Model-based approach to the detection and classification of mines in sidescan sonar

This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis.     

基于字典学习的主动声呐目标分类方法

主动声呐目标分类在军事和民用方面都有重要的应用和价值。文章基于稀疏表示理论,结合K-奇异值分解和正交匹配追踪算法,提出一种基于学习字典的稀疏表示分类方法（Dictionary Learning Sparse Representation Classification,DLSRC）。首先,利用K-奇异值分解算法训练各个类别目标回波信号,得到带有目标特征信息的类别字典,类别字典对信号具有良好表征能力并且带有目标类别信息;然后,利用正交匹配追踪算法和各个类别字典稀疏分解测试信号,得到各个类别字典下的稀疏系数后重构信号;最后,根据各个重构信号与测试信号的匹配度判定类别,得到分类准确率。结果显示,200个测试数据在信噪比分别为-5、-3、6 dB时,DLSRC法的分类准确率分别达到87%、89%、95.5%。不同信噪比下基于学习字典稀疏表示分类方法的准确率均高于已有的支持向量机（Support Vector Machine,SVM）、K-最近邻（K-Nearest Neighbor,KNN）和柔性最大值分类器（SoftMax）等分类方法,具有较好的分类性能。     

Supervised target detection and classification by training on augmented reality data

A proof of concept for a model-less target detection and classification system for side-scan imagery is presented. The system is based on a supervised approach that uses augmented reality (AR) images for training computer added detection and classification (CAD/CAC) algorithms, which are then deployed on real data. The algorithms are able to generalise and detect real targets when trained on AR ones, with performances comparable with the state-of-the-art in CAD/CAC. To illustrate the approach, the focus is on one specific algorithm, which uses Bayesian decision and the novel, purpose-designed central filter feature extractors. Depending on how the training database is partitioned, the algorithm can be used either for detection or classification. Performance figures for these two modes of operation are presented, both for synthetic and real targets. Typical results show a detection rate of more that 95% and a false alarm rate of less than 5%. The proposed supervised approach can be directly applied to train and evaluate other learning algorithms and data representations. In fact, a most important aspect is that it enables the use of a wealth of legacy pattern recognition algorithms for the sonar CAD/CAC applications of target detection and target classification     

基于自组织神经网络的声学底质分类研究

研究利用多波束测深系统获取的反向散射强度数据,应用自组织(Self Organizing Map,简称SOM)神经网络分类方法实现了对海底泥、砂、砾石和基岩等底质类型的快速、有效识别。通过实验示例,将SOM神经网络的分类结果与传统海底地质取样获取的真实底质类型进行分析比较,表明该方法是可行和有效的。     

Generating B-Scans of the Environment With a Conventional Sonar

This paper examines the task of displaying more information about the environment using a conventional ranging sonar than is available in standard time-of-flight (TOF) maps. A conventional ranging sonar forms an environmental image that displays the information in the echo envelope, similar to a medical ultrasound image. The sonar performs rotational sector scans of simple objects and two complex environments containing various reflecting structures. In acquiring sonar data, we repeatedly reset the conventional sonar to generate a point process whose density relates to the echo amplitude. This point process is displayed as a grayscale image, called a brightness scan (B-scan), analogous to B-scans in medical ultrasound. We compare the information content of sonar B-scans to TOF maps for object classification and show B-scans to be richer. B-scan textures produced by rough surfaces and volumes containing random scatterers exhibit statistical invariance, similar to some organs within the body, suggesting the feasibility of automated classification. Image artifacts and means for their identification are discussed. The qualitative information present in sonar B-scans should lead to improved quantitative techniques for classifying objects.     

一种频分MIMO声呐的波达方向估计算法

为了使声呐阵列在有限的载体空间内获得较高的角度分辨率,设计了基于频率分集的多输入多输出（MultipleInput Multiple-Output,MIMO）声呐。该MIMO声呐采用N发M收的布阵方式,接收阵为M元均匀线阵,发射阵由N个阵元组成,且各发射阵元发射中心频率不同、包络相同的窄带信号。建立密集式频分MIMO声呐的回波模型,并以此模型为基础提出了波达方向估计算法方向-相位域多重信号分类（Direction and Phase Domain-Multiple Signal Classification,DPD-MUSIC）算法。仿真实验中以双频MIMO声呐为例,将频分MIMO声呐的DPD-MUSIC算法的估计性能与单输入多输出（Single-Input Multiple-Output,SIMO）声呐MUSIC算法的估计性能进行了对比。仿真结果表明,频分MIMO声呐利用DPD-MUSIC算法可以获得优于等接收阵元数SIMO声呐的角度分辨率和角度估计精度。     

