基于接近觉的水下多目标定位系统

研制了一种弧形阵列接近觉传感器,实现对水下多目标的定位.考虑到作业环境的影响,在3个探头轮流探测一次的基础上,对接近觉传感器设计了可编程控制的单探头多脉冲的探测模式,增加了采集的数据量,同时测距的精度得到了提高.在单个探测点进行基于区域划分的目标定位基础上,本文提出了多点探测结果关联匹配的估计方法,用于接近觉对水下多目标进行定位.大量水池实验表明,接近觉定位系统可靠性好,精度高,便于工程实现.     

电容接近觉传感器的研究

提出一和中双极板式电容传感器,其利用静电感应原理,当障碍物或工件靠近传感器时,电场发生变化,使输出电压变化,从而得到待测物体的接近度。文中对其原理作了详尽分析与讨论,并给出了初步实验结果。     

基于神经网络的接近觉目标定位方法研究

针对水下声传感器的多目标定位问题,根据水下超声传播与反射原理研制了一种新型接近觉传感器。详细介绍了水下接近觉的工作原理及组成,并对基于此传感器的多目标定位进行了仿真研究;与目前国内外所采用的几何相交定位技术不同,所采用的BP神经网络定位方法突破了目前方法中定位目标数目的限制,通过区域定位和角度精确定位的两类仿真实验表明了所采用方法的有效性及定位精度。     

机器人超声接近觉传感器的研究

根据机器人超声接近觉的实际要求选择了超声探头,研制了相应的模拟控制电路和数字控制电路,并根据机器人控制系统结构的需要设计了相应的信号采集电路部分,对机器人超声接近觉传感器进行了某些研究.进行的校准后所作的性能测试已经表明,该接近觉传感器可以满足机械手控制的实际需要。     

机器人红外接近觉、光纤触觉和握力觉传感器

设计和研制了红外接近党传感器以探测物体的存在；设计和研制了机器人触须探测物体的触觉和模拟机械爪抓取物体的握力觉传感器。单片机系统控制传感器数据采集和初步判断，为机器人控制和驱动提供感觉信息。     

一种新型双极板电容接近觉传感器

介绍了一种新型双极板电容接近觉传感器，该传感器不仅能检测导体，而且能检测非导体对象的接近度．对该传感器的原理、结构及检测方法作了研究和讨论，并给出了初步实验结果及分析．     

基于水下传感器网络的目标跟踪技术研究现状与展望

水下目标跟踪在海洋资源的开发利用以及国家安全的防御等方面都具有广泛的应用价值和重要的战略意义. 基于水下传感器网络(Underwater sensor networks, USNs)的目标跟踪技术凭借其覆盖范围广、观测时间长和实时融合等优势已经成为一个新的研究热点. 本文针对基于USNs的目标跟踪关键技术的基本思想、研究进展、应用及局限性进行了综述, 主要从以下几个角度对其展开论述: USNs的建设现状、系统组成及其分类、目标跟踪系统模型、单目标跟踪技术、多目标跟踪技术以及能效优化措施. 最后, 本文不仅指出了基于USNs的目标跟踪研究目前存在的主要挑战, 并对该领域的未来发展方向进行了展望.     

如何选择和使用接近觉传感器

植入式智能已经拓宽了传感器的使用范围。这样一来。只有少数的电感、光电、激光和其它传感器能够应用到更多的领域，如包装业。在这个怡乐瞢利包无菌填充机里。一个旋转心轴从进水线抓取纸盒；指示流量计OG光电传感器（位于底部、中间和P．40）探测纸盒正确的填充高度。     

Distributed extended object tracking information filter over sensor networks

This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

识别水下物体材质的触觉传感器的研制

提出了一种用于识别水下物体材质的触觉传感器，介绍了该传感器的原理，结构和识别水下物体的方法，用研制出的水下触觉传感器系统对十种物体（铜，覆铜板，油石，大理石，陶瓷，玻璃，皮革，胶木，塑料和泡沫塑料）作了七百余次识别实验，结果表明，识别率≥９２％，识别时间≤５ｓ）。     

