博士 教授 博士生导师
邢孟道，1975年生，浙江嵊州人，博士，西安电子科技大学电子工程学院教授、博士生导师、前沿交叉研究院副院长。IEEE Fellow（2019）、国家杰出青年科学基金获得者(2018)、国家首届优秀青年科学基金获得者（2012）、科技部创新人才推进计划中青年科技创新领军人才获得者、入选中共中央组织部“万人计划”（2019）、教育部新世纪优秀人才支持计划获得者、陕西省创新团队负责人，2018年获得陕西省科学技术一等奖。担任IEEE Transactions on Geoscience and Remote Sensing副主编，连续五年（2013-2018）入选Elsevier电子和电气工程领域“中国高被引学者榜单”。2002年5月获西安电子科技大学信号与信息处理专业工学博士学位，其博士论文获得2004年度全国百篇优秀博士论文提名奖。2002年7月破格评为副教授，2004年7月破格评为教授。
主持多项国家自然科学基金杰出青年基金、优秀青年基金、重大项目、国防973项目、国家863计划项目、“十二五”预研项目及众多研究所横向课题等；2014年至今在国际遥感顶级期刊TGRS、GRSL、JSTAR发表SCI论文113篇，SCI他引980次，H-index因子42，连续5年入选Elsevier电子和电气工程领域“中国高被引学者榜单”；申请国家授权专利40余项，6项成果以专利形式得到了应用；培养和协助培养“百优”和“省优”博士论文6篇；出版著作4本 (《雷达成像技术》（保铮、邢孟道、王彤等，2006年）、《雷达成像算法进展》（邢孟道、保铮、李真芳、王彤，2014年）、《雷达信号处理基础》（邢孟道、王彤、李真芳等，2008年）、《雷达信号处理基础（第二版）》（邢孟道、王彤、李真芳等，2017年）)。现任IEEE TGRS副主编、IEEE senior member、《雷达学报》等编辑。
Science China Information Sciences，2015，58（）：1–11
This paper proposes a coordinate-transform (CT) implementation for the fast factorized backprojection (FFBP) algorithm (CT-FFBP) to process high-resolution spotlight synthetic aperture radar (SAR) data. Unlike the FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, CT-FFBP finishes the image-projection using CT with the accommodation of chirp-z transform and circular shifting. Without interpolation, CT-FFBP yields enhanced efficiency over the interpolation based FFBP, besides maintaining high precision simultaneously. Both simulation and real-data experiments verifies the efficiency and precision superiorities of the CT-FFBP.
International Journal of Antennas and Propagation ，2015，2015（）：852520
The multiple-input-multiple-output (MIMO) synthetic aperture radar (SAR) system with a linear antenna array can obtain 3D resolution. In practice, it suffers from both the translational motion errors and the rotational motion errors. Conventional single-channel motion compensation methods could be used to compensate the motion errors channel by channel. However, this method might not be accurate enough for all the channels. What is more, the single-channel compensation may break the coherence among channels, which would cause defocusing and false targets. In this paper, both the translational motion errors and the rotational motion errors are discussed, and a joint multichannel motion compensation method is proposed for MIMO SAR 3D imaging. It is demonstrated through simulations that the proposed method exceeds the conventional methods in accuracy. And the final MIMO SAR 3D imaging simulation confirms the validity of the proposed algorithm.
IEEE Geoscience and Remote Sensing Letters，2015，12（5）： 1086 - 10
The multibaseline phase unwrapping (PU) of L 1 -norm can be efficiently solved using linear programming. However, the huge memory requirement of linear programming limits its application in multibaseline PU for large-scale data. In order to reduce the required memory when linear programming is performed, a novel mixed-norm multibaseline PU algorithm is proposed in this letter, which is regarded as an approximation of the L 1 -norm method. In this method, an L∞-norm cost function is employed to substitute for that of the L 1 -norm, i.e., it takes the optimization which is aimed to minimize the maximum component of the optimization variable as the representation of the one that minimizes the absolute sum of L 1 -norm. Consequently, the cost function in the proposed method changes to be an L 1 -norm plus an L ∞ -norm. Compared with the traditional L 1 -norm method, the size of the optimization variable in the proposed method is generally reduced by about one-seventh. Therefore, it is logical that less memory is needed in the proposed algorithm. The effectiveness of the proposed algorithm is validated via a simulated and real repeat-pass interferometric-synthetic-aperture-radar data set.
