduces the complexity of the InISAR system. We perform extensive simulations to demonstrate the effectiveness of the proposed dual-antenna squint InISAR imaging method.

Noise-robust interferometric ISAR imaging of 3-D maneuvering motion targets with fine image registration

Low signal-to-noise ratio (SNR) and three-dimensional (3-D) maneuvering motion of targets present enormous challenges to the interferometric inverse synthetic aperture radar (InISAR) imaging. In this paper, we propose a noise-robust InISAR imaging algorithm to acquire high-precision InISAR images of 3-D maneuvering motion targets at low SNR. With respect to time-variant wave path difference (TVWPD) and spatial-variant wave path difference (SVWPD), a novel algorithm, named non-coherent fusion image registration algorithm (NCFIR), is developed to realize fine image registration. By implementing the non-coherent fusion processing on the multi-look ISAR images, it improves the image SNR and therefore makes possible the joint compensation of TVWPD and SVWPD at low SNR. In addition, underpinned by the Bayesian compressive sensing (BCS) theory, a joint multi-channel BCS ISAR imaging algorithm (JMC-BCS) is also proposed. Its capability for suppressing the strong noise and high side lobes of ISAR imaging results effectively improves the SNRs of imaging results. Through iterative processing of JMC-BCS and NCFIR, high-quality 3-D InISAR images of 3-D maneuvering motion targets at low SNR can be obtained. The proposed algorithm improves the SNR of image twice through JMC-BCS and NCFIR, rendering it more robust against noise. Extensive experimental results from both simulated and real data corroborate the effectiveness and robustness of the proposed algorithm which outperforms other available InISAR imaging approaches at low SNR.

Interferometric ISAR imaging under squint model using phase cancellation compensation method of combined processing data

This paper proposes a new interferometric inverse synthetic aperture radar (InISAR) image registration method under squint model based on phase cancellation compensation of combined processing data. Firstly, the residue translational motion component (RTMC) in range profile and the mismatching in the ISAR images induced by joint motion compensation with the same parameters to each antenna are analysed. Then, the RTMC compensation is applied to re-align the range profile sequences of the received antenna. The phase cancellation compensation is carried out to eliminate the mismatching in the range and azimuth direction, respectively. Simulation results demonstrate that the mismatching between the ISAR images can be compensated effectively, achieving high quality InISAR 3D images consequently.

Three-Dimensional Interferometric ISAR Imaging Algorithm Based on Cross Coherence Processing.

Interferometric inverse synthetic aperture radar (InISAR) has received significant attention in three-dimensional (3D) imaging due to its applications in target classification and recognition. The traditional two-dimensional (2D) ISAR image can be interpreted as a filtered projection of a 3D target’s reflectivity function onto an image plane. Such a plane usually depends on unknown radar-target geometry and dynamics, which results in difficulty interpreting an ISAR image. Using the L-shape InISAR imaging system, this paper proposes a novel 3D target reconstruction algorithm based on Dechirp processing and 2D interferometric ISAR imaging, which can jointly estimate the effective rotation vector and the height of scattering center. In order to consider only the areas of the target with meaningful interferometric phase and mitigate the effects of noise and sidelobes, a special cross-channel coherence-based detector (C3D) is introduced. Compared to the multichannel CLEAN technique, advantages of the C3D include the following: (1) the computational cost is lower without complex iteration and (2) the proposed method, which can avoid propagating errors, is more suitable for a target with multi-scattering points. Moreover, misregistration and its influence on target reconstruction are quantitatively discussed. Theoretical analysis and numerical simulations confirm the suitability of the algorithm for 3D imaging of multi-scattering point targets with high efficiency and demonstrate the reliability and effectiveness of the proposed method in the presence of noise.

Three‐dimensional InISAR imaging method for noncooperative targets based on respective LvD transformation and iterative Doppler focussing

