基于RSSI自适应三维加权质心定位算法

针对无线传感网络RSSI加权质心定位算法精度较低的问题,提出了一种采用RSSI值作为加权因子的三维加权质心定位算法。依据RSSI值自适应缩小定位区域,并根据筛选出的最优参考节点构建三维球体定位模型。仿真结果表明,改进的定位算法在相同测试条件下,在精度与稳定性上相较传统加权质心算法有了大幅提高。     

工业机器人的局部标定

以ABB公司的IRB120工业机器人和激光位移传感器组成的数据采集系统为研究对象,通过对实验系统底座平面上数据点的采集和处理,来标定机器人与激光位移传感器之间的齐次变化矩阵。以采集到的数据点在机器人坐标系中位于同一个平面上为标定原理,利用最小二乘法拟合平面方程,得到机器人需要标定的各个参数及平面参数。在最小二乘优化处理过程中,通过对不同优化结果的分析对比,最终得到最优解,并为以后该系统在数据采集中的应用提供依据。     

基于RSSI和惯性导航的融合室内定位算法

针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。     

Novel localization algorithm for wireless sensor network based on intelligent water drops

High localization rigor and low development expense are the keys and pivotal issues in operation and management of wireless sensor network. This paper proposes a neoteric and high efficiency algorithm which is based on new optimization method for locating nodes in an outdoor environment. This new optimization method is non-linear optimization method and is called intelligent water drops (IWDs). It is proposed that the objective function which need to be optimized by using IWDs is the mean squared range error of all neighboring anchor nodes. This paper affirms that received signal strength indicator (RSSI) is used to determine the interior distances between WSNs nodes. IWDs is an elevated performance stochastic global optimization tool that affirms the minimization of objective function, without being trapped into local optima. The proposed algorithm based on IWDs is more attractive to promote elevated localization precision because of a special features that is an easy implementation of IWDs, in addition to non cost of RSSI. Simulation results have approved that the proposed algorithm able to perform better than that of other algorithms based on optimization techniques such as ant colony, genetic algorithm, and particle swarm optimization. This is distinctly appear in some of the evaluation metrics such as localization accuracy and localization rate.

     

无线传感器网络混合定位技术研究

在大规模复杂无线传感器网络中往往采用多种节点定位技术,在此结合现有无线传感器定位技术的现状,提出了一种混合定位技术以实现不同定位方法之间的互补。一方面利用RSSI定位弥补TDOA定位覆盖范围小的缺点;另一方面将测距信息引入到非测距定位DV—Hop算法中,用RSSI测距模型来提高DV-Hop算法中定位节点与信标节点间有效距离的精度。实验结果表明,该混合定位技术实现了TDOA,RSSI以及DV-HOP等定位技术的融合,有效地提高了复杂大规模无线传感器网络的节点定位精度。     

RSSI定位原理的研究与实现

针对无线传感器网络平台,对基于接收信号强度(RSSI)的定位原理进行了介绍,并探究了影响RSSI的因素。利用CC2530硬件平台和TinyOS软件平台对RSSI的定位原理进行了实践性的测量工作,利用数据与图表从功率、收发点相对角度、多结点收发和环境因素等方面定量分析了RSSI影响因素的大小。同时也利用测量的数据分析得到了RSSI的Log衰减模型,并利用这一模型进行了定位实验,通过一定的算法优化,定位精度能达到1m。     

基于无线传感器网络的煤矿安全监测系统的研究与设计

提出了应用于矿井安全监测和人员定位的无线传感器网络解决方案,以取代传统矿井安全监测系统的总线式数据传输和RFID定位,具有节点布置简易,方便移动,不易损坏,定位精度高以及成本低廉等优势。测试结果表明．基于CC2430的无线传感器网络解决方案和基于RSSI测距的改进APIT定位算法,完全满足矿井安全监测和人员定位的要求。     

Multi‐robot Mapping Using Omnidirectional‐Vision SLAM Based on Fisheye Images

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional‐vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas–Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi‐robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional‐vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.     

基于ZigBee室内定位算法研究

研究了基于信号强度的 ZigBee 定位算法,以接收信号强度指示(RSSI)定位算法为基础。通过全局坐标系中已知的参考节点位置以及与各个参考节点之间的信号强度,分析计算得到盲节点的坐标位置,即室内移动机器人当前的坐标位置。通过实验研究和数据分析,可以借助 ZigBee 低功耗及组网稳定等特性对室内定位领域有进一步的理解和应用。     

Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

In large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.     

