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%.

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.

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.

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 coefficient 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.

A Target Recognition Approach to Projecting HRR Profiles onto Subspace

A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixes are constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Taking the methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Whereupon, a template library for target recognition is built by the projection of a class-mean vector made from the radar data onto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and each template in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar data arte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noise when SNR≥5dB.

New statistical model for radar HRRP target recognition

The mixture of factor analyzers(MFA) can accurately describe high resolution range profile(HRRP) statistical characteristics.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 independent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model automatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.

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。

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。

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。

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。

TCA2003全站仪自动识别系统ATR的实测三维精度分析

简述智能型全站仪TCA2003的自动目标识别(ATR)功能及其二次开发的机载测量软件的特点,以琅琊山抽水蓄能电站大坝变形监测网应用ATR功能三维测量实例,基于对其测量平差成果的评定精度,分析ATR三维测量的精度,比较ATR测量与常规的人工测量的功效,并给出有益的结论,为同类工程的应用以及拓展ATR全站仪应用领域提供技术依据。

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的研究现状和发展趋势

本文概述了自动目标识别——ＡＴＲ领域的研究现状和采用的各种技术途径，并指出对该领域必须予以重视的一些发展趋势。

空中目标动态电磁散射数据仿真系统设计与实现

电磁散射数据是目标识别研究的基础,但由于试验测量成本高、可重复性差等问题,空中目标电磁散射实测数据十分有限。基于去遮挡的N点模型和电磁计算数据插值研究了空中目标动态电磁散射数据仿真方法,设计了空中目标动态电磁数据仿真系统,该系统将电磁散射仿真与实际飞行场景相结合,支持场景自定义和空中目标三维模型库扩展。分析了飞机和巡航导弹2类目标仿真数据及成像结果,结果验证了仿真方法正确性和系统有效性,可为研究实际场景下的空中目标识别提供支撑。

基于被动声呐音频信号的水中目标识别综述

基于被动声呐音频信号的水中目标识别是当前水下无人探测领域的重要技术难题,在军事和民用领域都应用广泛.本文从数据处理和识别方法两个层面系统阐述基于被动声呐信号进行水中目标识别的方法和流程.在数据处理方面,从基于被动声呐信号的水中目标识别基本流程、被动声呐音频信号分析的数理基础及其特征提取三个方面概述被动声呐信号处理的基本原理.在识别方法层面,全面分析基于机器学习算法的水中目标识别方法,并聚焦以深度学习算法为核心的水中目标识别研究.本文从有监督学习、无监督学习、自监督学习等多种学习范式对当前研究进展进行系统性的总结分析,并从算法的标签数据需求、鲁棒性、可扩展性与适应性等多个维度分析这些方法的优缺点.同时,还总结该领域中较为广泛使用的公开数据集,并分析公开数据集应具备的基本要素.最后,通过对水中目标识别过程的论述,总结目前基于被动声呐音频信号的水中目标自动识别算法存在的困难与挑战,并对该领域未来的发展方向进行展望.

基于灰色关联分析的ATR系统作战能力评估

针对ATR效能评估现有研究的不足方面,重点研究了ATR效能的概念,建立了作战能力指标体系和基于灰色关联分析法的作战能力评估模型,并对指标体系的合理性和评估方法的适用性进行了分析,解决了ATR效果评估所涉及的部分重要问题。

ATR系统评价中的因素作用测算方法及应用

针对自动目标识别(Automatic Target Recognition,ATR)系统评价中存在的性能建模手段难以有效分析因素变化对系统性能作用的情况,提出了一种新的因素作用测算方法。该方法根据工作条件下ATR实验结果所具有的面板数据特点,基于Malmquist指数对因素变化进行定量度量,无需对ATR系统的代价进行严格限定。求解过程中采用了非参数的DEA方法计算距离函数,避免了对ATR性能随因素变化趋势的模型假设,并通过引入AHP约束锥实现了多指标情况下的权重限制。最后,通过一个应用实例说明如何运用该评价方法实现因素作用的测算与分析。