Distributed Node Localization Algorithm Based on MDS and SDP

提出一种新的节点定位算法,基于MDS和SDP的分布式节点定位算法(DLMS).该算法的主要思想是将整个无线传感器网络划分成若干个局部定位区域,在每个局部定位区域选择MDS定位算法或SDP定位算法建立一个局部相对定位图,然后将所有的局部相对定位图合并成全局相对定位图,最后利用锚节点的信息得到节点的估计位置.实验仿真结果表明,该算法能够提高节点的定位精度,尤其是在节点分布不均匀的情况下,同时该算法还能够节约锚节点数量.     

基于几何学的无线传感器网络定位算法

提出一种基于几何学的无线传感器网络(WSN)定位算法。把网络区域中的节点分为锚节点和未知节点,假设在定位空间中有n个锚节点,由于受到几何学的限制,实际可行的锚节点序列是有限的,因此利用一种几何方法判断锚节点间的位置关系,从而选取最优的锚节点序列,能够更精确地确定未知节点的位置,并且分析了待定位节点的邻居锚节点数量对定位精度的影响。仿真结果表明,与已有的APS(Ad-Hoc positioning system)定位算法相比,该算法可有效地降低平均定位误差和提高定位覆盖度。     

基于最小包含圆的无线传感器网络定位算法

提出一种新的无需测距定位算法--基于最小包含圆的定位(SECL).该算法根据目标周围的锚节点所决定的最小包含圆来估计目标位置.基于最小包含圆的算法考虑的不是坐标系中所有锚节点位置的平均值,而是覆盖所有锚节点区域的几何中心,能够有效地控制锚节点分布不均匀给定位带来的负面影响.仿真结果显示,相对于质心算法平均定位精度能提高10%以上.SECL在锚节点拓扑不均匀情况下,精度提升更高.     

三维无线传感器网络节点的序列垂心定位

针对无线传感器网络的节点自定位技术提出了一种三维序列垂心算法,该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垂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。     

基于局部分布式优化的无线传感器网络定位算法

针对在不规则的无线传感器网络中锚节点分布稀疏,对某些节点定位造成较大误差的情况,在Anchor Selection算法的基础上提出一种局部分布式优化定位算法,采用先局部后整体的定位思想,通过几何关系优化对节点之间跳距的处理,并在定位过程中将合理估计出来的节点晋升为锚节点,为后续未知节点的定位带来更多的信息参考。实验结果表明,该算法在处理不规则网络时有较强的容错性,能得到较好的定位效果。     

三点定位估计算法在WSN定位中的优化研究

在WSN中通常使用RSSI进行节点定位,在大部分研究中存在所假设的锚节点的移动轨迹相对理想化等问题。为了提高定位精度,文中提出了三点定位估计算法,设定初始时锚节点以随机角度做直线运动,任意未知节点在锚节点转向之前,随机选取两组RSSI值进行一次坐标计算,转向之后,每收到一组RSSI值就对坐标计算结果进行一次确定,最后对每次的确定结果进行加权估计,最新的确定结果所占权重最大。对比常见的PI算法和DuRT算法进行仿真分析,在锚节点以随机角度作直线运动轨迹情况下,三点定位估计算法在收敛速度表现较好的情况下,能够得出相对更高的定位精度。     

无线传感器网络中基于锚节点功率调节的加权质心定位算法

提出了一种基于锚节点功率调节的加权质心定位算法,通过锚节点的功率调节确定各个锚节点对于未知节点的影响力因子,并将其作为权重计算未知节点的位置,体现了不同锚节点为未知节点位置计算结果的影响.仿真表明,该算法减小了节点的平均定位误差,是一种适合于无线传感器网络的定位方法.     

基于混合多锚节点的WSN传感器定位方案

针对传统DV-hop或DV-distance算法在随机大型WSN网络中定位精确度较低的问题,提出一种混合多锚节点的WSN定位方案。第一阶段,为OLSR协议(最优链路状态路由协议)引入补充的锚节点选取准则,并采用改进的OLSR完成第一阶段的定位;第二阶段,分别采用DV-hop与DV-distance搜索中继节点,同时将已定位的节点加入锚节点集合。实验结果表明,该算法获得了较好的定位精确度与稳定性。     

松弛迭代多维定标 WSN 分布式定位算法改进﹡

在无线传感器网络的很多应用场景中,传感器节点的自定位都是非常关键的。传感器节点的分布是随机散布的,有规则的和不规则的两种。经典的 MDS 定位算法,在大尺度传感器网的节点数较多时定位精度较低。基于大尺度无线传感网络提出了基于松弛迭代随机扩散消息的分布式定位算法。仿真表明经过大约18次左右的局部地图扩散与合并,基本上能完全覆盖一个在规则的方型区域内200个节点随机分布的网络,算法的复杂度低为 O(n(n-1)),在8个锚节点的情况下,仿真得平均连通度为30.114,平均定位误差仅为3.188%。仿真表明基于松弛迭代多维定标的节点的定位精度比经典的多维定标算法定位精度高,误差小。     

