基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

基于联合分步APIT定位算法的改进

本文研究了无线传感网络( Wireless Sensor Network,WSNs)的节点定位问题,并针对APIT由于锚节点在低密度环境下的节点误判和节点失效等问题给出了改进,在APICT定位算法的基础提出了联合分步定位算法UNION-APICT(Union Approximate Point-In-Circumcircle Test),该算法是结合连通性的测距技术,RSSI测距技术以及质心定位和APICT等技术,来联合解决对未知节点定位问题。通过仿真实验结果表明,改进后的UNION-APICT在APICT算法的基础之上平均定位误差减少了10%-25%,定位性能有了明显的提升;随着通信半径R和最大探测距离rmax的增加,定位误差也在逐渐减小,该算法较APIT和APICT定位算法在锚节点密度、节点覆盖率和定位精度上都有所提高。     

基于四节点RSSI的三维空间定位算法

针对提高室内三维空间的定位精度和速度的问题,从减少节点数量、建立特定坐标系的角度提出了一种基于四节点和特定坐标系的三维空间定位算法。该算法引入位置相对固定的四个节点,提高解算速度;充分考虑有效反射区对衰减指数影响,根据伯鲁斯特角将天线辐射场区分为叠加加强区和叠加减弱区,建立三维坐标系进行定位,结合信号强度指示(Received Signal Strength Indicator, RSSI)衰减模型,在叠加减弱区定位,把三维空间问题转化到二维空间,降低定位算法的复杂度。文中分别阐述方法的原理与验证,结果表明,四节点定位算法的解算速度比降低复杂度(Complexity-Reduced Trilateration Approach , COLA)定位算法有较大提高,该算法平均定位误差小于2米。     

Exploiting perturbed and coalescent anchor node geometry with semidefinite relaxation for sensor network localization

Localization in wireless sensor networks (WSNs) suffer from performance issues whenever the anchor nodes (which are aware of their location) are subjected to motion from their usual position. Moreover, accurate localization demands more anchor nodes which is a scarce resource and needs to be used judiciously. In the current work, we propose a novel framework that addresses these two prime concerns by harnessing the inter relationship of anchor node geometry. For an unknown source node surrounded by anchor nodes, the anchors lying on the inner boundary of the deployment geometry may be carrying closely related information about source node, leading to redundancy and inefficient utilization. By anticipating the level of correlation between these anchors, localization can be made more frugal. Rigorous mathematical analysis is carried out to derive lower bounds on estimated locations. Based on fisher information from two proposed models, a convex estimation objective function is formulated using semidefinite programming (SDP) approach to validate the theoretical proceedings. Based on the findings, the proposed method is able to successfully extract useful information about the unknown source node location with limited number of anchor nodes, hence achieving superior localization.     

A Spherical Band-based DV-Hop localization technique for three-dimensional wireless sensor network

Wireless Sensor Networks have been the focal point of research for many years due to their wide range of application areas. Such networks consist of resource-constrained sensor nodes that are generally not equipped with any positioning component due to cost issues. This requires the adoption of suitable methodologies to infer the location of the deployed sensor nodes. Location information of such sensor nodes can be obtained with the help of some location-aware nodes. Numerous localization algorithms exist in the literature. Amongst them, Distance Vector Hop (DV-Hop) is a computationally less expensive algorithm that uses hop count values between sensor nodes and anchor nodes for location estimation of the deployed sensor nodes. However, the traditional DV-Hop algorithm produces a larger positioning deviation for a higher hop count value. Several existing works attempt to address this issue by either modifying the hop size or optimizing the estimated position resulting in comparatively higher localization errors and computationally expensive. This paper aims to solve the issue by modifying the hop size by dividing it into equal-sized spherical bands (SB). Sensor nodes use this SB value for computing their distances from anchor nodes and non-coplanar anchor nodes for location estimation. The simulation results demonstrate that the mean localization error of the proposed approach has reduced approximately by 75%, 66%, and 47% in comparison to traditional DV-Hop, 3D PSODV-Hop, and 3D GAIDV-Hop respectively.     