Interferometric inverse synthetic aperture radar (InISAR) is an usually adopted approach for three-dimensional (3D) imaging of noncooperative target with a real or equivalent turntable motion. In practice, the Doppler frequency of non-cooperative target usually vary nonlinearly, especially when the rotational velocity of the target is fast or the imaging time is relatively long, the azimuth echoes of the target often contain quadratic phase terms. In this case, if we do not compensate the nonlinear phase terms contained in the echoes, the ISAR image will be defocused and then the 3D imaging quality will be degraded. Aiming at solving this problem, a new 3D InISAR imaging method based on respective Lv's distribution transformation and iterative Doppler focussing (RLvDT-IDF) is proposed in this paper. The major contributions of the proposed approach are as follows: (1) the echo of a range cell which can be treated as the superposition of a series of linear frequency modulation signals (LFMs) is processed by LvDT to specify the chirp rates (CRs) of these LFMs. The CRs are used to construct phase functions so as to remove the quadratic phase term contained in the echo by the IDF method; (2) Considering that the CRs within a range cell received by different channels are relatively different, so different ISAR images are focussed by respective LvDT-IDF (RLvDT-IDF), so all the well-focussed ISAR images can be obtained, which help to improve the accuracy of the image registration and scatterer extraction; (3) Considering that the phase information from the well-focussed ISAR images may have different errors by the RLvDT-IDF processing and the unfocused ISAR images contain the complete phase information, so they are used instead of the well-focussed ISAR images to estimate the interferometric phases of the extracted scatterers; (4) Based on the estimated CRs and 3D coordinates, the target's 3D rotation vector is retrieved by minimising the sum of squared residuals. Both simulations and practical experiment are conducted to validate the proposed method.

InISAR Imaging of Maneuvering Target Base on Motion Compensation and Image Coregistration

The imaging of maneuvering target is the main research content of Interferometric Inverse Synthetic Aperture Radar (InISAR). Since it is quite difficult to obtain the effective data of the moving target, both the residual translational compensation and least square method are proposed to realize the imaging of the target according to the principle of InISAR in this letter. First step, the echo model is established to perform motion compensation. Second step, perform image registration on the signal. Third step, the ISAR image is obtained via extracting the peak value, then perform the interference processing imaging. Finally, Simulation results show that this method can be used for image recognition.

InISAR Imaging for Maneuvering Target Based on the Quadratic Frequency Modulated Signal Model With Time-Varying Amplitude

Interferometric inverse synthetic aperture radar (InISAR) has proven to be an effective tool for 3-D imaging of noncooperative targets. The traditional InISAR imaging algorithms are almost entirely based on the assumption of a constant amplitude polynomial phase signal (PPS) model. However, target’s maneuverability tends to cause the echo signal to exhibit the characteristic of time-varying amplitude (TVA) in practice. To remedy this problem, a novel InISAR imaging algorithm for maneuvering targets based on quadratic frequency modulated (QFM) signal model with TVA is presented. First, the echo of each range cell is modeled as a multicomponent TVA-QFM signal. Then, through the matrix derivation, the parameter estimation problem of this signal is converted to a convex optimization problem. Subsequently, with the help of the scaled Fourier transform (SCFT), an efficient iterative update approach based on alternative direction method of multipliers (ADMM) framework is proposed to solve this optimization problem. Furthermore, associated with the range instantaneous Doppler (RID) method and multichannel interference technology, 2-D ISAR images and 3-D space shape of the target can be generated. Finally, some simulation results are provided to evaluate the effectiveness and robustness of the proposed algorithm.

Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

Long Orbit Arc InISAR Imaging of Space Targets With Monostatic Radar

By exploiting the spatial-variant property of the inverse synthetic aperture radar (ISAR) imaging plane of a space target, interferometric ISAR (InISAR) can provide three-dimensional (3-D) images of space targets using fewer antennas than traditional InISAR imaging systems. This article analyzes InISAR imaging of space targets with monostatic radar. First, a more realistic ISAR imaging model for space targets is established, which adopts the rotation of the earth and can provide a mathematical apprehension of the movement of space targets in InISAR imaging. Next, a sequential phase unwrapping method for InISAR imaging of space targets with monostatic radar is proposed. Then, the performance indicator of this one-antenna InISAR system is provided with analysis of the impact of the influencing factors in InISAR imaging under different scenarios. Finally, numerical simulations are performed to validate the theoretical analysis and effectiveness of the proposed method.

Images of 3-D Maneuvering Motion Targets for Interferometric ISAR With 2-D Joint Sparse Reconstruction

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

Image Registration for 3-D Interferometric-ISAR Imaging Through Joint-Channel Phase Difference Functions

To perform 3-D inverse synthetic aperture radar (ISAR) imaging through interferometric processing, all ISAR images should be correctly registered such that the same scatterer appears at the same position along both the range and Doppler directions. However, this condition generally does not hold in most situations because different radar view angles introduce additional angular motion errors that must be compensated. Hence, we herein propose a new ISAR registration method based on joint-channel phase difference (JC-PD) operations. From the result of the JC-PD function, JC-PD profiles are obtained, in which the locations and phases of the maximum peak contain constant and time-varying angular motions, respectively. Using our proposed method, correct ISAR registrations can be achieved in both the range and Doppler directions. In particular, compared to conventional Doppler registration methods, time-varying angular motions are more accurately compensated without the problem of cross-term phase interference. Through simulations and experiments, we verified that the proposed method yielded accurate ISAR registration results; hence, an excellent 3-D target reconstruction through interferometric ISAR imaging was demonstrated.

