共查询到20条相似文献,搜索用时 156 毫秒
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建立了一种对声纳和视觉图像进行融合的模型,提出了采用高斯方法和对水下环境进行描述建立融合地图的新的表达方法。首先假定传感器的观测信息为高斯分布,通过空间关系的变换和投影将声纳和视觉投影到公共的状态空间,然后对各传感器的其它信息进行加权,并嵌入到其中,得到适合计算机处理的传感器地图。提出了对水下机器人进行位置估计及地图匹配的算法,在导航过程中通过找出当前地图与参考地图的最大相关系数,从而对机器人位置进行更新,得出其最佳位置估计。仿真结果显示:采用融合地图对水下机器人的位置估计是连续的、可计算的、有效的。 相似文献
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针对稀疏型水声传感器网络定位算法面临的定位覆盖率低和误差高的问题, 本文提出一种水下机器人协
同控制的截角八面体(TO)模型区域划分定位算法. 首先搭建定位系统模型, 提出TO模型满足三维目标区域划分原
则, 并证明其体积比相对最优; 然后设计TO模型最优区域划分方式, 提出最小值判定法进一步整合目标节点, 自主
水下机器人(AUVs)协同控制筛选包含目标节点的子区域; 通过分析通信半径和虚拟锚节点数量对实验结果的影响,
设置最优定位参数, 降低能耗和定位误差, 最后利用最小二乘法完成定位. 本文分别对定位覆盖率、子区域AUV路
径长度和定位精度进行了仿真实验, 结果表明, 相比于其他区域划分方案, 所提算法误差较小、定位覆盖率高且鲁
棒性强. 相似文献
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由于子区域分割粒度的限制,基于阶次序列的定位算法(Sequence-based localization,SBL)存在边缘区域节点定位误差较大和不能保证平均定位误差性能的问题。针对这些问题,提出了一种基于SBL和APIT的混合定位算法,利用APIT信标三角形切割SBL算法子区域,减小子区域面积,实现了分割粒度的细化。通过预先进行系统训练,优化了混合算法的加权系数,进一步提升了算法的定位精度。仿真结果表明,相比于原算法,所提出的混合算法有效地提升了边界区域节点的定位精度,其平均定位误差降低了17.9%,使基于阶次序列的定位算法的定位精度得到了有效改善。 相似文献
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节点自定位技术是无线传感器网络的关键技术之一。三维序列重心算法利用锚节点两两之间的垂直平分面将定位空间分为边、面和体三类区域,缩小了未知节点可能存在的范围,并在所在范围内再次求出离未知节点最近三点组成的三角形的重心作为未知点位置的估计。该算法改善了二维序列算法误差较大的问题,且不需要增加硬件设施来实现特殊的功能。仿真结果表明,该算法可以达到较高的定位精度,能够满足三维空间中未知节点定位的应用需要。 相似文献
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Pritpal Dang Prasanna Ballal Frank L. Lewis Dan O. Popa 《Journal of Intelligent and Robotic Systems》2008,51(2):235-257
A novel approach for relative and absolute localization of wireless sensor nodes using a potential field method is presented.
The main idea of our work is to develop relative and absolute localization algorithms for the position estimate of stationary
unattended ground sensor (UGS) nodes using a potential field method. A dynamical model is derived for each sensor node to
estimate the relative and absolute position estimates under the influence of a certain fictitious virtual force. In the algorithm
the sensor nodes do not move physically, but a virtual motion is carried out to generate optimal position estimates. The convergence
of the estimator system to a least squares solution is guaranteed using Lyapunov theory. Separate control algorithms for relative
and absolute localization are developed which guarantee the convergence of the position estimates. The relative localization
algorithm assumes that distance (i.e. range) measurements between UGS nodes are available and for absolute localization algorithm,
uninhabited aerial vehicles (UAV) are available with on board GPS such that they have absolute position information together
with range measurement information. In the relative localization algorithm the UGS nodes are localized with respect to an
internal co-ordinate frame. In absolute localization the UGS nodes are localized with respect to the known absolute position
of UAV in the air–ground network. The effectiveness of the control algorithm is highlighted by the real time implementation
results. 相似文献
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建立加性高斯白噪声的线性数学模型,针对此模型对基于稀疏贝叶斯学习的消息传递算法进行研究。对传统的因子图通过添加额外的硬约束节点得到改进的因子图,然后在改进的因子图中利用联合BP-MF规则,提出低复杂度的BP-MF SBL算法。为了进一步降低复杂度,在BP-MF SBL的基础上提出近似BP-MF SBL算法。仿真结果表明与向量形式的MF算法相比,所提方法复杂度低,且性能有所提升;与标量形式的MF算法相比,在复杂度相似的情况下,所提方法的性能更好。 相似文献
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To overcome the shortcomings of existing robot localization sensors, such as low accuracy and poor robustness, a high precision visual localization system based on infrared-reflective artificial markers is designed and illustrated in detail in this paper. First, the hardware system of the localization sensor is developed. Secondly, we design a novel kind of infrared-reflective artificial marker whose characteristics can be extracted by the acquisition and processing of the infrared image. In addition, a confidence calculation method for marker identification is proposed to obtain the probabilistic localization results. Finally, the autonomous localization of the robot is achieved by calculating the relative pose relation between the robot and the artificial marker based on the perspective-3-point (P3P) visual localization algorithm. Numerous experiments and practical applications show that the designed localization sensor system is immune to the interferences of the illumination and observation angle changes. The precision of the sensor is ±1.94 cm for position localization and ±1.64? for angle localization. Therefore, it satisfies perfectly the requirements of localization precision for an indoor mobile robot. 相似文献
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在多站无源均值定位算法中,为了解决部分传感器间夹角过大或过小所导致的定位精度下降问题,提出一种基于虚拟量测变换的多传感器管理无源定位算法.首先在全局坐标系下分析了传感器间夹角对误差几何稀释度(GDOP)的影响,进而得到双站获得较好定位精度的夹角约束关系;其次针对不满足该约束关系的传感器组合提出一种虚拟量测变换定位算法,通过空间管理的方法达到对传感器的优化布站,并结合算法的实施步骤对其原理及特点进行了理论分析,尤其对变换前后的交点精度进行了比较.仿真结果表明虚拟量测算法的定位精度要明显优于均值算法,进而说明该算法的有效性及传感器管理在多站无源定位中的重要作用. 相似文献
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基于DV-Hop定位算法和RSSI测距技术的定位系统 总被引:4,自引:1,他引:4
针对 DV Hop算法在实验环境中存在的问题,加入接收信号强度指示器(RSSI)测距模块辅助定位,对算法进行改进。为了实现定位系统,首先,需要建立当前实验环境的RSSI模型;然后,应用该模型,从锚节点和非锚节点两方面分别控制DV Hop定位过程。实验证明:改进后的定位系统在增加少量计算复杂度的情况下,改善了系统的稳定性,提高了定位的精度,可以被应用到无线传感器网络中。 相似文献
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Hüseyin Akcan Vassil Kriakov Hervé Brönnimann Alex Delis 《Journal of Parallel and Distributed Computing》2010
A critical problem in mobile ad hoc wireless sensor networks is each node’s awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions. 相似文献