Improvement of Positioning Technology Based on RSSI in ZigBee Networks

In this paper, the node localization methods of ZigBee wireless sensor networks were studied. There are two key issues affecting the positioning accuracy: accuracy of RSSI value and optimization of localization algorithm. For the first issue, the effects of two kinds of environmental disturbance on RSSI values were analyzed, and then RSSI values were pretreated using Kalman filter. For the second, the RSSI-based localization algorithm were introduced in detail, and a new algorithm-triangle centroid localization algorithm based on weighted feature points-was proposed. MATLAB simulation and actual network tests were carried out. The simulation and experimental results all showed that our pretreatment strategy of RSSI and optimization of localization algorithm had great effects on positioning accuracy.     

改进的无线传感器节点定位算法

由于无线传感器网络具有其它网络不可比拟的各种优势,使得它在很多领域都有广泛的应用。对于无线传感器网络中的未知节点本身的定位工作是网络的各项应用的基础。本文主要分析无线传感器网络的节点定位技术,研究已有的定位算法,并根据现有算法提出一种改进的分布式的节点定位算法。该算法使用RSSI方法测距,无需增加新的硬件设备,通过分布式的算法来提高效率降低能耗,利用多次定位的平均值提高定位精度,降低了网络中的能量消耗,延长网络寿命。     

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

Improving Tightly LiDAR/Compass/Encoder-Integrated Mobile Robot Localization with Uncertain Sampling Period Utilizing EFIR Filter

Mobile Networks and Applications - In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the...     

3D-RABLC：一种基于LQI置信度的三维空间定位求精算法

提出一种基于LQI置信度的三维空间定位求精算法(3D-RABLC)。通过大量节点实验,获得节点间一跳RSSI值与距离的关系、LQI与分组错误率的关系,依此划分LQI置信度,对测得的RSSI值进行过滤,建立三维多跳求精模型或弥补求精方法对置信度低的RSSI值进行修正。节点实验表明,该算法大大降低了RSSI测距误差,比已有三维定位算法具有更好的定位精度。     

Robust tracking algorithm for wireless sensor networks based on improved particle filter

Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non‐Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re‐sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. The quality prediction function is proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, a centroid shift vector is calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks

The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.     

CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors

Indoor localization using a Received Signal Strength Indicator (namely, RSSI localization) has been considered a poor measurement for target tracking. The main cause of this inaccurate measurement is that RSSI’s behaviors heavily depend on environmental factors. That is, one significant challenge to localization using RSSI is that the strength of a signal varies with the environment confounding wireless communications power and signal control. In this paper, we propose Circular RSSI And Multi-Sector tracking (CRAMStrack), a novel approach to reducing the uncertainty of RSSI localization by modifying the relationship of RSSI-to-Distance (RtD), based on the sectors of a circle and the position of the tracked target. Traditional RSSI tracking uses one uniform RtD relationship to locate a target whereas CRAMStrack utilizes multiple RtD responses for each wireless sensor. The paper examines CRAMStrack’s tracking ability in a Euclidean space with estimation techniques. Real-world experiments demonstrate CRAMStrack in a testbed environment to locate targets in both stationary, linear, and non-linear movement patterns with single and group-based formations. The track accuracy was about 1.46m for moving targets, while CRAMStrack had a 40% reduction in Root Mean Square Error (RMSE) over Uni-RtD using neighboring sensor information.

     

Wideband source localization using sparse learning via iterative minimization

In this paper, two extensions of the Sparse Learning via Iterative Minimization (SLIM) algorithm are presented for wideband source localization using a sensor array. The proposed methods exploit the joint sparse structure across all frequency bins, and estimate the spatial pseudo-spectra at various frequency bins jointly and iteratively. Via several numerical examples, we show that the proposed methods can provide high-resolution angle estimates and excellent source localization performance, and are able to resolve the left–right ambiguity problem, when used together with the vector sensor array technology.     

WSNs中基于无人机的强健节点定位算法

通过移动无人机(UAV)收集无线传感网络数据的方案已受到广泛关注,将感测的数据与产生此数据的传感节点位置关联起来是十分必要的。为此提出了基于无人机的强健节点定位算法(UAV-NL)。UAV-NL算法将UAV位置作为未知信息。传感节点接收由UAV在随机位置传输的beacon包,并记录接收信号强度指示(RSSI)矢量;通过理论推导2个RSSI矢量的范数距离与这2节点距离的线性关系;最后,通过RSSI值测距,并利用半定规划(SDP)算法估计节点位置。仿真结果表明,提出的UAV-NL算法即使在噪声信道条件下仍具有高的定位精确度。