Journal of Electronics and Information Technology ，2015，37（5）：1111-1115
Multi-baseline phase unwrapping problem can be solved according to find the optimal solution of the L1-norm optimization. However, there are two problems: one is the huge memory required and the other is the difficulty in processing interferograms with severe noise. In order to decrease the memory requirement of the L1-norm method, with a cost function of L-norm is employed to approximate the L1-norm. Consequently, the objective function of the improved multi-baseline phase unwrapping is the form of L-norm+L1-norm, and the size of the new optimization variable is decreased by 57%. The performance of the proposed algorithm is validated via a real dataset with severe noise present, and the experiment demonstrates that the proposed algorithm not only presents a well phase unwrapping result of interferograms with good quality, but also performs a filtering against noise region.
Interferometric SAR (， InSAR)， ,， Multi-baseline,， Phase unwrapping,， L1-norm,， L+， L1-norm
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University，2014，41（6）：12-17,
This paper proposes a new algorithm for solving cross-range scaling for the inverse synthetic aperture radar (ISAR) imaging during a short Coherent Processing Interval (CPI) under a low Signal Noise Ratio (SNR). Based on the sparsity characteristic of the ISAR image, a Weighted Compressive Sensing (WCS) procedure is applied to generate high-resolution images, which can encourage signal components while suppressing noise. Then on the basis of the characteristics of 2-D Fourier transform (2-D FFT) and polar mapping, the Rotation Angle Velocity (RAV) initial estimation is realized by the correlation between two polar images. Finally, the maximum correlation position is found by using WCS, improving the estimation precision and efficiency of RAV. The rescaled ISAR image can be implemented. Both simulated and real-measured data confirm the feasibility and effectiveness of the proposed algorithm.
Journal of Astronautics，2016，37（1）：127-134
For wide swath circular scanning synthetic aperture radar (CSSAR), the complex slant range history and large variant range cell migration (RCM) makes it difficult to implement imaging. To deal with the problem, the echo signal slant range equation of curve trajectory model is established by using high order approximation according to the characters of the CSSAR movements. The rangevariant range history is analyzed in details and an improved Chirp Scaling (CS) imaging algorithm for large scene CSSAR is proposed based on the derived high order approximated twodimensional spectrum. Simulation results demonstrate that the range equation under curve trajectory model is more precise and the algorithm can deal with the problem of the rangevariant RCM perfectly and realize the large scene imaging successfully.
Circular scanning SAR,， Range cell migration (， RCM)， ,， Twodimensional spectrum,， Chirp scaling (， CS)，
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ，2016，10（4）：1425 - 144
For high-resolution wide-swath synthetic aperture radar imaging algorithms, signal reconstruction is a key step. The steering vector plays an important role in signal reconstruction, which can be constructed by the ambiguity components. The information of the ambiguity components, e.g., number and index, is usually regarded to be constant and known. However, we find that the information of ambiguity components is always a piecewise function of the baseband frequency. This means that the steering vector cannot be preconstructed accurately and it will negatively affect the signal reconstruction. This paper presents an improved signal reconstruction method based on the Doppler spectrum estimation. The proposed method can estimate the variant ambiguity components to form the steering vector exactly by the Capon estimation. As a result, the method is able to restore the Doppler spectrum entirely and performs well on the noise reduction. Moreover, the baseband Doppler centroid and antenna pattern can be obtained in the proposed method. Simulated data and airborne raw data are processed to validate the algorithm.
