Focused Inverse Synthetic Aperture Radar Image Research Articles

Use of ICA to Separate Micro-Doppler Signatures in ISAR Images of Aircraft That Has Fast-Rotating Parts

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts.

ISAR Imaging Algorithm for Maneuvering Targets Based on Fourier Ptychographic Microscopy

Aiming at the problems of low resolution and poor noise suppression for maneuvering targets with traditional inverse synthetic aperture radar (ISAR) imaging algorithms, we propose a motion compensation-driven high-resolution imaging algorithm based on Fourier ptychographic microscopy (FPM) technology. FPM is a phase retrieval technology that uses the concept of synthetic aperture to obtain the high-resolution image of the target. The process of FPM imaging is composed of the enhancement of the known target information in the spatial domain and some fixed restricted alternating iteration in the frequency domain. This article analyzes the mathematical model of maneuvering target echo and then studies the rationality of applying the FPM phase retrieval algorithm to ISAR maneuvering target imaging. The proposed algorithm mainly includes two steps. First, a series of low-resolution ISAR images are generated by sampling echo data through an aperture function. Second, the FPM phase retrieval technology is applied to splice and reconstruct the spectrum of the low-resolution images to achieve the focused ISAR image. The effectiveness of the proposed algorithm is verified by using multiple groups of simulation data and real radar data.

ISAR Imaging of a Maneuvering Target Based on Parameter Estimation of Multicomponent Cubic Phase Signals

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Approach for ISAR imaging of near-field targets based on coordinate conversion and image interpolation

Inverse synthetic aperture radar (ISAR) imaging of near-field targets is potentially useful in some specific applications, which makes it very important to efficiently produce high-quality image of the near-field target. In this paper, the simplified target model with uniform linear motion is applied to the near-field target imaging, which overcomes the complexity of the traditional near-field imaging algorithm. According to this signal model, the method based on coordinate conversion and image interpolation combined with the range-Doppler (R-D) algorithm is proposed to correct the near-field distortion problem. Compared with the back-projection (BP) algorithm, the proposed method produces better focused ISAR images of the near-field target, and decreases the computation complexity significantly. Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

An approach of motion compensation and ISAR imaging for micro-motion targets

Inverse synthetic aperture radar (ISAR) imaging of the target with the non-rigid body is very important in the field of radar signal processing. In this paper, a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain, and the micro-Doppler (m-D) signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition (CEMD) algorithm, which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center. Then, a better focused ISAR image of the target with the non-rigid body can be obtained consequently. Results of the simulated and raw data demonstrate the effectiveness of the algorithm.

Micro-Doppler Effects Removed Sparse Aperture ISAR Imaging via Low-Rank and Double Sparsity Constrained ADMM and Linearized ADMM.

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.

A Study on RCS and Scattering Point Analysis Based on Measured Data for Maritime Ship

In order to set up radar cross section(RCS) reduction factors for a target, the scattering point position of the target should be identified through inverse synthetic aperture radar(ISAR) image analysis. For this purpose, ISAR image focusing is important. Maritime ship is non-linear maneuvering in the sea, however, which blur the ISAR image. To solve this problem, translational and rotational motion compensation are essential to form focused ISAR image. In this paper, hourglass and ISAR image analysis are performed on the collected data in the sea instead of using the prediction software tool, which takes much time and cost to make computer-aided design(CAD) model of the ship. Keywords: Radar Cross Section(ë ˆì´ë”ë°˜ì‚¬ë‹¨ë©´ì ), Scatterer(ì‚°ëž€ì ), High Resolution Range Profile(ê³ í•´ìƒë„ 거리측면도), Hourglass(모래시계), Inverse Synthetic Aperture Radar(ì—­í•©ì„±ê°œêµ¬ë ˆì´ë”)

ISAR Imaging for Low-Earth-Orbit Target Based on Coherent Integrated Smoothed Generalized Cubic Phase Function

