Schemes for synthesizing high-resolution range profile with extended OFDM-MIMO

Two novel schemes are proposed to synthesize highresolution range profile(HRRP) based on co-located multiple-input multiple-output(MIMO) system in the context of the joint radar and communication system. The difference between two schemes is the pattern of selecting pulses, which depends on the demand for the velocity information. The system, a type of frequency diverse array(FDA), takes full advantage of the phase-coded orthogonal frequency division multiplexing(OFDM) signal. Furthermore, the complete discrete form of the phase-coded OFDM echoes is utilized to derive the HRRP processing. The velocity estimation in the second scheme aims to eliminate velocity ambiguity, and high velocity can be retrieved exactly. Meanwhile, the imaging method is investigated with random frequency coding applied to an array.The desired performance of resolving velocity ambiguity and suppressing noise is shown by means of comparisons with previous work. The advantages in the radar imaging and the significance of the work are concluded in the end.

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。

空中目标动态HRRP特征可重构模拟方法

针对目前无源模拟技术无法实现目标动态电磁特征模拟的问题,提出了一种利用相位调制表面(phase-switched screen, PSS)进行空中目标动态高分辨距离像(high resolution range profile, HRRP)特征可重构的模拟方法。以无人直升机旋翼为例,对旋翼的运动状态进行了建模,并详细分析了其动态HRRP特征。另外,通过建立PSS的周期调制信号模型,详细分析了PSS对雷达入射波的调控效果。在此基础上,提出了一种调制频率时变的PSS可重构模拟方法,该方法能够生成随时间变化的可控谐波分量,模拟产生的谐波分量与无人直升机旋翼的动态HRRP特征变化规律相似。通过仿真数据对比分析,调制频率时变的PSS可重构模拟方法能够实现无人直升机旋翼的动态HRRP特征模拟效果。

High resolution range profile analysis based on multicarrier phase-coded waveforms of OFDM radar

Orthogonal frequency division multiplexing(OFDM) radar with multicarrier phase-coded waveforms has been recently introduced to achieve high range resolution.The conventional method for obtaining the high resolution range profile(HRRP) is based on matched filters.A method of synthesizing HRRP based on the fast Fourier transform(FFT) and decoding is proposed.The mathematical expressions of HRRP are derived by assuming an elementary scenario of point-scattering targets.Based on the characteristic of OFDM multicarrier signals,it mainly analyzes the influence on HRRP exerted by several factors,such as velocity compensation errors,the sampling frequency offset,and so on.The conclusions are significant for the design of the OFDM imaging radar.Finally,the simulation results demonstrate the validity of the conclusions.

Length estimation of extended targets based on bistatic high resolution range profile

The approach to estimate the length of extended targets by using the bistatic high resolution range profile( H RRP) is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of extended targets are investigated. It is demonstrated by simulations that the target length measured by the bistatic H RRP is more meaningful and accurate than that by the monostatic HRRP,though the monostatic H RRP has been well developed and widely used in target recognizing and classification. The estimation results of a cone shaped target are present and compared at the end of the paper. To assure the reliability of the simulation,the bistatic H RRP is obtained through the scattering field data calculated by a fullwave numerical method,FE-BI-MLFMA.

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 multichannel noncoherent integration detection scheme where the integration number in each channel varies 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 integration 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 implementation.

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 group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

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。

基于角域特征PSO的海面目标HRRP识别方法

针对特征空间中各类海面目标特征混叠严重和高分辨距离像(high resolution range profile, HRRP)的角度特征利用率低的问题,提出了一种基于角域特征粒子群优化(particle swarm optimization, PSO)的海面目标HRRP识别方法。该方法引入HRRP的角度信息优化特征空间,增加特征空间的整体可分性;利用自适应分帧算法对特征空间进行角域划分,增加特征空间的局部可分性,并利用PSO算法确定特征空间角域划分时最优的单帧最小样本数目,增强方法的鲁棒性与适用性。实验结果表明,通过将特征空间优化和区域划分进行结合,可以有效提升多类海面目标的分类识别性能,PSO算法可以有效增强方法的抗误差性和抗噪鲁棒性。