IEEE Journal of Selected Topics in Signal Processing，2015，9（8）：1583 - 159
In this paper, the squint mode multi-channel (MC) synthetic aperture radar (SAR) with hybrid baseline and fluctuant terrain is proposed and studied for high-resolution and wide-swath (HRWS) imaging. During the imaging process, due to the cross-track baseline and fluctuant terrain, the azimuth signal reconstruction is the kernel problem for this imaging mode. To deal with this problem, in this paper a robust azimuth signal reconstruction approach is proposed, where terrain elevation of scene is considered. At first, the pre-processing of the linear range cell migration correction (RCMC) and topography-independent phase compensation is implemented in the azimuth time domain. After that, combining the azimuth echo signal characteristics, the local polynomial Fourier transform (LPFT) is utilized to obtain the coarse-focused SAR image. Then, based on joint pixel pair vector and robust Capon beamforming (RCB), a Doppler ambiguity suppression approach is proposed to reconstruct the Doppler ambiguity-free azimuth signal in LPFT frequency domain, during which the influence of the cross-track baseline component and fluctuant terrain is eliminated using the coarse digital elevation model (DEM) for the imaging scene. At last, the chirp scaling imaging algorithm is utilized to focus the SAR image. The effectiveness of the proposed imaging approach is demonstrated via simulated and real measured squint mode MC-HRWS SAR data.
IEEE Geoscience and Remote Sensing Letters，2014，12（1）：165 - 169
This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.
IEEE Geoscience and Remote Sensing Letters，2015，12（8）：1755 - 175
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.
IET Radar, Sonar & Navigation，2015，9（7）：900 – 906
In airborne case, synthetic aperture radar images generally suffer from the deterioration because of the unknown phase error caused by unstable platform and atmosphere perturbation. To obtain the phase error, a novel autofocus algorithm referred to as blind homomorphic deconvolution autofocus algorithm is proposed in this study. In this method, a wavelet scaling function is used to construct a smooth subspace that is orthogonal to noise subspace. Then, the phase error can be separated and reconstructed by the generated smooth subspace based on the differences of the smoothness properties between phase error and image reflectivity. Compared with the traditional autofocus methods, the proposed method does not require an iterative estimation. Thus, the computational complexity can be significantly reduced. Simulation and real data processing results validate the effectiveness of the proposed method.
image reconstruction， computational complexity， radar imaging， phase estimation， airborne radar， deconvolution， iterative methods， wavelet transforms， synthetic aperture radar
IET Radar, Sonar & Navigation，2015，9（7）：875 – 880
The back-projection algorithm (BPA) is a useful technique for synthetic aperture radar (SAR) imaging. The fast factorised BPA (FFBPA) recursively partitions the back-projection integral, thus significantly reducing the overall computation complexity corresponding to the improvement obtained by the FFT algorithm compared with the direct implementation of the discrete Fourier transform. In this study, the authors propose a new fast method, termed as the factorised polar-format BPA (FPFBPA), which combines the polar format algorithm and the factorised back-projection concept. It is demonstrated that, when the first-stage subaperture in the FPFBPA contains more than 3 pulses, the proposed method further reduces the computation complexity comparing with FFBPA, provided that the same interpolation method is used. The proposed algorithm is also capable of processing curved orbit and multi-mode SAR data. The effectiveness of the proposed algorithm is verified by the processed results using measured airborne data.
discrete Fourier transforms， airborne radar， synthetic aperture radar， radar imaging， computational complexity， interpolation
IEEE Transactions on Geoscience and Remote Sensing，2014，53（2）：687 - 709
This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches.
IET Radar, Sonar & Navigation ，2014，8（6）：685 – 691
The compact polarimetric (CP) synthetic aperture radar (SAR) data is directly decomposed into three canonical components involving surface, double-bounce and volume, which avoids the approximated quad-polarimetric reconstruction based on several promising assumptions. Through several algebraic operations, the three-component decomposition under CP modes (π/4 mode and right circular transmit, linear receive (CTLR) mode) is deduced. With the experiments by using two classical data sets – San Francisco data from AIRSAR system and Oberpfaffenhofen data from ESAR system, the feasibility and flexibility of the CP decomposition are validated. Also, it has been found that the use of CP data can be further extended into many other polarimetric applications, and the three-component decomposition can achieve a better result with CTLR mode than with π/4 mode.
radar imaging， synthetic aperture radar， radar polarimetry
Systems Engineering and Electronics，2010，38（1）：60-63
An unsupervised classification method based on find of density peaks(FDP) is proposed for the polarimetric synthetic aperture radar (POLSAR) image. For the great impact of the boundary and strong points in the POLSAR image, the following density becomes unstable. The saliancy image which is based on the information entropy is proposed to remove these points before classification. The feature in H//A/SPAN space of the remaining pixels is weighted with the saliancy value. Then the unsupervised classification is achieved based on the FDP. In the experiment with the ESAR data, results validate the effectiveness of the new method.