一种基于蒙特卡洛的移动传感网节点定位的优化算法

在无线传感网中,针对蒙特卡洛移动节点定位算法中通信半径无法确定这一缺陷,本文提出了一种结合跳／距转换模型的蒙特卡洛定位改进算法。该算法首先利用实际中测得节点间的跳数信息得到节点的预估计坐标,进而精化出一个环形采样区域,提高了采样效率。仿真结果表明,优化之后的算法能够显著地减少定位采样次数,能够有效提高定位的准确性,并且能改善网络中低锚节点密度时的性能。     

On localization in sensor network with inaccurate and incomplete distance measurements

The sensor network localization problem has received a lot of attention in recent years because many important applications resort to node position information. In contrast to the many interesting algorithms proposed in the literature, this paper provides a relatively straightforward procedure that can tackle localization problem for sensor network in a Least Squares Euclidean Distance Matrix Approximation （LS-EDMA） framework. Simulation results reveal that our proposed algorithm is more robust than another popular Multi-Dimensional Scaling （MDS） and Semi-Definite Programming （SDP） based localization techniques, especially with inaccurate and incomplete distance measurements.     

Performance of IR-UWB cross-layer ranging protocols under on-body channel models with body area networks

This paper deals with the positioning performance of the 3-Way ranging protocol (3-WR) in a wireless body area network (WBAN). The purpose is to propose a new cooperative algorithm to improve the number of estimated positions with a conditional permutation of the on-body anchors. To do so, we first evaluate and analyze the positioning success rate under a realistic mobility scenario and using two scheduling strategies: Broadcast single node localization (P2P-B) and aggregated and broadcast (A&B)) with a medium access control (MAC) layer based on time division multiple access (TDMA). The 3-WR estimates the distance between two nodes placed on the body with the transmission of three packets. The wireless transmission is based on impulse radio ultra wideband (IR-UWB). However, these transactions can be lost through the WBAN channel leading into a “bad positioning service.” We consider a physical layer based on IR-UWB with three different channels: (1) an empirical theoretical model based on the on-body CM3 path loss channel (Anechoic chamber), (2) a simulated channel calculated by ray-tracing with the PyLayers simulator, and (3) an experimental channel model obtained by measurements. Our results show that the channel affects the positioning success rate that decreases as a function of the sensitivity threshold at the receiver. This can be solved with long and short term analysis for the choice of virtual anchors to increase the positioning performance.     

A Comprehensive Tutorial on Localization: Algorithms and Performance Analysis Tools

This tutorial offers a comprehensive view of technological solutions and theoretical fundamentals of localization and tracking (LT) systems for wireless networks. We start with a brief classification of the most common types of LT systems, e.g. active versus passive technologies, centralized versus distributed solutions and so forth. To continue, we categorize the LT techniques based on the elementary types of position-related information, namely, connectivity, angle, distance and power-profile. The attention is then turned to the difference between active and passive LT systems, highlighting the evolution of the localization techniques. Motivated by the interests of industry and academia on distance-based active localization system, a deep review of the most common algorithms used in these systems is provided. Non-Bayesian and Bayesian techniques will be tackled and compared with numerical simulations. To list some of the proposed approaches, we mention the multidimensional scaling (MDS), the semidefinite programming (SDP) and the Kalman filter (KF) methods. To conclude the tutorial, we address the fundamental limits of the accuracy of range-based positioning. Based on the unifying framework proposed by Abel, we derive the closed-form expressions for the Cramér?CRao lower bound (CRLB), the Battacharyya Bound (BB), the Hammersley?CChapmann?CRobbins Bound (HCRB) and the Abel Hybrid Bound (AHB) in a source localization scenario. We show a comparison of the aforementioned bounds with respect to a Maximum-Likelihood estimator and explore the difference between random and regular (equi-spaced anchors) network topologies. Finally, extensions to cooperative scenarios are also discussed in connection with the concept of information-coupling existing in multitarget networks.     

移动无线传感器网络节点的定位

文中在MCB（Monte—Carlo Localization Boxed）定位算法的基础上提出了一种新的移动无线传感器网络（Mobile Wireless Sensor Networks）节点的定位算法——权重MCB算法。MCB算法在定位过程中,在采样和滤波阶段用到了一阶锚节点和二阶锚节点的位置信息,而没有应用到邻居节点的位置信息。权重MCB在定位过程中不仅用到了一阶锚节点和二阶锚节点的位置信息,还应用到了一阶邻居节点的采样集合里的采样点（即一阶邻居节点的估计位置）,从而改进了定位精度。对比MCB算法,权重MCB算法对定位精度的改进为13％～18％。     

