共查询到19条相似文献,搜索用时 483 毫秒
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无线传感器网络中基于多维定标的定位算法通常采用最短路径代替距离矩阵中的未知项,会导致较大的定位误差。针对这一问题,提出一种基于距离矩阵重构的无线传感器网络多维定标定位算法DR-MDS。算法利用节点间的公共邻居信息对距离矩阵线性重构,计算距离矩阵中的未知项,然后对重构的距离矩阵运用双中心化并进行特征分解,从而求得网络坐标。由于算法能够更为准确的获得网络节点之间的空间相对关系,并充分利用其空间相关性计算节点相对坐标,可获得较好的定位效果。仿真结果表明,本文提出的DR-MDS算法与MDS-MAP、ISOMAP相比定位精度更高,误差范围更小。 相似文献
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基于超声波测距的定位技术以其精度高、范围广和性能稳定等优点,在无线传感器网络中广泛应用。为了实现较大范围的高精度定位,利用自主实现的超声波六元传感器阵列进行TDOA测距,并进行测距误差分析,然后采用基于测地距离的多维定标算法(Geodesic Distance MDS)进行无线传感器网络节点定位。在MATLAB平台下与Cricket采用的迭代式三边定位和AHLoS采用的多点定位算法进行对比仿真实验,结果表明Geodesic Distance MDS算法在不同网络规模和测距误差条件下均能够获得更高的定位精度和较小的定位误差。 相似文献
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基于大部分距离无关算法能以改善锚节点比例提高无线传感器网络定位精度,提出了一种引入虚拟节点的无线传感器网络极限学习机(ELM)定位算法.通过引入的虚拟节点,寻找合适的未知节点升级为次锚节点,以增加锚节点比例,提高了定位精度.将ELM应用于节点定位,有效提高了定位的速度和精度,并因其强大的泛化性能,为无线传感器网络节点定位提供了新的思路.仿真结果表明:引入ELM定位算法和虚拟节点,有效提高了定位精度. 相似文献
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研究无线传感器网络在位置信息不确定时,同时定位无线传感器网络节点并跟踪移动目标。利用RSSI测量节点对之间的距离,多维定标技术根据距离矩阵完成传感器网络的初始定位。估计与更新阶段提出了压缩EKF滤波确定传感器节点位置和目标位置。仿真结果显示:算法在较低的网络覆盖率下有较高的定位和跟踪准确度,在初始定位误差为5m时,节点和跟踪误差均小于3m,特别是在长距离的跟踪任务中有很好的精度和实时性。 相似文献
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HE QinBin CHEN FangYue CAI ShuiMing HAO JunJun & LIU ZengRong Institute of System Biology Shanghai University Shanghai China 《中国科学:信息科学(英文版)》2011,(5)
Node positioning is a fundamental problem in applications of wireless sensor networks (WSNs). In this paper, a new range-free algorithm, called spring swarm localization algorithm (SSLA), is proposed for positioning WSNs. To determine the locations of sensor nodes, the proposed algorithm uses network topology information and a small fraction of sensor nodes which know their locations. Numerical simulations show that high positioning accuracy can be obtained by using the algorithm. Some examples are given to... 相似文献
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Viet-Hung Dang Author VitaeViet-Duc Le Author Vitae Young-Koo Lee Author VitaeSungyoung LeeAuthor Vitae 《Journal of Parallel and Distributed Computing》2011,71(3):471-484
A great deal of research achievements on localization in wireless sensor networks (WSNs) has been obtained in recent years. Nevertheless, its interesting challenges in terms of cost-reduction, accuracy improvement, scalability, and distributed ability design have led to the development of a new algorithm, the Push-pull Estimation (PPE). In this algorithm, the differences between measurements and current calculated distances are modeled into forces, dragging the nodes close to their actual positions. Based on very few known-location sensors or beacons, PPE can pervasively estimate the coordinates of many unknown-location sensors. Each unknown-location sensor, with given pair-wise distances, could independently estimate its own position through remarkably uncomplicated calculations. Characteristics of the algorithm are examined through analyses and simulations to demonstrate that it has advantages over those of previous works in dealing with the above challenges. 相似文献
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为解决无线传感器网络中质心算法对锚节点密度要求较高和定位精度过度依赖锚节点分布的问题,提出了一种多节点协作迭代求精的WSNs加权质心定位算法.该算法采用加权质心估算初始坐标,以请求二跳锚节点的方式增加可用锚节点,由锚节点以多边测距方式估算待定位节点的实际坐标与估算坐标的差值,迭代调整估算坐标,提高定位精度.实验结果表明,与普通加权质心算法相比较,该算法具有更高的定位精度和定位覆盖度. 相似文献
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Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then utilized for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes. 相似文献
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节点位置是无线传感器网络应用不可缺少的信息。DV-HOP算法是一种常见的无线传感器网络节点自定位算法。标准DV-HOP算法在计算跳数时并未根据邻居节点间距离对跳数进行加权处理,导致当邻居节点间距离差别较大时算法定位精度低的问题。从RSSI的耗散模型可看出,RRSI可以作为距离的比征,提出一种基于RSSI的DV-HOP加权算法。该算法基于节点接收信标节点位置元组时的信号强度(RSSI)对邻居节点间跳数进行加权处理,将节点间的跳数与距离相关联。仿真实验结果证明该加权算法可大大提高定位精度。 相似文献
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无线传感器网络中锚节点分布情况在很大程度上影响未知节点定位的精度,但目前对均匀性的分析相对较少,针对这一问题,对锚节点分布与无线传感器网络定位算法性能之间的关系进行全面分析。首先提出了锚节点均匀分布的相关概念,并设计建立了相应的网络系统模型,然后对质心算法、DV-Hop算法、最小包容圆算法性能与锚节点分布之间的关系进行了仿真实验。结果表明:锚节点的分布情况对所有定位算法均有影响,其中质心算法对锚节点均匀性最敏感,DV-Hop算法次之,最小包容圆算法对锚节点均匀性最不敏感,分析结果对无线传感网络实际应用具有指导意义。 相似文献
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节点定位是无线传感器网络(WSNs)的关键技术之一.接收信号强度指示(RSSI)测距技术以其不需增加任何额外的硬件设备的特点在节点定位中得到广泛应用.为了提高定位精度,在RSSI测距的基础上,提出将粒子群优化算法( PSO)引入节点定位中.首先由RSSI测得未知节点与锚节点的距离,然后应用PSO算法计算出未知节点的估计... 相似文献