共查询到19条相似文献,搜索用时 46 毫秒
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为解决传感器网络在空间目标分布式跟踪过程中的异步采样及通信延迟问题,该文提出一种异步分布式信息滤波算法(ADIF)。首先,局部传感器与相邻节点之间以一定的拓扑结构传递带采样时标的局部状态信息和量测信息,然后将收到的异步信息按时间排序,使用ADIF算法进行计算,分别对目标状态进行估计。该方法实现简单,传感器间通信的次数少,支持网络拓扑的实时变化,适用于空间目标监测中的多目标跟踪问题。该文分别对空间单目标、多目标跟踪进行了仿真,结果表明算法可以有效解决异步传感器滤波问题,分布式滤波精度一致逼近于集中式结果。
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本文从多传感器结构设计、融合跟踪算法两方面,进行了光电跟踪测量系统多传感器融合跟踪的设计与实现方法研究。设计了一套集可见光测量、红外测量和激光测量为一体的光电跟踪测量系统,实现了适应不同环境背景下的单站定位测量功能。 相似文献
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检测光电跟踪测量设备的激光模拟空间目标 总被引:1,自引:0,他引:1
为了在室内检测光电跟踪测量设备的跟踪性能和测量精度,提出了用激光光束模拟空间。与传统的光学旋转靶标相比具有结构简单、控制灵活、动态范围大,适应性强的优点;不仅可以完成静态指向精度检测,也可以实现动态性能检测,可以替代传统的光学靶标,实现对短焦距电视系统的性能指标检测。 相似文献
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主要利用检测前跟踪动态规划(DP-TBD)算法解决目标跟踪问题。动态规划(DP)是一种先通过对量测空间栅格化处理,然后对离散的量测空间中所有可能的物理路径进行遍历的算法。但是该算法提供的是一种未经滤波的点迹序列。此外,基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳,航迹丢失情况较严重,因此利用基于DP-TBD的多雷达协同探测势在必行。然而,由于DP-TBD算法没有状态误差协方差矩阵,导致无法将不同雷达的点迹序列进行基于各种融合准则的融合。另外,由于多个雷达不同的采样周期和通信时延,导致了各个雷达的数据是异步的。为了解决以上问题,文中提出了一种基于DP-TBD的分布式异步粒子滤波融合算法(DP-PFF)。该算法分为两步,第一步提出了一种适用于DP算法的粒子滤波方法;第二步是将不同雷达获得的异步状态估计转化为同步的并进行基于DCI准则的分布式融合。仿真结果说明,和单雷达相比,该算法显著提升了目标跟踪的性能。同时,该算法也减少了航迹丢失率并且可以显著提升系统的鲁棒性。 相似文献
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针对运动目标跟踪,介绍了一种光电跟踪角速度、角加速度计算方法,给出了控制系统拉格郎日多项式插值跟踪算法误差分析,提出的伺服参数计算方法与分析,可为控制系统优化设计提供数据参考. 相似文献
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目前,很多电力企业的电力通信设备仍然采用点对点方式进行输电传输,设备老化、稳定性差、可靠性低等问题给电力企业的正常运营带来了巨大挑战,同时又威胁到了电网的安全.本文在光电一体化技术基础上,提出了一种电力通信设备组网方案,设备数量少、组网方式灵活,可适用于县级供电企业. 相似文献
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Distributed fusion architectures and algorithms for target tracking 总被引:15,自引:0,他引:15
Liggins M.E. II Chee-Yee Chong Kadar I. Alford M.G. Vannicola V. Thomopoulos S. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1997,85(1):95-107
Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given. 相似文献
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一种基于多特征自适应融合的运动目标跟踪算法 总被引:3,自引:0,他引:3
针对复杂背景下的运动目标跟踪问题,提出了一种基于多特征自适应融合的运动目标跟踪算法。通过构建目标与背景的图像特征分布方差的比值函数来衡量目标与背景间的区分度,采用各特征的区分度对特征集进行线性加权自适应表示运动目标并集成在基于核的跟踪方法中。为了克服模板更新过程中的漂移,通过计算前后相邻两帧间目标模型的相似度函数,对跟踪模板进行自适应更新。基于生物视觉认知理论,目标的颜色、边缘特征以及纹理特征被用来实现基于多特征自适应融合的运动目标跟踪算法。仿真实验表明:采用本文算法能有效地对复杂背景下的运动目标进行跟踪。 相似文献
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The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking 相似文献
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《IEEE transactions on information theory / Professional Technical Group on Information Theory》1980,26(3):372-375
Casting phase tracking into a binary decoding problem, as in a previous paper, a sequential decoding algorithm is applied to a third-order system. The goal is to reduce the "tracking threshold" below that of a phase-locked loop. Simulation results are obtained by microprogramming the algorithm for a HP-2100A minicomputer. 相似文献
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一种快速的多目标跟踪非线性滤波算法 总被引:3,自引:3,他引:0
多机动目标跟踪问题是目前目标跟踪领域的一个重要研究方向,而数据关联与跟踪维持是多目标跟踪的核心部分。利用支持向量机在分类识别方面的优势,研究了基于支持向量机的数据关联方法。在此基础上,采用交互式多模型算法和无味卡尔曼滤波相结合的方法研究了多机动目标的跟踪问题。在该方法中,目标的运动状态和方位误差由选定的采样点来近似,在每个更新过程中,采样点随着状态方程传播并随非线性测量方程变换,得到目标的运动状态和方位误差的均值,避免了对非线性方程的线性化,至少给出最佳估计的二阶近似。与传统的扩展卡尔曼(EKF)方法进行了仿真比较,仿真结果表明了该算法的有效性。 相似文献
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In this paper, we address the problem of tracking a single ship in inland waterway closed circuit television (CCTV) video sequences given its location in the first frame and no other prior information. First, based on the compressive sensing theory, we employ two kinds of random measurement matrices to extract two complementary good features to track the target ship. Second, in order to track both location and scale, we construct our random measurement matrices according to spatial and temporal structure constraints in consecutive frames, which can be easily obtained and recorded in an offline manner. Having obtained the low-dimensional features in the compressed domain, we further take the different discriminability strengths of the extracted features into account and perform feature evaluations through their cumulative classification performances. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background and a coarse-to-fine strategy is adopted to speed up the time-consuming detection procedure. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm outperforms several state-of-the-art methods in terms of accuracy, precision and robustness 相似文献