PHASE AUTOFOCUSING ALGORITHM FOR COMPRESSED INVERSE SYNTHETIC APERTURE RADAR IMAGING

As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.

A COMPARISON OF SOME ELECTRONIC COUNTERMEASURES ON INVERSE SYNTHETIC APERTURE RADAR (ISAR)

Inverse Synthetic Aperture Radar (ISAR) is an important means for target classification, recognition, identification and many other military applications. A simulation model of ISAR system is established after analyzing the principle of ISAR imaging, and then several ECM (Electronic Counter Measurement) techniques are studied. Simulation experiments are done on the basis of such research. The experimental result of the research can be used for ECM equipment.

Micro-motion effect in inverse synthetic aperture radar imaging of ballistic mid-course targets

For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.

Inverse synthetic aperture radar range profile compensation of plasma-sheath-enveloped reentry object

The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this problem,we deduce the echo model of the plasma-sheathenveloped reentry object.By estimating the coupled velocities,we propose a compensation method to correct the defocus of an inverse synthetic aperture radar(ISAR)image in range dimension to improve the quality of the ISAR images.The simulation results suggest that the echoes from different regions of the surface of the reentry object have various coupling velocities,and the higher the coupled velocity,the more serious the displacement and energy diffusion in the range dimension.Our proposed method can correct the range dimension aberration.Two measurement metrics were used to evaluate the improvement of the compensation method.

Compressive Sensing Inverse Synthetic Aperture Radar Imaging Based on Gini Index Regularization

In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.

Automatic target recognition method for inverse synthetic aperture sonar imaging

To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar（ISAS） imaging,a method for target recognition is proposed based on topology vector feature（TVF） of multiple highlights. Analysis of the projection relationship from 3 D space to 2 D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. A classification rate of 84.0% is reached.

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 modified OMP method for multi-orbit three dimensional ISAR imaging of the space target

The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)algorithm.Firstly,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension.Then,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data.Finally,scattering centers can be reconstructed with specific three dimensional locations.Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

ISAR MOTION COMPENSATION USING ROPE

Motion compensation is a key step for inverse synthetic aperture radar (ISAR) imaging. Many algorithms have been proposed. The rank one phase estimation (ROPE) algorithm is a good estimator for phase error widely used in SAR. The ROPE algorithm is used in ISAR phase compensation and the concrete implementation steps are presented. Subsequently, the performance of ROPE is analyzed. For ISAR data that fit the ROPE algorithm model, an excellent compensation effect can be obtained with high computation efficiency. Finally, ISAR real data are processed with ROPE and its imaging result is compared with that obtained by the modified Doppler centroid tracking (MDCT) method, which is a robust and good estimator in ISAR phase compensation.

Entropy function optimization for radar imaging

The convergence performance of the minimum entropy auto-focusing（MEA） algorithm for inverse synthetic aperture radar（ISAR） imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multidimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking （DCT）algorithm. Since these values are obtained according to the maximum likelihood （ML） principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

ISAR MOTION COMPENSATION USING MODIFIED DOPPLER CENTROID TRACKING METHOD

With regard to the phase compensation in inverse synthetic aperture radar (ISAR),the modified Doppler centroid tracking (MDCT) method is developed which applies the phase gradient autofocus (PGA) algorithm developed by Wahl[1]to improve the Doppler centroid tracking (DCT) method[2].When the phase compensation is performed,the proposed approach smartly eliminates the effect of the rotational phase component (RPC) on the estimation of the translational phase component (TPC) by circular shifting,windowing and iteration steps. After several iterations,the maximum likelihood estimation and compensation of the TPC of the target can be realized more effectively.The processing results of live data show that the proposed method can improve the imaging quality of ISAR significantly.

基于0.22 THz高功率回旋管的步进频率雷达系统设计与仿真

基于0.22 THz高功率回旋管振荡器设计了频率步进成像雷达,采用收发分开的双卡塞格伦天线,发射信号形式为频率步进脉冲信号,回旋管频率调节通过调整回旋管电子束电压或者超导磁场强度来实现。给出了成像雷达设计方案,对雷达最大作用距离和步进频率雷达系统高分辨一维距离像进行了分析,开展了ISAR成像仿真。理论计算和仿真结果表明,该雷达对于散射面积为0.01 m2的目标探测距离可达到1.982 km,可分辨500 m处间距2 cm的点目标。该雷达可对低高度小目标进行高精度探测,比如对可携带武器和危险物质的无人机进行高精度探测与目标识别。