In the high-resolution inverse synthetic aperture radar (ISAR) imaging of low-earth-orbit space targets, the received signal from one range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. The chirp rates and the quadratic chirp rates of the multicomponent CPS need to be estimated with parameter estimation algorithms to obtain the focused ISAR image. In this article, a new parametric range-instantaneous-Doppler ISAR imaging method is proposed, which introduces a new algorithm for parameter estimation of multicomponent CPSs. The cross terms of the generalized cubic phase function (GCPF) are analyzed. By eliminating the cross terms in the ambiguity function domain, the coherent integrated smoothed GCPF (CISGCPF) is proposed to estimate the quadratic chirp rates. Numerical examples are provided to verify the accuracy of the parameter estimation and the effectiveness of the cross-term suppression for CISGCPF in processing multicomponent signals. Simulated and real data imaging results demonstrate the performance of the CISGCPF-based ISAR imaging method. Comparisons with the existing algorithms show that the proposed method is effective in reconstructing focused images with less fake scatterers.

High‐resolution ISAR imaging via MMV‐based block‐sparse signal recovery

Recently, compressed sensing (CS) methods are widely used in high-resolution inverse synthetic aperture radar (ISAR) imaging. However, these CS-based imaging methods generally do not take the block-sparse structure of the ISAR images into account, and the image recovery performance needs to be improved. By utilising the block-sparse structure of the signal, more sparse solution and better focused ISAR images can be obtained. In this study, the authors convert the block-sparse signal recovery problem into a sparse recovery problem for multiple measurement vector (MMV), which can be solved more efficiently. A sparser and more accurate solution can be obtained based on the MMV model, and therefore better focused ISAR images can be recovered. Simulation and experimental results validate the effectiveness of the proposed method.

Optimal Coherent Processing Interval Selection for Aerial Maneuvering Target Imaging Using Tracking Information

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. In this case, selecting optimal coherent processing interval (CPI) (or frame) of the fixed rotational axis is the only way to obtain focused ISAR images. In this article, we focus on selecting optimal CPI for ISAR imaging of maneuvering targets, and a novel method using tracking information is proposed. Without complex computational procedures, the proposed method provides robust performance. The method includes three steps. First, target’s orientation (roll, pitch, and yaw) is estimated by using an extended Kalman filter. Second, the least-square method is adopted to find the best fitted lines of roll, pitch, and yaw angles in different time intervals, and the linearity degrees of the three rotation angles are measured over the imaging intervals. Third, the thresholds of the linearity degrees are set, according to which, optimal CPIs can be selected. The performance of proposed method is demonstrated using both simulated data and measured data. In both cases, optimal CPIs can be selected and well-focused ISAR images can be achieved.

Inverse Synthetic Aperture Radar Imaging of Targets with Complex Motion based on Optimized Non-Uniform Rotation Transform

Focusing on the inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, this paper proposes a modified version of the Fourier transform, called non-uniform rotation transform, to achieve cross-range compression. After translational motion compensation, the target’s complex motion is converted into non-uniform rotation. We define two variables—relative angular acceleration (RAA) and relative angular jerk (RAJ)—to describe the rotational nonuniformity. With the estimated RAA and RAJ, rotational nonuniformity compensation is carried out in the non-uniform rotation transform matrix, after which, a focused ISAR image can be obtained. Moreover, considering the possible deviation of RAA and RAJ, we design an optimization scheme to obtain the optimal RAA and RAJ according to the optimal quality of the ISAR image. Consequently, the ISAR imaging of targets with complex motion is converted into a parameter optimization problem in which a stable and clear ISAR image is guaranteed. Compared to precedent imaging methods, the new method achieves better imaging results with a reasonable computational cost. Experimental results verify the effectiveness and advantages of the proposed algorithm.

ISAR Imaging Algorithm of Multiple Targets with Complex Motions Based on the Fractional Tap Length Keystone Transform

Complex motions of targets cause migration through resolution cells which leads to the inverse synthetic aperture radar (ISAR) image blurred and makes difficulties to follow-up work such as identification, recognition, and classification. In this paper, a novel ISAR imaging algorithm based on the fractional tap length keystone transform (FTLKT) is proposed for multiple targets with complex motions. In noncooperation case, different targets behave variously. For phase coupling of different orders, the proposed algorithm first employs FTLKT to achieve decoupling of the frequency and slow time variable, then detection of Doppler peaks is applied for ambiguity resolution of different targets. According to this, focused ISAR images of different targets are obtained. Simulation results demonstrate the validity of the proposed algorithm.