Moving object detection using region tracking

For many vision-based systems, it is important to detect a moving object automatically. The region-based motion estimation method is popular for automatic moving object detection. The region-based method has several advantages in that it is robust to noise and variations in illumination. However, there is a critical problem in that there exists an occlusion problem which is caused by the movement of the object. The occlusion problem results in an incorrect motion estimation and faulty detection of moving objects. When there are occlusion regions, the motion vector is not correctly estimated. That is, a stationary background in the occluded region can be classified as a moving object.In order to overcome this occlusion problem, a new occlusion detection algorithm is proposed. The proposed occlusion detection algorithm is motivated by the assumption that the distribution of the error histogram of the occlusion region is different from that of the nonocclusion region. The proposed algorithm uses the mean and variance values to decide whether an occlusion has occurred in the region. Therefore, the proposed occlusion detection and motion estimation scheme detects the moving regions and estimates the new motion vector, while avoiding misdetection caused by the occlusion problem. The experimental results for several video sequences demonstrate the robustness of the proposed approach to the occlusion problem.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

时空运动显著性的目标跟踪

目的 在目标跟踪过程中,运动信息可以预测目标位置,忽视目标的运动信息或者对其运动方式的建模与实际差异较大,均可能导致跟踪失败。针对此问题,考虑到视觉显著性具有将注意快速指向感兴趣目标的特点,将其引入目标跟踪中,提出一种基于时空运动显著性的目标跟踪算法。方法 首先,依据大脑视皮层对运动信息的层次处理机制,建立一种自底向上的时空运动显著性计算模型,即通过3D时空滤波器完成对运动信号的底层编码、最大化汇集算子完成运动特征的局部编码;利用视频前后帧之间的时间关联性,通过时空运动特征的差分完成运动信息的显著性度量,形成时空运动显著图。其次,在粒子滤波基本框架之下,将时空运动显著图与颜色直方图相结合,来衡量不同预测状态与观测状态之间的相关性,从而确定目标的状态,实现目标跟踪。结果 与其他跟踪方法相比,本文方法能够提高目标跟踪的中心位置误差、精度和成功率等指标;在光照变化、背景杂乱、运动模糊、部分遮挡及形变等干扰因素下,仍能够稳定地跟踪目标。此外,将时空运动显著性融入其他跟踪方法,能够改善跟踪效果,进一步验证了运动显著性对于运动目标跟踪的有效性。结论 时空运动显著性可以有效度量目标的运动信息,增强运动显著的目标区域,抑制干扰区域,从而提升跟踪性能。     

一种改进的粒子滤波目标跟踪算法*

传统的Condensation跟踪算法使用状态转移分布作为采样粒子的建议分布函数,没有考虑当前的观测值,大量的粒子运算浪费在了那些具有小似然性的区域。针对该问题,提出一种基于Mean Shift以改进建议分布函数的粒子滤波跟踪方法。实验表明,由于有效地利用了当前观测值,改进的算法具有较强的鲁棒性和实时性。     

改进的粒子滤波器目标跟踪方法

针对现有的粒子滤波跟踪方法存在的不足,提出了一种改进的粒子滤波器方法用于运动目标跟踪.将颜色直方图和边缘直方图结合起来建立目标的参考模型,有效地克服了使用单一特征建模的缺点,提高了跟踪的准确性.分别计算目标颜色模型和目标边缘模型与粒子的欧几里德距离,使用这2个距离作为粒子权值计算的重要依据.实验结果表明该算法具有较高的实时性、准确性和鲁棒性.     

Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.     

基于自适应分块的视频目标跟踪方法研究*

针对目标跟踪中的遮挡问题,提出了一种基于颜色特征的自适应跟踪算法。该算法利用模糊K均值聚类将目标自适应分块,采用单调递减的核函数对子块进行加权,目标模型的更新只需更新子块的颜色均值,计算量小。在跟踪过程采用目标整体匹配的方法,结合设计的自适应Kalman滤波器,有效地解决了跟踪过程中的目标遮挡问题。实验结果表明,新算法能够实现目标的准确跟踪。