OPTIMIZATION OF WEIGHTED HIGH-RESOLUTION RANGE PROFILE FOR RADAR TARGET RECOGNITION

For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

频域空域二维稀疏SIMO高分辨雷达成像方法

利用频域稀疏的线性调频步进信号(FSCS)作为雷达发射信号,并结合空域稀疏的SIMO雷达阵列来构建二维稀疏的高分辨雷达成像模型。针对该稀疏模型,首先通过对低维数据简单补零处理,然后利用图像熵准则完成对运动目标速度的有效估计。在此基础上,结合压缩感知理论,构造有效的观测矩阵、稀疏变换矩阵以及重构算法,获得目标高分辨距离像(HRRP),进一步提出基于保相性的频域空域二维稀疏SIMO高分辨雷达成像方法。该方法可以大幅减少FSCS脉冲串的子脉冲个数,大幅减少SIMO高分辨雷达接收天线阵元个数,并获得高质量的HRRP和目标二维像。仿真实验验证了本文方法的有效性和鲁棒性。

小斜视角下稀疏空域SIMO雷达运动目标成像方法

针对小斜视角下单发多收(SIMO)雷达对空运动目标单次快拍成像时天线数目较多的问题,提出了一种稀疏空域SIMO雷达运动目标成像方法。首先详细分析了小斜视角下SIMO雷达单次快拍成像原理,其次结合压缩感知理论具体阐述了小斜视角下稀疏空域SIMO雷达运动目标成像方法。该方法不仅能够对运动目标实现单次快拍成像,避免了非合作运动目标强加速、大转角等引起的运动补偿难题,同时又能够大幅减少接收天线单元数,便于工程实现。最后利用仿真实验验证了文中方法的有效性和可行性。

A WEIGHTED FEATURE REDUCTION METHOD FOR POWER SPECTRA OF RADAR HRRPS

Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.

Photonics-based radar with balanced I/Q de-chirping for interference-suppressed high-resolution detection and imaging

Photonics-based radar with a photonic de-chirp receiver has the advantages of broadband operation and real-time signal processing, but it suffers from interference from image frequencies and other undesired frequency-mixing components, due to single-channel real-valued photonic frequency mixing. In this paper, we propose a photonicsbased radar with a photonic frequency-doubling transmitter and a balanced in-phase and quadrature(I/Q)de-chirp receiver. This radar transmits broadband linearly frequency-modulated signals generated by photonic frequency doubling and performs I/Q de-chirping of the radar echoes based on a balanced photonic I/Q frequency mixer, which is realized by applying a 90° optical hybrid followed by balanced photodetectors. The proposed radar has a high range resolution because of the large operation bandwidth and achieves interference-free detection by suppressing the image frequencies and other undesired frequency-mixing components. In the experiment, a photonics-based K-band radar with a bandwidth of 8 GHz is demonstrated. The balanced I/Q de-chirping receiver achieves an image-rejection ratio of over 30 dB and successfully eliminates the interference due to the baseband envelope and the frequency mixing between radar echoes of different targets. In addition, the desired dechirped signal power is also enhanced with balanced detection. Based on the established photonics-based radar,inverse synthetic aperture radar imaging is also implemented, through which the advantages of the proposed radar are verified.

MIMO雷达降维的低运算量波束形成方法

多输入多输出(Multiple Input and Multiple Output,MIMO)雷达在运用常规地最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)波束形成器和线性约束最小方差(Linearly Constrained Minimum Variance,LCMV)波束形成器进行接收波束形成的时候,需处理的数据维数比常规雷达要大许多,由此导致了其运算量十分巨大。考虑到MIMO雷达发射的是多个相互正交的波形,所以匹配滤波之后在进行接收波束形成的时候可以将MIMO雷达全维波束形成等效为多个单输入多输出(Single Input and Multiple Output,SIMO)雷达的波束形成合成,由此在进行数据处理的时候降低了数据的维数,减少了估计协方差矩阵需要的快拍数目,大大降低了运算量,并且与已有的降维算法相比不需要对波束形成权矢量进行迭代求解。仿真表明在大量数据快拍数时新方法与全维的接收波束形成性能基本一致,且在低数据快拍数时依然保持良好的性能,同时运算量与全维方法相比大大下降。