Improved Frame-Selection Scheme for ISAR Imaging of Targets in Complex 3-D Motion

When a target is in complex three-dimensional (3-D) motions, inverse synthetic aperture radar (ISAR) images cannot be obtained with any motion compensation algorithms. To address this problem, phase nonlinearity can be used to select imaging frames where target 2-D motion is dominant enough to obtain focused ISAR images. Conventionally, adaptive joint time frequency (AJTF) and particle swarm optimization (PSO) algorithms are performed to estimate the phase signals of prominent scatterers. These estimates are used to determine phase nonlinearity. However, such estimation algorithms undergo multi-dimensional optimizations, which yield significant increases in computational complexity for ISAR frame-selections. For more practical use of AJTF- and PSO-based frame-selections, we introduce a method to improve the computational efficiencies of the conventional methods. To this end, in the proposed method, a simple phase unwrapping-and-least square fitting method is used as an alternative to the AJTF and PSO methods. Because PU-LSM can provide reliable phase estimates quickly without complex optimization procedures, the proposed method is an improvement over AJTF- and PSO-based methods, especially in terms of computational efficiency. In simulations using ideal point scatterers, our proposed method performs well, in terms of frame-selection ability and computational efficiency. Furthermore, experimental results using real radar echoes validated the robustness and efficacy our proposed frame-selection method.

Sparse Coding-Inspired High-Resolution ISAR Imaging Using Multistage Compressive Sensing

Sparse coding-inspired high-resolution inverse synthetic aperture radar (ISAR) imaging based on multistage compressive sensing (CS) is proposed in this paper. To achieve high cross-range resolution within a short coherent processing interval (CPI), we divide the ISAR imaging process into multiple stages, each of which can be regarded as sparse-coding processing, and the encoding and decoding matrices can be obtained by solving an optimization problem using the error backpropagation algorithm. The decoding matrix can be regarded as a sparse dictionary, and we can recover the ISAR image by exploiting the smoothed ${l_0}$ norm algorithm based on the decoding matrix and CS theory. The ISAR image is then transformed into a time-domain echo by the inverse fast Fourier transform, and the echo can be regarded as the input for the next stage. At each stage, the resolution is gradually improved until we obtain the expected high-resolution ISAR image. Thus, the signals' energy can be fully accumulated step by step, and we can obtain a less noise and focused ISAR image. Experimental results show that the proposed method can obtain higher quality ISAR images compared with other current techniques, and that it is an effective approach to ISAR imaging within a short CPI.

Isar rotational motion compensation algorithm using polynomial phase transform

When a target is involved in complicated motion, non-uniform rotational motion is produced, and this leads to blurred inverse synthetic aperture radar (ISAR) images. In order to resolve this problem, we propose a new rotational motion compensation (RMC) method. In the proposed method, a polynomial phase signal of a dominant scatterer is estimated through the polynomial phase transform (PPT). Next, a new slow time variable that achieves uniformly sampled radar signal along the aspect angle direction is defined using the estimated polynomial phase signal. We then interpolate the radar signal in terms of the newly defined slow time variable to construct focused ISAR images. In simulations using the point-scatterer aircraft model and radar cross section (RCS) data computed from physical optics (PO), we demonstrated the robustness and effectiveness of our proposed method. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1551–1557, 2016

ISAR 영상 형성을 위한 회전운동보상 기법 연구

In this paper, we propose a inverse synthetic aperture radar(ISAR) rotational motion compensation(RMC) method to remove residual blurring caused by non-uniform rotational motion of a target. First, a range bin having an isolated scatterer is selected. Next, polynomial phase signal in the selected range bin is estimated by using both Fourier transform(FT) and polynomial-phase transform(PPT). Finally, a new slow time variable that uniformly samples radar signal along the aspect angle directions is defined by using the estimated phase signal, and we interpolate radar signal in terms of the new time variable. As a result, rotational motion to blurr ISAR images is removed, and focused ISAR images are obtained. Simulation results using battleship model validate the robustness and effectiveness of our proposed RMC method.