Optimal constant shape parameter for multiquadric based RBF-FD method

Radial basis functions (RBFs) have become a popular method for interpolation and solution of partial differential equations (PDEs). Many types of RBFs used in these problems contain a shape parameter, and there is much experimental evidence showing that accuracy strongly depends on the value of this shape parameter. In this paper, we focus on PDE problems solved with a multiquadric based RBF finite difference (RBF-FD) method. We propose an efficient algorithm to compute the optimal value of the shape parameter that minimizes the approximation error. The algorithm is based on analytical approximations to the local RBF-FD error derived in [1]. We show through several examples in 1D and 2D, both with structured and unstructured nodes, that very accurate solutions (compared to finite differences) can be achieved using the optimal value of the constant shape parameter.     

基于可靠性的相位去包裹算法

提出了一种新的基于可靠性的相位去包裹算法。采用了队列算法及去包裹可靠性的判别标准。与传统方法相比,能够自动避开噪声区域,解决了相位去包裹中的阴影遮挡以及空洞等问题,克服了传统去包裹方法中的误差传播效应,能准确快速地对全场进行相位去包裹。设计了一套基于此算法的三维形面测量系统。     

三维形貌柔性测量系统标定方法及验证

为了避免机器人模型误差对三维形貌柔性测量系统手眼标定的影响,对手眼关系的标定方法进行了研究。提出了一种融合特征点拟合的手眼标定方法。将三维形貌扫描仪安装在工业机器人末端搭建三维形貌柔性测量系统。标定时,首先利用激光跟踪仪对工业机器人末端法兰盘坐标系进行测量,得到两者转换关系;然后,利用三维形貌扫描仪和激光跟踪仪对空间固定的特征点组进行测量,利用特征点约束和基于罗德里格矩阵的算法求解两者转换关系即可间接地求解出手眼关系。基于ATOS三维扫描仪、安川HP20D机器人和API公司生产的激光跟踪仪进行了手眼标定实验,并进行了精度验证。结果表明:标定后的三维形貌柔性测量系统,其重复性测量精度(3σ)不超过0.1 mm,长度测量精度的均方根误差在0.2 mm以内,点云拼接精度优于±0.7 mm。该方法有效避免了传统手眼标定过程中会引入机器人模型误差的问题,在求解手眼关系解时采用了线性的解法,并且适用于三维形貌柔性测量系统。     

Linear sinusoidal phase-shifting method resistant to non-sinusoidal phase error

Non-sinusoidal phase error is common in structured light three-dimensional（3D）shape measurement system,thus we perform theoretical and experimental analyses of such error.The number of non-sinusoidal waveform errors in a 2πphase period is the same as the number of steps of the phase-shifting algorithm;no errors occur within the one-phase period.Based on our findings,a new structured light method,the linear sinusoidal phase-shifting method（LSPS）,that is resistant to non-sinusoidal phase error is proposed.Experiments show that the non-sinusoidal waveform error is reduced to an almost negligible level（0.001 rad） using the proposed LSPS.     

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.     

自适应在线模型校准的RSS实时定位算法*

针对传统的RSS定位方法对环境因素要求过高,在设计校准和运行操作阶段工作量大效率不高的问题,提出一种基于锚节点RSS在线建模校准的无线传感器网络定位算法。首先,利用标准偏差阈值方法对信号强度不确定度较大的锚节点进行过滤,并对锚节点和未知节点间的距离与接收信号强度的关系进行在线建模。其次,采用周期校准方式对在线模型进行实时修正,然后利用加权平均的方式对未知节点进行定位,建立对环境因素的自适应机制实现节点的实时准确定位。最后,通过仿真显示该方法能够有效对无线传感器网络未知节点进行定位且精度满足要求,算法简单易于实现具有实际应用价值。     

Linearized iterative method for determining effects of vessel attitude error on single-beacon localization

Single-beacon localization is an advanced (and relatively new) acoustic localization technique. The traditional single-beacon uses non-linear trajectories to localize underwater vessels, which has notable drawbacks in actual underwater operations. To mitigate the limitations associated with non-linear trajectories, we developed an innovative single-beacon localization with linear trajectories. The traditional linearized least square method is not suited to linear trajectories, so we developed a linearized iterative method that includes a virtual long baseline array to analyze the effects of vessel attitude error on single-beacon localization results. We simulated the forward velocity, number of beacons, distance measurement period, and vessel heading impact on horizontal localization. The results showed that according to the proposed analysis method, localization precision is strongly dependent on vessel attitude error.     