一种基于半定松弛技术的TDOA-FDOA无源定位算法

在运动目标的无源定位场景下,闭式算法在低噪声情况下可以到达克拉美罗下界(CRLB),但是这些算法往往不能适应较大的测量噪声环境。针对目前闭式算法适应大噪声能力较差这一问题,该文联合到达时间差(TDOA)以及到达频率差(FDOA),提出一种基于半定松弛(SDR)技术的无源定位算法。该算法首先构建传统闭式解的伪线性方程,其次利用随机鲁棒最小二乘(SRLS)的思想以及目标参数与额外变量之间的非线性关系,将无源定位问题转化为了具有2次等式约束的最小二乘问题;随后,将半定松弛技术应用到这一问题上,约束最小二乘问题松弛为半定规划(SDP)问题,最后,借助优化工具箱可以有效地对目标参数进行求解。该文所提出的算法不需要初始值先验条件,仿真实验表明了所提算法的有效性。     

A BDD-based algorithm for reliability analysis of phased-missionsystems

This paper presents a new algorithm (PMS-BDD) based on the binary decision diagram (BDD) for reliability analysis of phased-mission systems (PMS). PMS-BDD uses phase algebra to deal with the dependence across the phases, and a new BDD operation to incorporate the phase algebra. Due to the nature of the BDD, cancellation of common components among the phases can be combined with the BDD generation, without additional operations; and the sum of disjoint products (SDP) can be implicitly represented by the final BDD. Several examples and experiments show that PMS-BDD is more efficient than the algorithm based on SDP, in both computation time and storage space; this efficiency allows the study of some practical, large phased-mission systems     

Pascal’s triangle-based range-free localization for anisotropic wireless networks

This paper introduces a Pascal’s triangle model to draw the potential locations and their probabilities for a normal node given the hop counts to the anchors according to the extent of detour of the shortest paths. Based on our proposed model, a Pascal’s triangle-based localization (PTL) algorithm using local connectivity information is presented for anisotropic wireless networks with a small number of anchors. The superiority of the PTL algorithm has been validated over the state-of-the-art algorithms through MATLAB simulations. We have shown that compared to the other algorithms, the PTL algorithm achieves higher localization accuracy with even fewer anchors. We have also validated the performance of the PTL algorithm in a real environment.     

基于PSO-BP算法的无线传感器网络定位优化

在研究现有定位算法的基础上,针对基于接收信号强度指示(RSSI)定位模型中的参数易受环境影响等问题,提出了一种新型的粒子群优化(PSO)算法与后向传播(BP)神经网络相结合的算法.BP网络算法权值的修正依赖于非线性梯度值,易形成局部极值,同时学习次数较多,需先通过粒子群算法进行优化.为了提高定位精度,首先采用速度常量法滤波处理,然后通过改进的混合优化算法对BP神经网络初始权值和阈值进行优化,并分析算法的性能.试验中隐层节点个数采用试错法,从12到19变化,以确定合适数目.实验结果表明,与一般加权算法和传统BP算法相比,改进的混合优化算法可大幅改善测距误差对定位误差的影响,同时可使25 m内最小定位误差小于0.27 m.     

基于RSSR融合RNGO-Elman神经网络的室内可见光定位

针对动态环境下基于接收信号强度的传统可见光定位方法定位精度低、稳定性差等问题,提出一种基于接收信号强度比的改进北方苍鹰算法(NGO)优化Elman神经网络(RNGO-Elman)的室内可见光定位系统。提出选择一个辅助参考点,将待测参考点与辅助参考点的接收信号强度比值和接收机的真实位置作为训练集数据,建立不受动态环境影响的指纹数据库。针对NGO算法收敛速度慢、容易陷入局部最优等问题,利用折射反向学习策略初始化种群,增加种群多样性,引入非线性权重因子来加快收敛速度,避免陷入局部最优。使用优化后的NGO算法来优化Elman神经网络的初始权值和阈值,构建RNGO-Elman动态定位预测模型。仿真结果表明,在4m×4m×3m的实验空间下,优化后的RNGO-Elman定位模型平均定位误差为1.34cm,定位精度相较于Elman定位算法、NGO-Elman定位算法分别提高了82%,21%。在LED发射功率波动时,基于RSSR的RNGO-Elman定位误差为1.29cm,1.38cm。所提可见光定位方法具有定位精度高、定位性能稳定等优点。     

基于反向学习策略的自适应花授粉接收信号强度指示室内可见光定位

为提高大型室内场所的定位精度,提出一种基于改进自适应花授粉算法的接收信号强度指示(RSSI)可见光定位方案。利用固定在屋顶呈网格型排布的LED发送位置信息,接收端采用基于反向学习策略和自适应花授粉算法的RSSI定位方法实现精确定位。传统花授粉算法具有易陷入局部最优、缺乏变异机制等缺点,利用反向学习策略可使初始种群分布更加均匀,通过提高种群多样性可使算法跳出局部最优;采用有利于全局广泛搜索的自适应移动因子提高收敛速度。在100 m×100 m×100 m大型室内场所的一层100 m×100 m×10 m的空间中,考虑热噪声和散射噪声干扰的情况,经过多次仿真可得,相比于传统定位算法,随机灯排布下采用改进花授粉的RSSI算法的定位误差小于±1 cm;采用网格型灯排布结合改进定位算法的室内可见光定位系统时,定位精度得到明显提升,定位时间大幅缩短。该方案具有定位精度更高、计算速度更快、工作稳定等优点。