Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm

This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR

Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.

Spin-image surface matching based target recognition in laser radar range imagery

We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar （Ladar） using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coeff^cient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one.

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 Target Recognition Approach to Projecting HRR Profiles onto Subspace

Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Takingthe methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Where-upon, a template library for target recognition is built by the projection of a class-mean vector made from the radar dataonto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and eachtemplate in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar dataarte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noisewhen SNR≥5dB.

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.

DE-JSMA:面向SAR-ATR模型的稀疏对抗攻击算法

DNN易受攻击的特点使得以智能算法为识别手段的SAR-ATR系统也存在一定脆弱性。为验证其脆弱性,结合SAR图像特征稀疏的特点,在显著图对抗攻击算法和差分进化算法基础上提出了DE-JSMA稀疏攻击算法,精确筛选出对模型推理结果影响较大的显著特征后,为显著特征优化出合适的特征值。为了更全面地验证攻击的有效性,构建了一种结合攻击成功率和对抗样本平均置信度的新指标Fc值。实验结果表明,在没有增加过多耗时,且保证高攻击成功率情况下,DE-JSMA将只能定向攻击的JSMA扩展到了非定向攻击场景,且在2种攻击场景下均实现了可靠性更高、稀疏性更优的稀疏对抗攻击,仅扰动0.31%与0.85%的像素即可达到100%与78.79%以上的非定向与定向攻击成功率。

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.

测量机器人ATR测量精度研究

分析了不同观测条件对测量机器人ATR功能测量精度的影响,建立了研究实验场,仅在距离、角度、时段、背景等观测条件发生变化时采集数据,总结了数据精度受观测条件的影响趋势。观测角度变化对ATR自动观测照准精度有影响,且随着角度增大而增大;距离对测量机器人ATR观测影响比较明显,距离越长误差越大,精度越低;白天观测好于夜间观测效果,为提高TM50测量机器人ATR功能的测量精度提供了参考依据。

基于ATR技术的短边精密点位测量研究

为提高短边精密点位测量的工作效率,本文尝试将TS30全站仪的ATR技术应用到短边精密点位坐标测量中,并通过测量实例对比分析了人工测量方法与使用ATR技术的自动观测法的测量精度。试验结果表明:采用ATR技术相较于传统方法可以大大缩短测量时间,2种方法得到的待测点位坐标结果接近,而且自动观测法的一测回测角中误差与三角网测角中误差略小于人工观测法。研究结果可为精密工程的快速测量提供参考借鉴。

基于专利数据的ATR技术融合关系预测研究

文中旨在实现ATR领域关键技术融合关系的发展趋势预测,并对基于机器学习的技术融合预测方法进行了改进,以提高预测精度。文中以全球ATR技术领域的专利数据为数据源,搭建了一种CNN与SVM相结合的深度学习模型来进行技术融合,预测未来趋势,并将CNN自动学习特征与SVM在非线性问题分类上的优势结合起来,预测到2025年ATR技术领域将出现融合概率较高的技术类别。模型在预测实验中最高能达到90%精度,较之现有研究有所提高。另外,对行业发展情况进行了描述和分析,预测结果显示,〈G06K,G06T〉,〈G06K,G06V〉,〈G01S,G06K〉等IPC对,在未来一段时间内出现技术融合的概率相对较高。

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。

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.

异构工业控制网络多源目标入侵自动识别研究

异构工业控制网络多源目标特征处在不断变化中,具有起伏性,导致入侵识别精准度较低。提出考虑数据特征实时变化的入侵自动识别方法,并应用到异构工业控制网络中。设定归一化入侵特征空间,将全部网络数据规范到该空间内,并根据最大值和最小值比对将超出范围数据重新作归一化处理。以时域矩阵偏度特征、峰度特征以及包络起伏度特征作为入侵特征提取类别,分别计算工业控制网络中三种特征数据大小。在此基础上,首先计算一组入侵数据样本在网络中的各项特征表现;然后将表现参数转换为聚类中心值,求解待识别目标与聚类中心间欧式距离;最后按照距离大小完成入侵目标自动识别。试验数据证明:该方法在工控网络中识别精准度较高,并可在多种网络攻击环境下实现精准识别。该方法具有较好的应用效果。

基于OpenMV的无人机目标跟踪系统

无人机在众多领域展现出巨大的应用潜力,其中作为关键技术之一,目标跟踪对于提升无人机自主作业能力至关重要。文章介绍一种无人机目标识别与跟踪系统。该系统基于OpenMV平台,利用阈值分割的图像处理算法,确保目标识别的高精度与鲁棒性。通过分析图像中特定色块的位置,计算相对于图像中心的偏移量,进而确定目标运动轨迹。所获取的偏移信息被实时传输至无人机控制系统,无人机通过PID控制器调节,自动调整飞行路径,对目标实施紧密跟踪。在实现过程中,通过优化算法参数和进行大量实验测试,确保系统的稳定、可靠运行。实验结果表明,该系统能准确识别目标色块,并实现对目标的稳定伴飞,为无人机在目标跟踪、智能监控等领域的应用提供有力支持。

A feature extraction method for synthetic aperture radar(SAR) automatic target recognition based on maximum interclass distance

Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.

Automatic target recognition method for inverse synthetic aperture sonar imaging

To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar（ISAS） imaging,a method for target recognition is proposed based on topology vector feature（TVF） of multiple highlights. Analysis of the projection relationship from 3 D space to 2 D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. A classification rate of 84.0% is reached.

自动目标识别(ATR)算法评估研究综述

自动目标识别(ATR)算法评估是ATR研究领域的一项关键技术,已成为近年来该领域的一个热门课题。从ATR算法评估的国内外研究现状开始,对其常用的评估指标、典型的评估方法等进行了较为全面的分析和综述,在此基础上,比较了各种指标的优缺点,讨论了典型方法的适用范围,给出了主要研究结论,并提出了需要进一步解决的问题。

ATR算法识别率的区间估计与样本量分析

针对雷达ATR性能评估中样本量的特点,从贝叶斯分析角度出发,选取最小长度准则对ATR算法的识别率进行区间估计,主要分析不同估计精度要求下的最小样本量。分析了识别率区间估计的影响因素,分别给出了无先验信息和两类典型有先验信息情况下的识别率区间估计方法和样本量确定准则,得到了识别率估计精度和所需最小样本量的关系。