非合作线谱声源分布式定位方法

非合作目标定位是水声定位领域的研究热点。为充分挖掘并利用各类可观测参数,提高非合作目标定位能力,本文提出一种基于目标频率变化信息的非合作目标定位方法。该方法针对匀速直线运动目标的定位问题,首先根据多普勒频移原理,建立观测频率与目标辐射频率、目标运动速度、位置之间的函数映射关系,利用最小均方准则建立目标函数,通过优化算法估计分布式定位系统中各个测量单元与目标运动轨迹的致近点距离,最后综合各观测节点的测距结果,构建几何定位模型,求得目标运动轨迹的解析解。本文通过仿真分析,证明了该方法的有效性,并指出了该方法需要目标通过与各测量单元的致近点才能获得较好的距离估计能力。文中分析了不同频率估计精度对定位精度的影响,结果表明,提出的定位方法对测频精度具有一定的容限,是一种高精度的非合作线谱声源定位方法。     

回归的能源有效网络大数据流汇聚算法研究

为了降低传感器网络数据流汇聚时的能源消耗,提出了一种基于回归的能源有效数据流汇聚算法。首先,将传感器节点分为活跃节点和能源有效节点。然后,以活跃节点为中心点将所有节点进行聚类,并应用回归方法通过活跃节点的数据流对能源有效节点的数据进行预测。接下来,通过节点预测值的累积误差不断修正活跃节点集。最后,应用活跃节点的数据流信息对能源有效节点的数据进行预测。实验表明,本文提出的算法与其它相关算法相比具有更好的预测准确性。     

观测站非机动情况下的渐进无偏目标跟踪算法

针对无源目标跟踪中将非线性测量方程伪线性化带来有偏估计的缺点,提出了一种基于方位和径向速度联合估计的渐进无偏目标跟踪算法。该算法通过对方位-径向速度组成的伪线性测量方程的噪声协方差矩阵进行约束,使其均方误差达到最小,得到目标状态向量的最小二乘无偏解。仿真和实验结果表明,在观测站非机动情况下,算法能够渐进无偏地收敛到真值,收敛后的距离跟踪误差在5%以内。     

A three-step system calibration procedure with error compensation for 3D shape measurement

System calibration, which usually involves complicated and time-consuming procedures, is crucial for any three-dimensional （3D） shape measurement system based on vision. A novel improved method is proposed for accurate calibration of such a measurement system. The system accuracy is improved with considering the nonlinear measurement error created by the difference between the system model and real measurement environment. We use Levenberg-Marquardt optimization algorithm to compensate the error and get a good result. The improved method has a 50% improvement of re-projection accuracy compared with our previous method. The measurement accuracy is maintained well within 1.5% of the overall measurement depth range.     

分布式浮标阵水下高速运动声源三维被动定位

给出一种用于垂直爬升弹道三维坐标精确测量的分布式浮标网络超长基线宽带被动定位技术。考虑四个呈正方形布局的浮标节点,利用水平方位角交汇的定位方法测量上浮弹道的水平x、y坐标,水平方位角估计由灵巧的声矢量传感器替代常规阵得到,并给出定向误差小于0.4°的湖试结果;利用直达声和海面反射声之间的时延差提取弹道的垂直坐标z,并给出了高速运动目标垂直爬升弹道的海试测量结果,弹道测量误差小于10 m。      

Improvement of Delegated Proof of Stake Consensus Mechanism Based on Vague Set and Node Impact Factor

The Delegated Proof of Stake (DPoS) consensus mechanism uses the power of stakeholders to not only vote in a fair and democratic way to solve a consensus problem, but also reduce resource waste to a certain extent. However, the fixed number of member nodes and single voting type will affect the security of the whole system. In order to reduce the negative impact of the above problems, a new consensus algorithm based on vague set and node impact factors is proposed. We first use fuzzy values to calculate the ratings of all nodes and initially determine the number of agent nodes according to the preset threshold value. Then, we judge whether a secondary screening is needed. If needed, calculating the nodes’ impact factor based on their neighboring nodes, and combining their impact factors with adjacency votes to further distinguish the nodes with the same fuzzy value. In addition, we analyze the dynamic changes in the composition and scale of the agent node set and give its ideal size through testing. Finally, we compare the proposed algorithm with DPoS algorithm and existing fuzzy set-based algorithms in different scales and network structures. Results show that no matter in what kind of network structures, the effectiveness of the proposed algorithm is improved. Among which, the most noticeable improvement is seen in complex network structures.