FMCW-InISAR imaging for high-speed target based on bistatic configuration

Interferometric inverse synthetic aperture radar (InISAR) imaging has been proved to be a powerful means for obtaining three-dimensional (3-D) space shape of noncooperative targets. Frequency modulated continuous wave (FMCW) InISAR (FMCW-InISAR) has unique advantages of low power, low cost, and small volume compared with traditional coherent pulsed InISAR. However, FMCW-InISAR imaging has two additional issues to consider, the one is the invalidation of the assumption of stopg the other is the isolation of the transmitting and receiving antennas, which is the inherent issue of the transmitter-receiver community radar systems. To solve these two problems, a bistatic FMCW-InISAR imaging algorithm for high-speed targets is proposed in this paper. For improving the isolation of the transmitting and receiving antennas, a bistatic configuration based FMCW-InISAR system is designed. According to the characteristics of bistatic, a bistatic equivalent motion model and corresponding signal model are established. Since the assumption of stop&go is invalid in the case of FMCW, indicating that the target cannot be viewed as motionless during a sweep repetition interval (SRI), a parametric estimation based quadratic phase factor (QPF) compensation method is investigated to eliminate the range walk caused by the radial motion of the target during the SRI. In addition, considering the far-field trait of the target and combining the traditional InISAR imaging process, a combined QPF compensation technique is proposed to reduce the computational burden of the algorithm. Finally, the effectiveness and the robustness of the proposed algorithm are evaluated by some simulations.

High‐quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks

AbstractIn this article, a modified complex‐valued convolutional neural network (MCV‐CNN) specifically for interferometric inverse synthetic aperture radar (InISAR) imaging is proposed. Comparing with the fast Fourier transformation‐based and sparsity‐driven imaging algorithms, the MCV‐CNN can achieve super‐resolution and side‐lobe suppression on the imaging results simultaneously within a short time. The inputs of the MCV‐CNN are complex‐valued radar echo data, and the outputs are complex‐valued ISAR images which contain both the amplitude and phase information. Then the phase information is adopted to perform an interferometric operation, and the high‐quality three‐dimensional InISAR imaging results can be achieved. A 0.22 THz InISAR imaging experiment has been carried out to show the superiority of the proposed method on imaging quality and computational efficiency.

Interferometric ISAR Imaging of Maneuvering Targets With Arbitrary Three-Antenna Configuration

In order to compensate for the shortcomings of inverse synthetic aperture radar (ISAR) imaging in target recognition, a three-dimensional (3-D) interferometric ISAR (InISAR) imaging technique is developed. The traditional InISAR imaging with three antennas usually assumes that the baselines are orthogonal to each other and the target moves steadily. However, in practical applications, due to the existence of system error and the unknown moving condition of the noncooperative targets, the baselines used for interference are difficult to be absolutely orthogonal and the movement of the target also tends to be complicated. In this article, a practical maneuvering target 3-D imaging algorithm based on the InISAR of an arbitrary three-antenna configuration is investigated. The main contributions of this article are as follows: 1) abstracted from an actual application scenario, a three-antenna InISAR system model with the baselines being nonorthogonal is established; 2) a joint translational motion compensation strategy, involving a joint accumulated cross correlation method for the envelope alignment and a joint Doppler centroid-tracking approach for the phase correction, is devised for the image coregistration in the range dimension; 3) a complicated movement model of the maneuvering target is described, and a fast high-resolution ISAR imaging algorithm maximum likelihood (ML)-fractional Fourier transform (FRFT), which incorporates the ML with the FRFT, is proposed; and 4) a coordinate correction technique is developed to eliminate the distortion of the 3-D image caused by the nonorthogonality of baselines. The effectiveness and performance of the proposed algorithm are evaluated by several simulations at the last of the article.

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.

Phase Unwrapping for Bistatic InISAR Imaging of Space Targets

In this paper, the spatial-variant property of inverse synthetic aperture radar (ISAR) imaging plane of space targets is exploited for phase unwrapping in bistatic interferometric ISAR (InISAR) imaging of space targets. We utilize the interferometric phases in different orbit arcs to obtain the correct fuzzy factor in a calculated set. The threshold for incorrect fuzzy factor excluding and the orbit arc selection strategy are derived for efficiency. Numerical simulations illustrate the effectiveness of the proposed method.