相干MIMO雷达无模糊区面积缩小问题与优化扩展技术研究

在时延-多普勒平面上,无模糊区面积决定雷达距离模糊与距离杂波折叠特性以及多普勒模糊与多普勒频率混杂特性.文章首次从理论上证明,采用N个波形的相干多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达存在无模糊多普勒缩小现象,在一定条件下缩小为单输入-多输出(Single-Input Multi-Output,SIMO)雷达无模糊多普勒的1/N,导致该雷达无模糊区面积缩小为SIMO雷达的1/N,并给出了仿真实例.无模糊区面积缩小将导致雷达在杂波环境下性能下降.对此,提出了将相干MIMO无模糊区面积扩展到与SIMO雷达相等的波形设计技术,并给出恢复实例,解决了采用N个频率波形的相干MIMO雷达无模糊区面积缩小的基础性问题.

近场单发多收合成孔径雷达成像的频域算法

提出了一种适用于单发多收合成孔径雷达成像的频域算法。该方法利用傅里叶变换将回波数据变换至波数域进行扩维,建立了波数域的补偿关系,从而在波数域实现了对波前弯曲的精确补偿,因此具有较高的成像精度,并且可以用于近场三维成像。另外,该方法采用基于快速傅里叶变换的成像结构显著提高了成像效率。理论分析和实验结果表明,与后向投影算法相比,该方法能够获得相同成像结果的同时大大降低运算量。

FAST ALGORITHM FOR NON-UNIFORMLY SAMPLED SIGNAL SPECTRUM RECONSTRUCTION

In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).

Advanced high-order nonlinear chirp scaling algorithm for high-resolution wide-swath spaceborne SAR

Spaceborne Synthetic Aperture Radar(SAR) is a well-established and powerful imaging technology that can provide high-resolution images of the Earth’s surface on a global scale. For future SAR systems, one of the key capabilities is to acquire images with both high-resolution and wide-swath. In parallel to the evolution of SAR sensors, more precise range models, and effective imaging algorithms are required. Due to the significant azimuth-variance of the echo signal in High-Resolution Wide-Swath(HRWS) SAR, two challenges have been faced in conventional imaging algorithms. The first challenge is constructing a precise range model of the whole scene and the second one is to develop an effective imaging algorithm since existing ones fail to process highresolution and wide azimuth swath SAR data effectively. In this paper, an Advanced High-order Nonlinear Chirp Scaling(A-HNLCS) algorithm for HRWS SAR is proposed. First, a novel Second-Order Equivalent Squint Range Model(SOESRM) is developed to describe the range history of the whole scene, by introducing a quadratic curve to fit the deviation of the azimuth FM rate. Second, a corresponding algorithm is derived, where the azimuth-variance of the echo signal is solved by azimuth equalizing processing and accurate focusing is achieved through a high-order nonlinear chirp scaling algorithm. As a result, the whole scene can be accurately focused through one single imaging processing. Simulations are provided to validate the proposed range model and imaging algorithm.

RECONSTRUCT AZIMUTH SIGNAL AND SUPPRESS INTERBEAM AMBIGUITIES OF SPCMB SAR WITH HYBRID FILTERBANK

Conventional Synthetic Aperture Radar (SAR) systems cannot obtain high-resolution and wide-swath illumination area due to the well-known minimum antenna area constraint. Single Phase Center MultiBeam (SPCMB) technique can overcome this limitation by adding spatial sampling through multiple receivers in azimuth direction. Unfortunately, this approach will lead to an increase of azimuth ambiguities (interbeam ambiguities), because each receive beam’s mainlobe overlaps with the other ones’ sidelobes. This paper proves that the front part of SPCMB SAR systems can be considered to be a hybrid filterbank. Therefore, the azimuth signal can be reconstructed and the interbeam am- biguities can be effectively suppressed by a well-designed hybrid filterbank.

RFI Detection for Multichannel HRWS SAR System Based on Spatial Cross Correlation

Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for HRWS SAR missions,which distorts measure-ments and produces image artifacts.In this paper,the spatial cross-correlation coefficients of multichannel HRWS SAR signals are investigated for RFI detection.It is found when the two channels are correlated,RFI-polluted areas present lower coherence values than non-polluted areas in the same scenarios,which makes previous methods fail.Further,this paper studies the case of two fully decorrelated channels to maximize the coherence difference among RFI and target echoes,and RFI detection is realized by exploiting the anomaly value of coherence.Experimental results of real air-borne multichannel SAR data demonstrate that the RFI can be detected successfully.

Radar automatic target recognition based on feature extraction for complex HRRP

Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.