卡尔曼滤波在无线传感器网络节点定位中的应用

为了减小无线传感器网络节点定位中节点测距误差和定位算法自身引入误差的积累对定位精度的影响,建立了适用于无线传感器网络的卡尔曼滤波模型.采用渐消自适应卡尔曼滤波对基于距离的初始定位算法进行求精,通过一跳节点间的相互制约,在全局范围对未知节点进行定位;基于卡尔曼滤波模型,对无线传感器网络的分布式算法和集中式算法的计算量进行了研究,得出计算量与节点数目的关系.研究结果表明,卡尔曼滤波能够有效提高节点的定位精度,尤其适用于网络节点密度小、信标节点比例低的情况.     

时间异步无线传感器网络的分布式目标跟踪

为了降低无线传感器网络在目标跟踪过程中的网络能耗,提出了一种时间异步条件下的分布式目标跟踪方法.首先,依据节点到目标的距离进行动态成簇,以跟踪簇为时间的计算单元,由簇头完成簇内跟踪时间计算及簇间贯序传递,然后引入并行粒子滤波(PPF)算法将粒子集分为多个子集,在子节点处并行采样、计算权重和重采样,最后,簇头节点收集各子节点上传的结果并完成目标的局部状态估计.仿真结果表明,PPF算法具有较好的跟踪精度,且相比于集中式粒子滤波(CPF)算法,可降低约38%的通信量.     

大规模组网的集中式基站休眠算法

分析了通过基站休眠降低无线通信网络能效的集中式算法和分布式算法的原理和性能,在此基础上,针对集中式休眠算法随着网络规模的增大其计算复杂度将会异常巨大的问题,提出了一种面向大规模通信网络的集中式分簇算法。该算法首先在时域上运用多目标均衡优化对休眠时间段进行划分,随后在空域上对基站进行合理的分簇,最后通过粒子群优化算法进行了休眠组合的确定。仿真结果表明,该算法的计算复杂度要低于其他集中式算法,并且性能衰减可以忽略不计,整体的休眠部署更加合理。     

水下网络移动节点分布式定位算法

随着水下网络系统的发展,水下移动节点的分布式定位技术的研究受到关注。提出了一种自组织、可容忍节点失效与测距误差、计算量和通信开销小的分布式水下节点定位算法,该算法利用自适应加权、循环三角组合测量法提高定位精度。对基于TOA测距的循环三角组合算法的定位精度进行了仿真,分析参考节点数对平均定位误差的影响,并与三边测量法的定位精度进行了比较。仿真结果表明,该算法具有较高的定位精度,对节点失效和测距误差的鲁棒性高,并且降低了水下网络对参考节点的密度要求。     

基于DV-Hop的无线传感器网络定位算法

针对无线传感器网络中DV-Hop经典算法定位精度低的不足,提出了一种改DV-Hop定位算法;改进后的算法对锚节点的选取进行了优化,并改进节点跳数,还采用平均计算方法计算节点平均每跳距离。仿真表明,改进的DV-Hop算法对未知节点的定位精度有显著提高。     

基于能耗均衡的水下传感器网络分簇路由算法

针对水下传感器网络能耗不均衡问题,提出一种能耗均衡的多跳非均匀分簇路由算法。算法在水下传感器网络非均匀分簇的基础上,通过改进节点簇头竞选的阈值计算方式,解决了网络后期簇头竞选阈值低导致的网络能耗激增;通过引入多跳路由选择公式,综合考虑节点剩余能量和链路能耗,延长网络生命周期。仿真表明,提出的算法生成簇头数目稳定,能耗较低,并且能有效延长水下传感器网络的生命周期。     

基于模糊图论的多跳WSN冗余节点部署算法

针对多跳无线传感器网络(WSN)的漏斗效应问题,提出了基于簇负载平衡的冗余节点部署算法(RNDA).RNDA采用分簇结构平衡簇内能耗,并根据各簇负载情况配置一定数量的冗余节点以平衡簇际能耗.该算法把节点下一跳选路概率作为边模糊权值引入模糊图论,提出了用于计算数据从源节点经m跳到达目的节点概率的到达率定理,为研究网络数据...     

基于能量优化模型的无线传感器网络分簇算法

考虑到无线传感器分簇网络中簇的规模、簇头数量和节点剩余能量是能量有效型分簇路由算法关注的重要指标,提出了一种基于能量优化模型(EOM)的分布式分簇算法——EOMC,该算法通过建立网络能耗优化模型,以最优簇头数构建分簇通信规模,并结合功率控制将候选簇头限制在一定宽度的选举环带,使得簇头分布均衡,同时兼顾到节点剩余能量进行分簇,以达到均衡节点能耗,延长网络生存期的目的。与低能耗自适应分簇分层(LEACH)协议的对比仿真的结果表明,该算法能够达到预期指标,算法的开销相对较小。     

基于时空滤波的无线传感器网络抗差节点定位算法

针对无线传感器网络的最小二乘定位算法抗差性的不足,提出了一种基于时空滤波(STF)的抗差性加权最小二乘(WLS)节点定位算法--STLS.该算法基于空间域滤波的数据一致性检测算法利用相邻节点间必须满足的几何约束关系,采用优化矩阵操作,剔除粗差邻居节点,其计算复杂度为多项式的平方.通过使用具有2步收敛特性的DFP算法,最小化目标代价函数,实现节点的快速定位.实验结果表明,在均匀网格拓扑或各向异性C型网格拓扑下,该算法均可有效识别和剔除测距低估粗差点,其定位精度明显优于未进行空间一致性检测的加权最小二乘定位算法,当网络平均连通度较低时,该优势表现得尤为明显.     

