Eigen-Range Profiles for Radar Target Classification

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Radar high resolution range profile recognition via multi-SV method

For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors (multi-SV) method. With the proposed method, a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally, the experiment based on the measured data verifies the excellent performance of this method.

Feature Extraction of Radar Range Profiles Based on Normalized Central Moments

The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.

Multichannel noncoherent integration detection using high range resolution profile

A multichannel noncoherent integration detection method based on high range resolution profile was presented in this paper. According to the property of the moment generating function, the distribution characteristics of the noncoherent integrated signals with or without target presence were derived under the circumstance with noncorrelated Gaussian distribution noises. The loss of noncoherent integration was due to improper selection of integration range of cell numbers. A multi channel noncoherent integration detection scheme where the integration number in each channel va ries was proposed to solve this problem. The quality of this method for detection of various targets was evaluated. A comparison of fixed integration range cell number detection and multichannel inte gration detection for a high range resolution profile was presented. Simulation results indicated that the principle of the method was correct and performed well for unknown physical dimension targets. The method required little prior knowledge about target and was convenient for practical implementa tion.

THE INFLUENCE OF SPECKLE ON HIGH RESOLUTION RANGE PROFILE RECOGNITION BASED ON THE MATCHING SCORE

Template database is the key to radar automation target recognition based on High Resolution Range Profile (HRRP). From the traditional perspective, average HRRP is a valid template for it can represent each HRRP without scatterer Moving Through Range Cell (MTRC). However, template database based on this assumption is always challenged by measured data. One reason is that speckle happens in the frame without scatterer MTRC. Speckle makes HRRP fluctuate sharply and not match well with the average HRRP. We precisely introduce the formation mechanism of speckle. Then, we make an insight into the principle of matching score. Based on the conclusion, we study the properties of matching score between speckled HRRP and the average HRRP. The theoretical analysis and Monte Carlo experimental results demonstrate that speckle makes HRRP not to match well with the average HRRP according to the energy ratio of speckled scatterers. On the assumption of ideal scattering centre model, speckled HRRP has a matching score less than 85% with the average HRRP if speckled scatterers occupy more than 50% energy of the target.

RADAR HRR PROFILING FOR GROUND MOVING TARGET USING PHASE-CODED AND HOPPED-FREQUENCY WAVEFORMS

To obtain the radar High Range Resolution (HRR) profile of the slowly moving ground target in strong clutter background, the Phase-Coded Hopped-Frequency (PCHF) waveform is proposed. By multiple-bursts coherent processing, the HRR profile synthesis, target velocity compensation and clutter compression can be accomplished simultaneously. The new waveform is shown to have good ability to suppress ground clutter and good Electronic Counter-CounterMeasures (ECCM) ability as well. The clutter compression performance of the proposed method is verified by the numerical results.

New statistical model for radar HRRP target recognition

The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers （VBMFA） model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen＇s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.

A New Radar Target Recognition Method Based on Polarimetric Processing and Neural Learning

The new millimeter-wave(MMW) radar target recognition method proposed uses polarmetric information to obtain stable amplitudes of range profiles and neural learning to extract angle-invariant features of range profiles and polarimetric processing reduces speckle to enhance ability to discriminate targets, and in comparison with conventional approaches, subclass features obtained by the neural learning carries more information and thus makes the correctness of target classification higher and simulation results vended the validity of this approach.

基于语义引导层次化分类的雷达地面目标HRRP识别方法

高分辨距离像(HRRP)反映了目标空间散射结构在雷达视线方向的投影,近年来被认为是地面目标识别的重要途径。现有的HRRP识别方法采用手工特征加传统机器学习分类器,均属于平面分类方法,即采用统一标准不加区别的优选特征并单次决策最终类别。然而该方法在实际应用中面临种类繁杂、数据不平衡、HRRP姿态敏感性等诸多问题,难以获取最佳的应用效果。层次化方法采取分而治之思想,将一个复杂的细粒度识别任务拆解为多个简单的识别子任务。本文采用层次化识别的思路,提出了一种基于语义引导层次化分类的雷达地面目标识别方法。该方法以联合语义和数据构建的树形结构将一个复杂的细粒度识别任务拆解为多个简单的识别子任务,并针对每一个识别子任务匹配一套优选特征集和一个局部分类器。本方法在仿真数据和实测数据上完成了验证。实验结果表明了本文方法处理地面目标识别任务的有效性。

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

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.

SAR图像辅助的雷达目标距离像检测识别

高分辨距离像在轻小型雷达平台探测识别目标中有重要应用,准确的目标和环境先验信息是提高距离像检测识别性能的关键因素。本文提出以离线或在线合成孔径雷达(synthetic aperture radar,SAR)图像作为先验信息来源,提取目标二维散射中心并向距离像视线方向投影获得一维散射中心特征模板,在此基础上设计了距离像检测、识别以及要害部位选择算法流程,采用电磁计算和外场实测地面目标数据进行了试验验证。结果表明:SAR图像辅助的距离像检测识别算法不仅具有较高的检测和识别率,在姿态、频段、分辨力等观测条件变化的情况下也具有较高的稳定性,具有较好的实用价值。