宽带复合双基地雷达舰船目标长度估计方法

舰船目标长度特征对舰船目标识别具有重要作用,该文提出一种基于宽带复合双基地雷达联合观测的舰船目标长度估计方法。该方法综合利用单/双基地雷达高分辨距离像(HRRPs)长度,结合双基地雷达-舰船之间的空间关系求解,实现了单次测量估计舰船目标的实际长度,并分析了各种误差因素和几何构型条件下的估计误差。典型场景动态仿真证明了所提方法的有效性,结果表明在高分辨距离像长度误差小于5%条件下,舰船目标长度估计误差小于5%,为舰船目标特征提取与识别提供了新思路。

基于高分辨一维距离像及其特征的空间目标识别效果分析

目标高分辨一维距离像(High Resolution Range Profile,HRRP)中包含了丰富的目标尺寸、结构等目标特征,是进行空间目标身份识别的有效途径。但由于卫星宽带雷达实测数据获取难度大,前期相关研究多集中于基于HRRP的目标识别算法,结论也多是基于仿真数据和少量类别(几类)的前提下得到的,与工程应用实际情况有较大差距,工程指导意义有限。为解决这一问题,基于地基雷达获取的30类卫星目标的大量一维距离像实测数据,从识别正确率的角度对目标HRRP及其特征(组合)的可分性和在空间目标个体识别中的应用效果进行了量化分析,分析结果可为后续基于HRRP的空间目标个体识别技术研究和工程应用提供可靠依据。

基于快速密度搜索聚类算法的极化HRRP分类方法

该文针对人造目标的极化高分辨距离像,提出一种基于快速密度搜索聚类算法的分类方法。首先根据散射结构在频率和极化维度的特性,对散射中心的类型进行判别,在此基础上构造目标分类的特征矢量。然后采用快速密度搜索聚类算法,实现目标的分类。仿真实验结果表明,文中构建的特征矢量能较好地描述目标的结构属性,具有较强的可分性。而快速密度搜索聚类算法简单高效,在人造目标的分类识别中具有极大的应用潜力。

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H/A/α分解的全极化HRRP目标识别方法

针对现有基于H/A/α分解提取全极化高分辨率距离像(high range resolution profile,HRRP)特征的方法都没有考虑度量尺度对所提取特征性能影响的问题,提取了平均度量尺度下的特征子集,给出联合动态互信息概念用于选择最优平均度量尺度,并剔除特征子集中的冗余特征;在此基础上,结合Bagging和Boosting算法,提出一种宽带全极化雷达目标识别方法;最后在多类飞机目标HRRP样本集上验证了该方法的有效性。

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.

基于极化分集接收的HRRP欺骗干扰鉴别

有源宽带欺骗干扰能够生成虚假高分辨距离像(high resolution range profile,HRRP),严重影响成像雷达的目标特征提取与识别。为此,研究了基于极化分集接收的HRRP欺骗干扰鉴别方法。首先,建立了目标回波和欺骗干扰的正交极化宽带响应模型,分析了两者的极化相关特性差异;然后,利用正交极化HRRP之间的互相关系数作为鉴别量,提出了相应的鉴别算法;最后,利用飞机缩比模型及干扰机天线的暗室测量数据进行仿真实验。实验结果表明:在一定信噪比(signal-to-noise ratio,SNR)及鉴别门限条件下,正确鉴别概率将大于90%,验证了算法的有效性。

基于熵的自适应加权投票HRRP融合识别方法

基于不同分类器对同一样本分类能力不同,同一分类器对不同样本可分程度不同的思想,为不同样本赋予不同融合权重,提出了一种基于熵的自适应加权投票高分辨距离像(high range resolution profile,HRRP)融合识别方法。该方法将二分类相关向量机(relevance vector machine,RVM)扩展为多类分类RVM概率模型,并对不同HRRP特征样本进行分类,利用每个多类分类RVM输出的样本后验概率信息计算出的熵值自适应为各个样本赋予权重,使得不同分类器以及同一分类器对不同样本的决策占有不同的比重,熵值越大的样本赋予的融合权重越低,最后通过加权投票方法实现融合识别,得到目标的最终识别结果。仿真实验结果验证了所提方法的有效性。