无人车基于激光雷达/视觉的目标体积自动测量方法

近些年，基于激光雷达和视觉的目标感知在无人系统中得到了广泛应用。目标的体积测量在很多应用场景可以发挥极其重要的作用，然而对识别感知目标的体积测量，目前尚无大量研究。首次提出了一种基于激光雷达/视觉的无人车目标体积自动测量方法，实现了无人车与目标体积测量功能的结合。通过在LeGO-LOAM算法中加入点云畸变补偿，相较于原始LeGO-LOAM算法，无人车在高速情况下的构图精度得到提升；通过将激光雷达与视觉进行深度融合，实现了目标的自动识别与全局定位；通过基于平面拟合的地面分割与欧式聚类，实现了目标点云轮廓的实时获取；通过设计一种基于切片法的不规则物体体积测量方法，实现了无人车在运动情况下对目标体积的自动估计。最终，分别通过Gazebo仿真和实际试验验证了算法的有效性。试验结果表明，所提算法在无人车运动的情况下对静态目标物的实时体积测量精度优于3%，具有较好的工程应用价值。     

Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications, such as early warning and surveillance system, intelligent traffic system and so on. Through trajectory similarity measurement and clustering, target behavior patterns can be found from massive spatiotemporal trajectory data. In order to mine frequent behaviors of targets from complex historical trajectory data, a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper. Firstly, spatial–temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, which can distinguish the behaviors with similar location but different course and velocity. On this basis, by combining the idea of k-nearest neighbor and density peak clustering, a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribution. Finally, we implement the proposed algorithm in simulated and radar measured trajectory data respectively. The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately compared to the existing methods, which has a good application prospect in intelligent monitoring tasks.     

多无人机协同导航技术研究现状及进展

UAV是无人驾驶飞机(Unmanned Aerial Vehicle)的简称,无人机相较于有人驾驶飞机具有用途广泛、成本低和生存能力强等特点,在军事民用领域都有着广阔的前景,受到了国内外研究者的关注。协同导航技术极大地扩展了无人机的应用范围,提高了多无人机定位的精度以及无人机编队的稳定性、安全性和可靠性。现阶段协同导航技术在编队导航、目标监测与跟踪等诸多方面都得到了研究与应用。从四个层次对协同导航的研究现状和进展进行探讨:首先是对多无人机协同导航的概念、基本原理、国内外发展现状和必要性进行阐述;其次对协同导航中相对导航方式进行了分类分析,并分别介绍了无线电导航与视觉导航的原理、优缺点和应用场景;然后从导航目的、协同导航优化算法、滤波方法、协同导航信息融合容错策略几个方面对协同导航进行分析归纳;最后,讨论了基于多无人机协同导航未来的发展趋势。     

基于多源信息融合分析的 导弹贮存寿命评估方法

对于长期贮存的导弹,通常能获取的贮存信息仅为贮存期内的年检数据与少量的例行试验数据,贮存信息相对匮乏,对导弹贮存寿命与可靠性评估工作非常不利。为解决这种工程实践中存在的实际问题,发展了一种采用工程加速贮存与地面鉴定试验相结合的加速贮存信息获取方法,获取典型单机与简单系统贮存信息。将典型单机与非金属材料加速贮存信息与地面鉴定试验信息、现场贮存信息、贮存环境数据以及例行试验信息等数据信息融合处理,建立基于多源信息融合的贮存信息数据库。在此基础上,建立基于现场贮存信息与加速试验信息融合分析的导弹贮存寿命与可靠性评估方法,并给出导弹在现有贮存时间下,继续贮存一定年限的可靠度。     

基于多模特征融合的雷达干扰信号识别

传统制导雷达面临的新型有源干扰样式越来越复杂，雷达必须对各种干扰类型加以鉴别。传统的干扰识别方法仅对特定单一样式有效，通用性较差、泛化能力较弱，无法应对复杂多变的干扰对抗环境。因此，必须提出智能化更高、稳健性更强的普适干扰识别方法，提升制导武器抗干扰能力。为了提高干扰信号识别的准确率，研究了多模特征融合算法，并最终对时域、时频域、信息论特征进行融合以实现分类。首次将信息论中熵、相对熵、相对距离等概念引入到干扰信号分类这个应用场景中，通过仿真实验表明，能够有效对常见干扰进行有效识别，在较低干噪比下也有较好的识别准确率。     

腿臂融合型在轨装配机器人运动建模与步态规划

针对目前机器人在轨装配存在的作业效率低、空间操作受限、环境感知不完全等问题，提出一种腿臂融合型在轨装配机器人。首先，使用D-H法建立了腿臂融合型装配机器人运动学模型，并对其单个腿部进行正逆运动学分析，得到单个腿各个关节角和足端之间的运动关系；其次，分析了腿臂融合型装配机器人的行走步态和装配步态，行走步态中分析了二步态和三步态，装配步态中分析了单臂装配和双臂装配，使机器人达到平稳的行走和装配；最后，基于ROS(robot operating system)搭建仿真平台并对行走步态进行了仿真验证，结果表明提出的方法能够有效用于该机器人的行走。     

A clustering based method to complete frame of discernment

When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness.     

利用多尺度区域权重的红外可见光图像融合

针对红外、可见光图像融合，提出了一种利用多尺度区域权重的方法，获得既具有良好视觉效果又能保留和继承特征细节的融合结果。首先，通过改变平滑滤波的参数构建多尺度分解，获得一系列具有不同细节的双波段图层；其次，基于每一图层生成区域权重图，实现双波段图层融合;最后，利用适当的系数对融合后的图层进行合成，获得最终的融合结果。通过对比实验结果的主客观评价分析，证明本文方法能够产生最佳视觉效果的融合图像，能保留和突出原始双波段图像中的细节与特征，并能获得最佳的客观评价指标。     

An ECR-PCR rule for fusion of evidences defined on a non-exclusive framework of discernment

In the research of uncertain information processing, Dempster-Shafer Theory (DST) provides a framework for dealing with uncertain information, where evidence is defined on a Frame of Discernment (FOD) consisting of mutually exclusive elements. However, the requirement of exclusiveness on FOD sometimes is not satisfied, as shown in Dezert-Smarandache Theory(DSmT), a derivative of DST. In DSmT, the non-exclusiveness is expressed by propositions’ intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution (PCR) rule. In order to handle non-exclusive FODs, a new framework called D Number Theory (DNT) has been proposed recently, which quantifies the non-exclusive degree between propositions different from DSmT. In previous studies, an Exclusive Conflict Redistribution (ECR) rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD, but there are some deficiencies in the ECR rule. In this paper, a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a non-exclusive FOD. Within the proposed rule, the definition of conflict utilizes the idea of ECR’s exclusive conflict, and the disposal of conflict is following the idea of PCR’s proportional redistribution. Properties of the ECR-PCR rule are presented. The effectiveness of the proposed new rule is verified through numerical examples and applications, in comparison with other fusion methods.     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...