稀疏无线传感器网络移动节点定位算法

为解决稀疏无线传感器网络的现有MCB移动节点定位算法存在定位精度低和无法定位的节点的数量多的问题,对MCB算法进行了改进,进而提出了一种新的移动节点定位算法MCBP,该算法把已定位的节点作为其它未定位节点的参考点(准信标节点),利用准信标节点的位置信息参与后续定位过程,使更多的节点可以定位,并且提高了定位精度。仿真结果表明,MCBP移动节点定位算法比前人的类似算法的定位误差最大可减少22%,平均减少16%。无法定位的节点的数量最大可减少26%,平均减少12%。     

双耳低频高分辨快速精确定向算法

小型被动探测节点由于成本低、布放简单等原因在水声领域应用广泛。已有的被动声测向技术在这种条件下存在定向精度低、角度分辨力差等缺点。为了解决小孔径基阵的目标快速精确定向问题及多目标分辨问题,将人类听觉系统对低频声源的定向过程与高精度时延估计算法相结合,提出双耳低频高分辨快速精确定向算法。通过理论分析和仿真与湖试数据的处理,证明算法对小孔径阵在低采样率的情况下可以对多个目标的方位进行快速精确估计,并可获得各目标的频率信息,利用频率的不同可以分辨相同方位入射的不同声源。     

An efficient and robust localization scheme on massively distributed systems

A parallel localization scheme is presented to enable solution transfers between unstructured grids. The scheme relies on neighbor walks to reduce the number of candidate elements that are visited to find the enclosing element. An advancing front method efficiently allows a subset of nodes to efficiently sweep through the grid, progressively reducing search spaces. The algorithm is parallelized permitting solution transfers over arbitrary grid decompositions. A hierarchical localization process helps prevent the neighbor walk algorithm from failing when encountering the boundaries of a concave domain by localizing the boundaries before the interior of the domain is localized. Random selections of the next step interrupt cyclic loops that may occur during a neighbor walk. The complexity of the search algorithm is verified over parallel decompositions and is effectively independent of the number of partitions. Copyright © 2014 John Wiley & Sons, Ltd.     

水声传感器网络移动节点定位技术

针对水声传感器网络的移动节点定位问题,首先研究了基于距离测量值的多边定位方法(Multilateral Localization,ML);然后利用节点运动信息,提出采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)进行跟踪的方法;最后针对水下移动节点的测量值不同步问题,提出了修正扩展卡尔曼滤波(Modified Extend Kalman Filter,MEKF)以改进EKF的精度。仿真分析结果表明,MEKF的定位精度要好于EKF,而EKF和MEKF由于其用到了节点的运动信息,因此其定位精度要远好于ML。     

New fast density evolution method for low density parity-check codes using higher-order statistic

Density evolution (DE) is a technique for tracking the distribution of the log likelihood ratio (LLR) messages exchanged between the variable nodes and the check nodes in a bipartite graph. It is widely assumed that these distributions are close to Gaussian. However, in many scenarios, this assumption is not valid, for example, the case that the signal to noise ratio is low, or the degree of variable nodes exceeds a certain threshold. A new (suboptimal) method for DE algorithm in low-density parity-check codes is introduced. We provide a more accurate model for the distribution of message bits (as compared to Gaussian) through matching the first n statistical moments. An iterative message passing algorithm is proposed to compute these moments from the graphical representation of the underlying code. It shown that the proposed algorithm results in an improved estimate of the underlying EXIT chart as compared to using a Gaussian assumption. In this respect, the proposed method achieves a performance very close to that of the best earlier methods, while it offers a much lower complexity.     

基于Gabor脸和隐马尔可夫模型的人像识别算法

提出了基于Gabor小波变换和隐马尔可夫模型的人像识别算法。该算法先对人脸图像进行多分辨率的Gabor小波变换,采用主元分析法对每个结点进行降维,最后形成Gabor脸。把Gabor脸的每个特征结作为观测向量,对隐马尔可夫模型进行了训练,并把优化的模型参数用于人脸识别。实验结果表明,本文方法识别率高,复杂度较低,对部分遮挡的图像具有较大的容忍度。     

基于非对称往返测距的海洋无线传感网络节点定位算法

提出一种新的基于非对称往返测距的海洋无线传感网络节点定位(LMARR)算法,该算法利用节点间非对称的接收与发送测距信息的时间差,推算出节点间海水声速以及未知节点与其邻居参考节点之间的距离,将三维距离信息转换成二维,运用最小二乘法完成定位计算。与SWN和ARTL算法相比较,仿真结果表明:LMARR算法能有效地提高节点定位的精度,特别是在深度为20～120m海水声速持续变化的区域,定位精度比SWN算法提高了20%,比ARTL算法提高了33%;此外,LMARR算法还具有较高的稳定性。     

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.     

一种基于层次熵和SVM的轴承故障诊断方法

针对样本熵在分析时间序列复杂度上的局限,在样本熵和多尺度熵的基础上提出一种新的基于层次熵（Hierarchical Entropy）的滚动轴承故障特征提取方法。首先用层次分解方法对实验数据进行分解,然后采用分解节点上的样本熵值作为特征向量,利用支持向量机（support vector machine,简称SVM）对故障进行识别。实验数据分析结果验证该方法的有效性。