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.

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.

High Range Resolution Profile Automatic Target Recognition Using Sparse Representation

Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.

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.

SAR-ATR系统复数对抗样本生成方法

针对现有对抗攻击方法只能用于攻击实数卷积神经网络这一限制,提出了一种基于生成对抗网络的复数对抗样本生成方法。首先,设计了一种产生有效对抗样本的复数模型,并引入了复数计算模块;其次,利用残差神经网络作为基本骨架,将预训练的复数网络作为判别器实现对抗训练,以增强对抗样本的攻击能力;最后,通过替代模型实现可迁移的对抗攻击,以此实现了更高的攻击成功率。实验结果表明,所提方法在有目标攻击和无目标攻击任务下的成功率分别达到了76.338%和87.841%,迁移的成功率更高且对抗样本与原始干净样本更为接近。所提方法将对抗攻击扩展到复数神经网络后,避免了合成孔径雷达目标信息和精度的丢失,为实际合成孔径雷达自动目标识别系统的安全性和鲁棒性提供了参考方案。

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.

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.

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。

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。

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。

A new feature extraction method using the amplitude fluctuation property of target HRRP for radar automatic target recognition

Due to the aspect sensitivity of high-resolution range profile(HRRP),traditional radar HRRP target recognition methods usually use average profile within some target-aspect region as the target-aspect template.Actually,the amplitude fluctuation property of target HRRP also represents some feature information of the target.Based on the scattering center model,a new feature extraction method using the amplitude fluctuation property of target HRRP is proposed in this paper.The weighted HRRP feature extracted by the new method can represent the scatterer distribution in every range cell,thereby it can describe the scattering property of the target better.The experimental results based on measured data show that the new feature extraction method can greatly improve recognition performances.

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。