一种基于CAE的HRRP去噪重构与识别方法

针对HRRP识别研究中面临的噪声污染问题,提出了一种基于卷积自编码器(convolutional auto encoder,CAE)的HRRP识别方法。此方法将CAE的重构功能与卷积神经网络(convolutional neural network,CNN)的分类性能相结合,将未含噪声的数据作为标签,利用CAE学习含噪声HRRP的噪声特征,实现对HRRP的去噪重构,后利用CNN对重构后的HRRP进行识别。仿真实验表明:在10 dB、20 dB、40 dB峰值信噪比的噪声环境下,该方法对HRRP的识别准确率分别可达到76.48%、95.14%、98.33%,能够一定程度上克服噪声对HRRP识别带来的不良影响,保证识别精度。

基于角度引导Transformer融合网络的多站协同目标识别方法

多站协同雷达目标识别旨在利用多站信息的互补性提升识别性能。传统多站协同目标识别方法未直接考虑站间数据差异问题,且通常采用相对简单的融合策略,难以取得准确、稳健的识别性能。该文针对多站协同雷达高分辨距离像(HRRP)目标识别问题,提出了一种基于角度引导的Transformer融合网络。该网络以Transformer作为特征提取主体结构,提取单站HRRP的局部和全局特征。并在此基础上设计了3个新的辅助模块促进多站特征融合学习,角度引导模块、前级特征交互模块以及深层注意力特征融合模块。首先,角度引导模块使用目标方位角度对站间数据差异进行建模,强化了所提特征与多站视角的对应关系,提升了特征稳健性与一致性。其次,前级特征交互模块和深层注意力特征融合模块相结合的融合策略,实现了对各站特征的多阶段层次化融合。最后,基于实测数据模拟多站场景进行协同识别实验,结果表明所提方法能够有效地提升多站协同时的目标识别性能。

地面目标HRRP识别的稳健性特征选择方法

特征选择是雷达高分辨一维距离像目标识别的关键步骤,可降低特征维度,提高特征稳健性。提出一种基于散度的特征选择方法,采用该方法对适用于距离像地面目标识别的特征集合进行特征选择,得到优选的特征子集后再进入分类器网络进行识别。采用地面目标仿真数据和实测数据进行神经网络分类器识别实验。实验结果表明:在距离像信噪比、俯仰角和距离分辨力参数变化的情况下,基于散度的特征选择方法在基本保持或提升特征集的识别性能的前提下,能保持甚至提升识别的稳健性,具有较好的应用价值。

基于HRRP时频特征和多尺度非对称卷积神经网络的目标识别算法

针对空间目标识别中特征提取难、准确率低等问题,提出了一种基于雷达高分辨率距离像(high range resolution profile,HRRP)时频特征和多尺度非对称卷积神经网络的目标识别算法。采用离差标准化、多特显点绝对对齐消除目标的强度敏感性和平移敏感性,利用雷达多普勒测速数据消除目标高速运动对HRRP产生的展宽、畸变、波峰分裂等影响。对HRRP进行时频分析,提取其时频特征。通过不同尺度的非对称卷积,实现时频特征不同精细程度和不同方向的特征提取。实测数据处理结果表明,文中方法目标识别准确率高,而且在同平台目标识别、抗姿态敏感性等方面具有很好的效果。

基于一维距离像的目标识别方法研究

一维距离像可为雷达对目标的识别提供重要依据,频率捷变LFMCW雷达具有距离分辨率高,抗干扰性能强等优点,本文采用该体制雷达对典型坦克目标进行基于一维距离像的目标识别方法的研究。首先对目标一维距离像进行仿真,对比不同方位角、俯仰角下的一维距离像,提出了一种基于一维距离像的连续多调频周期融合的目标识别方法,仿真结果表明该方法相较于单周期目标识别方法的识别率更高,稳定性更好。最后就噪声调幅干扰对两种目标识别方法的影响进行了仿真分析,结果表明连续多调频周期融合的目标识别方法受噪声调幅干扰的影响较小,且识别率更高。

基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法

非合作目标识别常常面临少量不完备的训练样本、训练样本与测试样本信噪比不一致等现象,本文为此提出了一种基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法.该方法沿格拉斯曼流形中测地线积分提取不变特征,且通过核函数映射可获得解析特征提取表达式.该方法还可作为预处理手段对数据降噪,进一步提高其他算法的识别准确率.实验结果表明,对于信噪比失配和少量不完备样本等问题,该方法都具有鲁棒目标识别能力,并且满足实时性要求.

基于注意力机制的SRU模型雷达HRRP目标识别

针对传统目标识别方法难以提取雷达高分辨距离像(HRRP)的深层特征问题,提出了一种基于注意力机制的简单循环单元(SRU)的模型进行HRRP目标识别。该模型通过SRU快速提取HRRP时序特性,引入自注意力机制自适应对重要特征进行加权,增强隐藏层状态特征的表达能力。同时通过堆叠由SRU,自注意力机制和前馈神经网络组成的模块构建深层网络,对HRRP深层特征自动提取。实验结果表明,对比其他模型,该模型可以有效识别目标。在二维可视化下,提取的深层特征可分性最好。

雷达目标散射中心模型反演及其在识别中的应用

提出了一种基于散射中心模型的距离像识别方法。首先采用稀疏空间网格上的宽带测量数据离线反演目标散射中心模型,再将从距离像中实时提取的一维散射中心特征与该散射中心模型的一维投影进行匹配完成在线识别。在散射中心模型反演中,提出了稳定散射中心的概念并基于此完成稀疏空间网格下的一维散射中心投影关联。在识别过程中,通过设计合适的匹配函数解决散射中心参数估计误差和模型误差造成的散射中心数目、幅度和位置不完全匹配问题。仿真实验表明,对于精度较高的模型,基于模型的识别方法与基于距离像模板的方法识别率相当,而在存储量和灵活性方面优势突出。