ISAR imaging based on improved phase retrieval algorithm

Traditional inverse synthetic aperture radar(ISAR)imaging methods for maneuvering targets have low resolution and poor capability of noise suppression. An ISAR imaging method of maneuvering targets based on phase retrieval is proposed,which can provide a high-resolution and focused map of the spatial distribution of scatterers on the target. According to theoretical derivation, the modulus of raw data from the maneuvering target is not affected by radial motion components for ISAR imaging system, so the phase retrieval algorithm can be used for ISAR imaging problems. However, the traditional phase retrieval algorithm will be not applicable to ISAR imaging under the condition of random noise. To solve this problem, an algorithm is put forward based on the range Doppler(RD) algorithm and oversampling smoothness(OSS) phase retrieval algorithm. The algorithm captures the target information in order to reduce the influence of the random phase on ISAR echoes, and then applies OSS for focusing imaging based on prior information of the RD algorithm. The simulated results demonstrate the validity of this algorithm, which cannot only obtain high resolution imaging for high speed maneuvering targets under the condition of random noise, but also substantially improve the success rate of the phase retrieval algorithm.

ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR)imaging has been successfully addressed by popular motion compensation algorithms.However,when the target’s rotational velocity is sufficiently high during the dwell time of the radar,such compensation algorithms cannot obtain a high quality image.This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm.The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion,and the deep learning algorithm is used to achieve a super-resolution image.The uniformly distributed point target data are used as the data set of the training u-net network.In addition,this method does not require estimating the motion parameters of the target,which simplifies the algorithm steps.Finally,several experiments are performed to demonstrate the effectiveness of the proposed algorithm.

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.

IMAGE-BASED SCALING FOR SHIP TOP VIEW ISAR IMAGES

A high quality top view or side view ship ISAR image with proper cross-range scale is very useful for target recognition. A technique aiming at solving the ship top view image scaling is developed, in which the cross-range scale information is obtained by measuring the slopes of the two feature lines of the ship, the centerline and the stern line. Neither the prior knowledge about the ship nor the tracking data is required. The proposed technique is fully based on the image. It is used with ease and high efficiency. Examples are provided to demonstrate its effectiveness with both simulated and real data. The scaled results are in accordance with the real shape of the ship.

Simulation of two-dimensional ISAR decoys on a moving platform

It is potentially useful to perform deception jamming using the digital image synthesizer （DIS） since it can form a two-dimensional （2D） decoy but suffers from multiple decoys ge- neration. Inspired by the intermittent sampling repeater jamming （ISRJ）, the generation of inverse synthetic aperture radar （ISAR） decoys is addressed, associated with the DIS and the ISRJ. Radar pulses are sampled intermittently and modulated by the scatter- ing model of a false target by mounting the jammer on a moving platform, and then the jamming signals are retransmitted to the radar and a train of decoys are induced after ISAR imaging. A scattering model of Yak-42 is adopted as the false-target mo- dulation model to verify the effectiveness of the jamming method based on the standard ISAR motion compensation and image for- mation procedure.

Particle swarm optimization for rigid body reconstruction after micro-Doppler removal in radar analysis

The rotating micro-motion parts produce micro-Doppler(m-D)effects which severely influence the quality of inverse synthetic aperture radar(ISAR)imaging for complex moving targets.Recently,a method based on short-time Fourier transform(STFT)and L-statistics to remove m-D effects is proposed,which can separate the rigid body parts from interferences introduced by rotating parts.However,during the procedure of removing m-D parts,the useful data of the rigid body parts are also removed together with the m-D interferences.After summing the rest STFT samples,the result will be affected.A novel method is proposed to recover the missing values of the rigid body parts by the particle swarm optimization(PSO)algorithm.For PSO,each particle corresponds to a possible phase estimation of the missing values.The best particle is selected which has the minimal energy of the side lobes according to the best fitness value of particles.The simulation and measured data results demonstrate the effectiveness of the proposed method.

ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

New method of time-frequency representation for ISAR imaging of ship targets

Inverse synthetic aperture radar （ISAR） imaging of ship targets is very important in the national defense. For the high maneuverability of ship targets, the Doppler frequency shift of the received signal is time-varying, which will degrade the ISAR image quality for the traditional range-Doppler （RD） algorithm. In this paper, the received signal in a range bin is characterized as the multi-component polynomial phase signal （PPS） after the motion compensation, and a new approach of time-frequency represen- tation, generalized polynomial Wigner-Ville distribution （GPWVD）, is proposed for the azimuth focusing. The GPWVD is based on the exponential matched-phase （EMP） principle. Compared with the conventional polynomial Wigner-Ville distribution （PWVD）, the EMP principle transfers the non-integer lag coefficients of the PWVD to the position of the exponential of the signal, and the interpolation can be avoided completely. For the GPWVD, the cross-terms between multi-component signals can be reduced by decomposing the GPWVD into the convolution of Wigner-Ville distribution （WVD） and the spectrum of phase adjust functions. The GPWVD is used in the ISAR imaging of ship targets, and the high quality instantaneous ISAR images can be obtained. Simulation results and measurement data demonstrate the effectiveness of the proposed new method.