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.

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.

HRRP target recognition based on kernel joint discriminant analysis

With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.

RADAR HRRP RECOGNITION BASED ON THE MINIMUM KULLBACK-LEIBLER DISTANCE CRITERION

To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.

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.

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。

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。

一种新的雷达HRRP自适应划分角域建模方法

基于雷达方位渐变高分辨距离像(HRRP)的连续性,提出了一种自适应递归划分角域的建模方法,利用自适应高斯分类器和高斯过程分类器,从雷达数据中提取连续HRRP序列中包含的非线性结构信息;提出了一种判定角域边界的准则,递归地对雷达数据自适应划分角域.实测数据仿真试验证明了该方法优于传统的等间隔划分角域建模法.

加权KNN分类器在HRRP库外目标拒判中的应用

针对雷达自动目标识别中的库外目标拒判问题,提出了一种人工生成库外样本的方法和一种加权k最邻近(knearest neighbors,KNN)分类器。通过人工生成库外高分辨距离像样本,解决了在训练阶段无法获取库外样本的难题。加权KNN分类器同时满足了基于问题和基于数据两大设计要求,能够很好地处理拒判问题。通过基于接收机工作特性(receiver operating characteristic,ROC)准则和基于损失函数准则的仿真实验,证明了加权KNN分类器具备优良的拒判性能。

一种用于雷达HRRP功率谱的加权特征压缩方法

针对雷达高分辨距离像的平移不变特征———功率谱特征,提出了一种基于F isher判决率的加权特征压缩方法.该方法利用目标功率谱特征的F isher判决率迭代搜索最优权向量,并根据最优权值的大小对特征向量降维.与直接使用原始功率谱特征及基于F isher可分性判据的几种现有的特征压缩方法相比,加权特征压缩方法在降维的同时可提高识别性能,且运算简单,在基于外场实测数据的识别实验中对测试数据具有良好的稳健性.

一种利用强度信息的雷达HRRP自动目标识别方法

针对雷达高分辨距离像(HRRP)的幅度敏感性,现有HRRP雷达自动目标识别(RATR)方法一般简单地能量归一化.本文借鉴切空间距离的思想,提出一种利用HRRP回波强度信息提高识别精度的方法,实测数据仿真试验证明与传统方法相比,该方法可提高识别率约5个百分点.

基于匹配度的闪烁HRRP谱特征分析

在未发生散射点越距离单元走动(Moving Through Range Cell,MTRC)的角域内,闪烁现象是导致高分辨距离像(High Resolution Range Profile,HRRP)波动的主要原因。该文基于匹配度准则定量分析闪烁时HRRP常用谱特征(幅度谱、功率谱及高阶谱)的变化规律,比较各种谱特征受闪烁影响的强弱及原因,以获得定量的结论指导谱特征的选择。蒙特卡罗实验和暗室实测数据均表明:闪烁对HRRP谱特征的影响与闪烁散射点在目标能量中所占比重、闪烁区间的远近有关;除此之外,闪烁对高阶谱的影响还与高阶谱的阶数有关;常用谱特征中,功率谱受闪烁的影响最弱。

基于核主分量相关判别分析特征提取方法的目标HRRP识别

为有效提高雷达高分辨1维距离像目标识别系统的总体性能,需要对目标高分辨1维距离像进行特征提取,以得到具有最小信息损失、高可分性且低维度的目标特征,为实现该目的提出一种基于核主分量相关判别分析的特征提取算法。该算法基于目标高分辨1维距离像的统计特性,通过对核主分量分析中核函数的选择,实现对不同类型距离单元的特征提取。同时综合线性判别分析与典型相关分析理论构建新的准则函数,以实现特征空间中类内相关性与类间差异性最大化,同时减少目标特征中的冗余信息。利用实测数据进行实验,结果表明该方法提高了特征向量的可分性,降低了特征向量的维度,并且对该算法在不同强度杂波下的识别性能进行了分析,实验结果表明,该方法可以有效的提高目标高分辨1维距离像目标识别系统的总体性能。