IET Radar, Sonar & Navigation，2016，10（3）：586 – 594
In high-resolution inverse synthetic aperture radar (ISAR) imaging, the rotational motion of the targets tends to introduce the time-variant Doppler modulation in the echo, which acts as the range-variant phase errors in the phase history. Moreover, the performance of translational phase error correction may be dramatically degraded without properly considering the range-variant phase errors. In this study, a joint approach of translational and rotational phase error corrections is introduced into high-resolution ISAR imaging. In the procedure, the joint phase error correction is modelled as that of range-invariant and range-variant phase errors using a metric of minimum entropy. Then, the minimum-entropy optimisation is solved by employing a coordinate descend method based on quasi-Newton solver. In comparison of the conventional methods, the proposed approach in this study promises a better performance of phase error correction with a higher efficiency. Finally, experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.
radar imaging， optimisation， minimum entropy methods， error correction， synthetic aperture radar， Doppler radar
IEEE Transactions on Geoscience and Remote Sensing ，2014，53（1）：494 - 504
Two-dimensional phase unwrapping (PU) is a key step of synthetic aperture radar interferometry (InSAR). Moreover, the conventional single-baseline PU method is restricted to the phase continuity assumption, so it cannot work correctly in the case that phase jumps between adjacent pixels are larger than π. To effectively solve this problem, multibaseline PU is put forward. The performance of conventional multibaseline PU methods is directly related to the noise level. In order to improve noise robustness, a cluster analysis (CA) based noise-robust PU algorithm for multibaseline interferograms (CANOPUS) is proposed in this paper, which is the extension and improvement of the CA-based efficient multibaseline PU algorithm proposed by H. Yu. For the sake of overcoming the disadvantages of the CA method, the dimension of the recognizable mathematical pattern is expanded. Under this condition, due to the density discrimination in spatial space, different clusters are able to be distinguished by the density-based clustering algorithm, and clusters are regarded as a set of density-connected patterns. Compared with the conventional CA method, the significant advantage of the new algorithm is that it improves noise robustness. What is more, the proposed algorithm runs in linear time. From the experiment results, it can be seen that the proposed method may be effectively applied to multibaseline InSAR data sets.
IEEE Transactions on Geoscience and Remote Sensing，2016，54（7）：4224 - 423
In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space-time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.
IEEE Transactions on Geoscience and Remote Sensing ，-0001，54（7）： 4023 - 40
The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ，2015，8（8）：4010 - 402
In high-resolution radar imaging, the rotational motion of targets generally produces migration through resolution cells (MTRC) in inverse synthetic aperture radar (ISAR) images. Usually, it is a challenge to realize accurate MTRC correction on sparse aperture (SA) data, which tends to degrade the performance of translational motion compensation and SA-imaging. In this paper, we present a novel algorithm for high-resolution ISAR imaging and scaling from SA data, which effectively incorporates the translational motion phase error and MTRC corrections. In this algorithm, the ISAR image formation is converted into a sparsity-driven optimization via maximum a posterior (MAP) estimation, where the statistics of an ISAR image is modeled as complex Laplace distribution to provide a sparse prior. The translational motion phase error compensation and cross-range MTRC correction are modeled as joint range-invariant and range-variant phase error corrections in the range-compressed phase history domain. Our proposed imaging approach is performed by a two-step process: 1) the range-invariant and range-variant phase error estimations using a metric of minimum entropy are employed and solved by using a coordinate descent method to realize a coarse phase error correction. Meanwhile, the rotational motion can be obtained from the estimation of range-variant phase errors, which is used for ISAR scaling in the cross-range dimension; 2) under a two-dimensional (2-D) Fourier-based dictionary by involving the slant-range MTRC, joint MTRC-corrected ISAR imaging and accurate phase adjustment are realized by solving the sparsity-driven optimization with SA data, where the residual phase errors are treated as model error and removed to achieve a fine correction. Finally, some experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.