Resolution ISAR Imaging Research Articles

High resolution ISAR imaging based on 2D-FMFSBL-net

High resolution ISAR imaging based on 2D-FMFSBL-net

A novel MMV based SBL algorithm for high resolution ISAR imaging

In existing methods of ISAR imaging with MMV (Multiple Measurement Vector) SBL (Sparse Bayesian Learning), the HRRP is always interpolated radially to realize the column-to-column correspondence between HRRP matrix and scattering coefficient matrix, then scattering coefficients are estimated with MMV SBL algorithm. In order to enhance the ability to suppress noise and eliminate stripe interference, a novel MMV SBL algorithm based on bidirectional interpolation (BI-MSBL) is proposed for ISAR imaging in this paper. Besides the radial interpolation and then estimation of scattering coefficients conventionally, the HRRP is also interpolated transversally to realize the row-to-row correspondence between HRRP matrix and scattering coefficient matrix, then scattering coefficients are also estimated with MMV SBL algorithm. So there are two ISAR images can be gained respectively from radially interpolated HRRP and transversally interpolated HRRP. Finally, the two obtained ISAR images are fused to get the final ISAR image. In the experiments, simulated HRRP data and measured HRRP data under different SNRs are used to test the performance of different algorithms, including the conventional R-D (Range-Doppler) algorithm, the MMV SBL algorithm, the PC-MSBL algorithm, and the BI-MSBL algorithm proposed in this paper. Through the comparison among the experimental results, it can be found that the BI-MSBL algorithm is more effective than the other three algorithms in suppressing noise, eliminating stripe interference, and enhancing the clarity of ISAR images. As a shortcoming, the BI-MSBL algorithm needs to work out two estimations of the scattering coefficients, so it needs twice the computation time as that of the MMV SBL algorithm.

A high resolution ISAR imaging method base on image fusion with low SNR

A high resolution ISAR imaging method base on image fusion with low SNR

Three‐dimensional super resolution ISAR imaging based on 2D unitary ESPRIT scattering centre extraction technique

Two-dimensional inverse synthetic aperture radar (2D ISAR) system can produce 2D electromagnetic images of a target, while failed to provide the 3D information. In this study, a novel method for 3D super resolution ISAR imaging is proposed via employing 2D U-estimation of signal parameters via rotational invariance technique (U-ESPRIT) and slice principle. First, the 3D scattered field data tensor is converted into a set of 2D matrix by stacking slices of the 3D data along one specific dimension. Then, 2D U-ESPRIT is applied to extract isolated scatterers from each slice. Combined with least-square method, complex scattering amplitudes are also estimated. Finally, 3D reconstructed scattering centres model with specific locations and complex scattering amplitudes is obtained, which can characterise the target to a great extent. The reverse process, reconstruction of the 3D ISAR image, can be readily done in real time by means of the extracted scattering centres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

High Resolution ISAR Imaging Based on Improved Smoothed L0 Norm Recovery Algorithm

In radar imaging, a target is usually consisted of a few strong scatterers which are sparsely distributed. In this paper, an improved sparse signal recovery algorithm based on smoothed l 0 (SL0) norm method is proposed to achieve high resolution ISAR imaging with limited pulse numbers. Firstly, one new smoothed function is proposed to approximate the l 0 norm to measure the sparsity. Then a single loop step is used instead of two loop layers in SL0 method which increases the searching density of variable parameter to ensure the recovery accuracy without increasing computation amount, the cost function is undated in every loop for the next loop until the termination is satisfied. Finally, the new set of solution is projected into the feasible set. Simulation results show that the proposed algorithm is superior to the several popular methods both in terms of the reconstruction performance and computation time. Real data ISAR imaging obtained by the proposed algorithm is competitive to several other methods.

High Resolution ISAR Imaging Based on Improved Smoothed L0 Norm Recovery Algorithm

In radar imaging, a target is usually consisted of a few strong scatterers which are sparsely distributed. In this paper, an improved sparse signal recovery algorithm based on smoothed l 0 (SL0) norm method is proposed to achieve high resolution ISAR imaging with limited pulse numbers. Firstly, one new smoothed function is proposed to approximate the l 0 norm to measure the sparsity. Then a single loop step is used instead of two loop layers in SL0 method which increases the searching density of variable parameter to ensure the recovery accuracy without increasing computation amount, the cost function is undated in every loop for the next loop until the termination is satisfied. Finally, the new set of solution is projected into the feasible set. Simulation results show that the proposed algorithm is superior to the several popular methods both in terms of the reconstruction performance and computation time. Real data ISAR imaging obtained by the proposed algorithm is competitive to several other methods.