基于复合特征及分层特征选择的雷达HRRP识别

雷达高分辨距离像(highresolutionrangeprofile,HRRP)具有方位敏感性、平移敏感性以及在特征空间高度交叠的特点,采用复合特征可以更好地描述目标特性。利用复合特征,结合分层识别结构提出了一种分层特征选择方法,充分利用了特征信息,简化了识别器结构,使识别运算量大大下降且提高了识别率。基于HRRP的平移不变特征和相关矢量机的计算机仿真实验表明,该方法是有效的。

雷达自动目标识别中的HRRP特征提取研究

通过分别考虑基于HRRP一阶统计特性的MCC-TMM(模板匹配分类器)和基于HRRP一阶和二阶统计特性的AGC(自适应高斯分类器),提出了两种新的特征提取方法,使得针对不同的分类器采用不同的特征提取方法,从而更好地滤除HRRP上的高斯噪声和更好地保持不同目标HRRP具有不同的幅度分布即HRRP的可分性。基于外场ISAR和微波暗室两种实测数据的识别实验表明,提出的特征提取方法能够明显提高识别性能。

基于AEPSO-SVM算法的雷达HRRP目标识别

针对雷达自动目标识别中的高分辨距离像(high resolution range profile,HRRP)识别问题,提出自适应进化粒子群(adaptive evolution particle swarm optimization,AEPSO)算法优化支持向量机(support vector machine,SVM)的目标分类识别方法。该算法利用非线性自适应惯性权重的调整以适应粒子寻优的非线性变化过程,采用分阶段调节加速因子增强粒子在进化过程中的学习能力,通过引入局部搜索算子在增加粒子多样性的同时有效避免了粒子陷入局部最优陷阱。通过改进的PSO算法优化SVM参数,建立分类识别器模型。将该AEPSO-SVM模型应用到雷达HRRP目标识别中,实验结果表明,该算法对于高分辨雷达目标识别精度高、鲁棒性强。

HRRP自动目标识别方法综述

雷达高分辨距离像自动目标识别是近年来雷达目标识别广受关注的一个研究领域.基于国内外HRRP自动目标识别方法的研究现状,指出目前研究工作中亟待解决的两个问题:非合作目标的识别与分类超平面的获取;针对待解决问题提出目前研究工作中的3个重点:信噪比失配问题的研究,少量不完备样本的研究以及分类超平面获取方法的研究;对每个研究重点的研究思路进行详细划分,包括移除噪声成分、特征提取以及支持向量等,并归纳每种思路的研究现状;最后,对3个重点的研究现状进行总结评述并指出未来的发展方向.

基于粗糙集和傅里叶-梅林变换的HRRP识别

一种有效的高分辨雷达距离像识别方法需要有效处理距离像表现出的不精确性和不确定性,这种不精确性和不确定性可用粗糙集理论描述。文中提出一种基于粗糙集理论和傅里叶-梅林变换的高分辨雷达距离像识别新方法。粗糙集理论能够挖掘目标类别相关信息,同时最大限度去除冗余信息;傅里叶-梅林变换具有时移与尺度伸缩不变性,能够克服距离单元的平移、伸缩给识别带来的困难。实验表明,该方法具有强大的特征压缩能力,所设计识别系统具有良好的识别率。

一种新的基于HRRP的雷达目标识别算法

雷达目标高分辨距离像(HRRP)包含目标结构信息,是目标重要的分类特征。提出基于独立分量分析(ICA)的雷达目标特征提取方法,将雷达目标高分辨距离像(HRRP)在"基信号"域中分解,提取相应的基系数组成向量,作为目标特征向量。采用反向传播(BP)神经网络作为识别系统的分类器,对神经网络的输出进行编码,为了克服分类器对非库属目标的误判问题,引入拒识门限设计一种新的分类器。采用电磁场时域有限差分(FDTD)算法仿真了飞机的宽带回波,并用所提的方法进行实验。结果表明,基于以上算法的雷达目标识别系统在最大拒识率前提下具有较高的正确识别概率。