以图层模型为特征的传统建筑数字资源库设计

目的探讨以图层模型为特征的传统建筑数字资源库设计的策略和方法,旨在为相关文化遗产的数字资源库建设提供新思路。方法深入分析资源库的用户需求,推导传统建筑数字资源库的建筑信息收录模型,将图层概念引入数字资源库的设计中,并通过实践案例验证。结论基于用户需求分析,提出具备"宏观层-中观层-微观层"广度和"背景领域-内涵领域"深度的传统建筑信息收录模型。基于对传统建筑信息收录模型的考量,设计以图层为特征的建筑信息分类和展示方式。传统建筑图层模型通过发挥其特性,可实现传统建筑的分层查看;可有序地连接物态信息词条库、行为信息词条库、心态信息词条库3个建筑内涵信息词条库;达到增强数字资源库信息分类的专业性和系统性,提升用户使用数字资源库的便利性和友好性的目的。     

多波束声呐海底底质半监督学习分类方法

多波束测深声呐的反向散射数据中包含海底表层的声学信息,可以用来进行海底表层底质分类。但实际中通过物理采样获得大范围的底质类型的标签信息所需成本过高,制约了传统监督分类算法的性能。针对实际应用中只拥有大量无标签数据和少量有标签数据的情况,文章提出了基于自动编码器预训练以及伪标签自训练的半监督学习底质分类算法。利用2018年和2019年两次同一海域实验采集的多波束测深声呐反向散射数据,对所提算法进行了验证。数据处理结果表明,相比仅利用有标签数据的监督分类算法,提出的半监督学习分类算法保证分类准确率的同时所需的有标签数据更少。自动编码器预训练的半监督学习分类方法在有标签样本数量极少时的准确率仍高于75%。     

干扰存在时被动声呐工作背景建模与分析

在实际应用中,声呐的工作背景除了各向同性背景噪声外,通常还会存在干扰。干扰存在时经典的被动声呐方程存在一定的局限性。在经典被动声呐方程的基础上,讨论了干扰对被动声呐工作背景的影响。提出了一种更加实用的工作背景模型,并详细分析了波束域的工作背景模型,应用此背景模型对经典被动声呐方程进行了修正,修正后的方程可用于干扰存在时被动声呐的性能预报。最后通过不同干噪比条件下的仿真验证了波束域工作背景模型的正确性。提出的工作背景模型可为被动声呐设备在干扰存在条件下的性能预报提供参考。     

声呐渗流测量数据降噪的分类模型研究

渗漏造成的一系列安全隐患己严重威胁到地下隐蔽工程的建设与正常运行.为了研发新的渗流测量手段与技术方法,减少控制渗漏事故的发生,提出了一种基于梯度提升树的声呐渗流检测结果分类模型.模型利用ReliefF算法选取贡献权重大的特征作为训练数据集,利用属性标注的数据集训练出区分水库渗流、井孔渗流与噪声的梯度提升树模型.实验结果...     

被动声纳目标识别系统设计研究

目标识别是声纳的主要功能之一,其性能包括识别正确率、泛化能力和识别距离。由于数据样本的保密特性,声纳目标识别系统设计有其自身特色。在设计过程中,应首先降低对数据样本的依赖,把目标辐射噪声的机理分析、组成、运动规律等知识巧妙地运用到系统设计中;其次还应尽可能提高泛化率,以提高声纳目标识别系统对未见过的样本的正确识别能力。讨论了声纳目标识别流程、声纳目标识别系统的设计方法;在声纳目标识别系统的性能评估中,给出了目标识别距离的估算方法。     

基于修正CycleGAN的声呐图像库构建方法研究

声呐图像数据集获取困难,导致很多水下工作无法正常开展,如水下目标检测与跟踪、声呐图像的超分辨等,因此构建充足的声呐图像数据库成为很多水下研究工作的重要前提条件。受光学图像与合成孔径雷达（Synthetic Aperture Radar,SAR）图像转换研究工作的启发,提出了基于CycleGAN实现声呐图像库的构建,即利用光学图像合成声呐图像,实现光学到声呐的图像风格迁移。通过对CycleGAN网络损失函数的改进,提高了声呐图像的合成效果。通过与Pix2Pix等图像风格迁移网络进行比较的实验结果证明,修正后的CycleGAN网络具有更好的图像风格迁移效果。最后用合成的声呐图像训练Mask RCNN目标检测网络,并用真实的声呐图像进行测试,训练得到的模型能够成功地检测出真实声呐图像中对应的目标,进一步验证了利用光学图像构建声呐图像库的有效性。     

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.     

