共查询到19条相似文献,搜索用时 62 毫秒
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文章提出了一种带有容错机制的目标定位算法,算法以传感器节点观测结果0.1值为依据,通过一种似然估计实现定位。文章提出的算法能够获得较好的定位精度,并在一定的节点差错概率下,保持算法性能。 相似文献
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基于无线传感器网络的声目标定位算法研究 总被引:2,自引:0,他引:2
在建立声音能量模型基础上实现了基于极大似然估计的声目标定位。由于极大似然估计算法是一个有偏估计,而且容易受到参数扰动的影响,这两个不足影响了其定位精度的稳定性。因此,提出了基于最优化极大似然估计的目标定位算法。仿真实验结果表明,与一些存在的定位算法相比,此算法在传感器节点数目不同时,都能得到更高的定位精度。 相似文献
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一种降低定位误差的无线传感器网络节点定位改进算法 总被引:5,自引:0,他引:5
本文针对无线传感器网络节点的定位精度问题,提出了一种采用误差修正的方法来降低累积距离误差和定位误差的传感器网络节点定位改进算法,给出了该算法的基本原理与实现方法.该算法在不增加原算法通信量及计算复杂度的基础上提高了定位精度.仿真结果显示,在同等条件下,本文提出的算法定位精度提高了5~10%. 相似文献
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针对DV-Hop距算法定位误差大的难题,提出一种改进离估计误差,并利用DV-Hop的传感器节点定位算法。首先修正知节点与信标节DV-Hop算法对节点进行定位;然后对进V-Hop算法定位误差行校正,最后在Matlab 2012平台上对算法性能进行仿真分析。仿真结果表明,本文算法可以较好地克服DV-Hop算法存在的不足,提高了传感器节点的定位精度。 相似文献
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This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed,which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase,the optimal number of measurements was determined due to the sequential compressed sensing theory,and then the locations of the initial candidate grids were reconstructed by applying lp (0< p<1) optimization.In the adaptive grid-based localization phase,the initial candidate grids were adaptively partitioned according to the compressed sensing theory,and then the locations of the targets were precisely estimated by applying lpoptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing,the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore,it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks. 相似文献
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针对MSP算法需要借助额外的外部扫描设备,不适合应用于对野外大规模部署的传感器网络进行定位这一缺点,提出了一种HG-MSP算法。该算法通过锚节点发出扫描信息,不需要额外的外部设备进行辅助定位,提高了算法的可用性。仿真实验表明,在去掉辅助设备的情况下,算法的定位精度并无明显下降。 相似文献
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Qinghua Gao Jie Wang Minglu Jin Hongyang Chen Hongyu Wang 《Wireless Communications and Mobile Computing》2014,14(2):210-220
For realizing robust target tracking with wireless sensor networks in the circumstance where the propagation parameters of the characteristic signal emitted by the target are unknown, a novel tracking algorithm under the particle filter framework is proposed. We propose a scheme to realize particle weight calculation without the prior knowledge about the propagation parameters of the target's characteristic signal. With the use of the monotonic relationship of the distance and the received signal strength, we define the signal characteristic sequence and particle distance sequence and utilize the modified sequence distance between the signal characteristic sequence and the particle distance sequence as the criterion to calculate the particle weight blindly with simple lightweight operations. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献