ISAR Imaging of Maneuvering Targets Based on the Modified Discrete Polynomial-Phase Transform.

Inverse synthetic aperture radar (ISAR) imaging of a maneuvering target is a challenging task in the field of radar signal processing. The azimuth echo can be characterized as a multi-component polynomial phase signal (PPS) after the translational compensation, and the high quality ISAR images can be obtained by the parameters estimation of it combined with the Range-Instantaneous-Doppler (RID) technique. In this paper, a novel parameters estimation algorithm of the multi-component PPS with order three (cubic phase signal-CPS) based on the modified discrete polynomial-phase transform (MDPT) is proposed, and the corresponding new ISAR imaging algorithm is presented consequently. This algorithm is efficient and accurate to generate a focused ISAR image, and the results of real data demonstrate the effectiveness of it.

Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

High resolution inverse synthetic aperture radar (ISAR) imaging of targets with complex motion is a main topic in the radar imaging domain. In fact, the traditional range-Doppler algorithm is not appropriate to generate a focused ISAR images because of the time-varying Doppler shifts caused by the target's complex motion. In this study, the azimuth received signal is modelled as multi-component amplitude-modulated and frequency-modulated (AM–FM) signal, and a novel algorithm for the cubic Chirplet decomposition based on generalised cubic phase function is proposed to investigate the AM–FM signal analytically. Then, the corresponding ISAR imaging algorithm associated with the range-instantaneous-Doppler technique is proposed. Results of simulated and real data demonstrate the effectiveness of the presented algorithm.

후방 감시 차량용 레이다를 이용한 ISAR 영상 형성

Abstract This paper introduces the inverse synthetic aperture radar(ISAR) imaging technique for rear view target of an automobile, which uses both linear frequency modulation-frequency shift keying(LFM-FSK) waveform and monopulse tracking. LFM-FSK waveform consists of two sequential stepped frequency waveforms with some frequency offset, and thus, can be used to generate ISAR images of rear view target of an automobile . However, ISAR images can often be blurred due to non-uniform change rate of relative aspect angle between radar and target. In order to address this problem, one-dimensional(1-D) Lagrange inter-polation technique in conjunction with angle information obtained from the monopulse tracking is applied to generate uniform data across the radar’s aspect angle. Simulation results show that the proposed method can provide focused ISAR images. Key words: ISAR, LFM-FSK Waveform, Automotive Radar o;p qrs htuvQY>wxWy_(‘ITz{|}~* x’(;p<h:c€(NIPA-2013-H0203-13-1001).‚ ƒ„ h@tGH th(Department of Electrical Engineering, Pohang University of Science and Technology) Manuscript received November 7, 2013 ; Revised December 3, 2013 ; Accepted December 4, 2013. (ID No. 20131107-111)Corresponding Author: Kyung-Tae Kim (e-mail: kkt@postech.ac.kr)

Micro-Doppler Analysis and Separation Based on Complex Local Mean Decomposition for Aircraft With Fast-Rotating Parts in ISAR Imaging

In traditional inverse synthetic aperture radar (ISAR) imaging of aircraft, rigid-body motion is usually assumed, and a focused ISAR image can be obtained using the classical range-Doppler algorithm after the translational motion compensation. However, in real-world situations, nonrigid-bodies such as the fast-rotating blades, propellers or turbofans are often present in aircraft. The ISAR image of the main body will be shadowed by the micro-Doppler (m-D) induced by the rotating parts of the target, whereas the m-D parameter estimation of the rotating parts also becomes more difficult because of the interference from the main body returns. To solve this problem, the Doppler frequency difference between the main body and the rotating parts of the aircraft is analyzed and the complex local mean decomposition (CLMD) is applied to separate the m-D signature in the ISAR imaging. The CLMD method can separate the oscillation mode embedded in signals accurately and decompose the complex modulation nonstationary signals adaptively into some stable monocomponents. After the separation, better geometrical features of the main body and better m-D features of the rotating parts can be obtained by processing each independently. The results from the simulated and measured data are given to verify the validity of the algorithm proposed in this paper.