Bistatic SAR ATR

With the present revival of interest in bistatic radar systems, research in that area has gained momentum. Given some of the strategic advantages for a bistatic configuration, and technological advances in the past few years, large-scale implementation of the bistatic systems is a scope for the near future. If the bistatic systems are to replace the monostatic systems (at least partially), then all the existing usages of a monostatic system should be manageable in a bistatic system. A detailed investigation of the possibilities of an automatic target recognition (ATR) facility in a bistatic radar system is presented. Because of the lack of data, experiments were carried out on simulated data. Still, the results are positive and make a positive case for the introduction of the bistatic configuration. First, it was found that, contrary to the popular expectation that the bistatic ATR performance might be substantially worse than the monostatic ATR performance, the bistatic ATR performed fairly well (though not better than the monostatic ATR). Second, the ATR performance does not deteriorate substantially with increasing bistatic angle. Last, the polarimetric data from bistatic scattering were found to have distinct information, contrary to expert opinions. Along with these results, suggestions were also made about how to stabilise the bistatic-ATR performance with changing bistatic angle. Finally, a new fast and robust ATR algorithm (developed in the present work) has been presented.     

Facial expression recognition based on spatio-temporal interest points for depth sequences

Of the numerous facial expression recognition methods previously proposed, most are based on texture frames or sequences. Recently, the development of depth sensors has raised new possibilities of dealing with 3D data. The proposed method extracts histograms of oriented gradient (HOG) and optical flow (HOF) for STIPs directly from depth sequences rather than involving registration/deformation techniques to find correspondence in 3D scan data. Mutual information score (MIS) and weighted matching score (WMS) are, respectively, calculated on the basis of naïve-Bayes mutual information maximization and constrained matching pairs. Finally, the MIS and WMS results are concatenated into feature vectors which are then fed into a support vector machine for facial expression classification. The proposed method is applied to the public BU-4DFE (Binghamton University 4D Facial Expression) database for six different facial expressions: anger, disgust, fear, happy, sad and surprise. Experimental results confirm that the proposed method is simple but effective.     

水下被动定位声纳显示及航迹跟踪方法

根据水下目标定位跟踪的系统功能,设计了用于水下目标被动定位跟踪的综合显示系统,能够实现信号的特征显示、轨迹跟踪处理和目标轨迹识别。采用B式瀑布图显示技术、目标航迹跟踪技术以及基于Hough变换的航迹信息提取技术,仿真分析和实验数据表明,该显示系统的设计可以提高被动定位跟踪综合显示性能。     

密集式MIMO声呐高分辨成像实验研究

为验证密集式多输入多输出(Multi-Input Multi-Output,MIMO)声呐高分辨成像算法的有效性,进行了水池实验并获得了期望的高分辨成像结果。首先,将成像算法分为角度维高分辨成像和距离维高分辨成像两种,分别建立了各自的成像处理流程。角度维高分辨成像基于虚拟阵列波束形成算法,距离维高分辨成像基于大带宽信号合成算法。然后,根据两种处理流程选择合适的发射信号和阵型,并据此搭建密集式MIMO声呐高分辨成像平台进行水池实验。最后,通过与传统单输入多输出(Single-Input Multi-Output,SIMO)声呐的成像结果进行对比,证明了MIMO声呐可结合发射信号、阵型和相应的处理流程获得更高的角度分辨率和距离分辨率。     

虚拟信源直接数据域高分辨算法

目标高分辨算法的性能优劣直接影响声纳、雷达的目标感知能力。考虑基于空间平滑思想的直接数据域算法,在干扰方向产生稳定的零陷。虚拟出期望信号并加入到接收数据中,将实际目标当作干扰,通过直接数据域自适应的方法在实际目标方向产生零陷：在波束图的左半部分适当位置虚拟一个期望信号,得到0°~90°的零陷分布图,在右半部分适当位置虚拟一个期望信号,得到90°~0°的零陷分布图,将这两部分零陷图组合后,取倒数得到实际功率谱图。该方法可以在单快拍、未知目标信号先验信息的情况下得到稳定、高分辨力的目标方位估计,并能够处理相干信号。仿真分析验证了算法的正确性和良好性能。     

Feature classification techniques in model-based object recognition

We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.     

基于干扰方位跟踪的自适应干扰抑制方法

针对复杂海洋环境中声呐探测弱目标易被强干扰淹没的问题,提出一种对被动声呐探测到的强干扰源进行跟踪抑制的方法,通过对观测数据的互谱密度矩阵(Cross Spectral Density Matrix,CSDM)进行特征分解,根据干扰方位范围的先验知识,对干扰源的方位进行跟踪,依据方位信息选择代表强干扰的特征向量,依此根据不同的算法重构剔除了干扰信息的CSDM。数值仿真和海试数据验证结果表明,该方法能够在已知干扰初始方位区域的情况下自适应地抑制强干扰,较好地保留并提取目标信息,检测出感兴趣的目标。该方法为后续的目标识别与跟踪提供了有利条件。