双基地雷达两极区ISAR超分辨成像

双基地雷达两极区ISAR超分辨成像

Modified two-stage separated virtual steering vector-based algorithm for high resolution inverse synthetic aperture radar imaging

A computationally efficient propagator and two-stage separated virtual steering vector based algorithm (SVSV) is recently developed by D. Liu and J. Liang for two-dimensional (2D) frequency estimation. SVSV has introduced a novel electric angle z to jointly estimate the 2D components (frequencies or scatterers' locations) and to avoid the incorrect mismatch. However, SVSV cannot resolve the multiple 2D components having equivalent value of z. This problem limits the use of SVSV in ISAR imaging application. Hence, in this paper, we have proposed a new modified SVSV method called MSVSV for 2D ISAR imaging. Main contributions of our work are: (a) use of the propagator based subspace methods (SVSV and MSVSV) for 2D high resolution ISAR imaging. (b) We have suggested a technique to estimate the number of unknown 2D components prior to calculating the propagator operator from the covariance matrix. (c) We have incorporated a technique, due to which MSVSV can successfully resolve the multiple scatterers sharing the same electric angles, where SVSV fails. Radar images are generated for simulated and experimental target data (collected at AERO, Pakistan) by using SVSV, MSVSV and conventional Fourier Transform methods.

UWB Time Domain Near Field High Resolution ISAR Imaging

近距离目标超高分辨率微波成像技术在安全检测、非破坏性控制、生物医学等领域中有着非常重要的应用价值和广阔的应用前景。该文利用基于窄脉冲形式的超宽带时域雷达系统研究近距离目标的高分辨率ISAR成像，给出了仿真和实测结果。利用转台旋转目标，基于超宽带脉冲源和超宽带收发天线以及高性能取样示波器，配以同步触发脉冲和自行研发的数据采集软件，搭建了该时域雷达实验系统，提出了准确获取目标回波信息的实验条件，和实现回波延时精确校正的误差补偿方法。并针对采样时间窗内的杂波干扰，讨论了时域后向投影算法(Back Projection，BP)与背景对消技术相结合的成像算法，实现了分辨率为8 mm的近距离目标的成像，准确地反映了目标的位置、形状和大小等信息。

High Resolution ISAR Imaging of Targets with Rotating Parts

For a target with rotating parts, the ISAR image of its main body may be overshadowed by micro-Doppler signals, which are traditionally treated as interference and removed from radar echoes. However, it is potentially useful to perform imaging using micro-Doppler signals because they contain information on the geometrical structure of the rotating part. Inspired by the inverse Radon transform, well-known in the image processing community, this paper creates a data-recording model for two-dimensional (2D) ISAR imaging of rotating parts. Based on this model, the real-valued inverse Radon transform is applied to image formation in the range-slow time domain. Then we put forward a complex-valued inverse Radon transform for imaging of rotating parts, which utilizes both the sinusoidal modulus and the phase of the radar echoes. Additionally, we analyze possible problems when applying these algorithms in real world situations and provide corresponding solutions. Finally, the effectiveness of the algorithms is demonstrated using simulated and measured data.

A New Method of High Resolution ISAR Imaging under Low SNR Based on Improved Compressive Sensing

针对低信噪比下基于压缩感知(CS)的ISAR成像方法性能下降甚至失效的问题，该文提出了一种改进CS的成像方法，即用能量门限分离含目标的距离单元和噪声单元，同时在方位向上用相干投影来提高观测数据的信噪比，利用迭代加权的1-范数优化以增强真实散射点能量并且压制噪声。 改进CS算法适用于强噪声的环境下的ISAR成像，并可利用很少脉冲数获得高分辨的目标像。实测数据处理验证了该方法可以有效克服强噪声与杂波。同时，仅用16个回波脉冲就可得到高分辨ISAR图像，验证了该方法在非常有限脉冲条件下的稳健性。

Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling

Recent theory of compressed sampling (CS) suggests that exact recovery of an unknown sparse signal with overwhelming probability can be achieved from very limited number of samples. In this letter, we adapt this idea and present a framework of high-resolution inverse synthetic aperture radar imaging with limited measured data. During the framework, we mathematically convert the imaging into a problem of signal reconstruction with orthogonal basis; hence, a conceptive upper bound of the cross-range resolution is presented based on the CS theory. Real data results show that the CS imaging approach outperforms the conventional range-Doppler one in resolution.

A new data extrapolation algorithm for high resolution ISAR imaging

A new data extrapolation algorithm for high resolution radar imaging is presented. The backscattered data are modeled as an autoregressive process where the prediction coefficients are computed using 1D least-square lattice filters. Unlike the well-known Burg or modified covariance methods, least square lattice modeling yields different prediction coefficients for forward and backward directions. The proposed method does not need to satisfy Levinson recursion, i.e. does not suffer from the limitations of the Burg method such as spectral splitting or bias in the locations of the scattering centers. Moreover, due to its lattice structure it does not need any matrix inversion like the modified covariance method. Results obtained for an experimental target are included to confirm the proposed algorithm.