基于变方差概率模型和进化计算的WSN 定位算法

定位是无线传感器网络技术和应用的重要基础.基于接收信号强度(received signal strength,简称RSS)的定位方法是实际应用中比较重要的定位方法考虑到实际应用中不同地点RSS测量信号的方差有所不同这一特点,运用最大概率似然理论,建立了更加符合实际的基于RSS测量的概率定位模型.对于模型中目标表达式高度非线性不好求解的特点,运用进化计算理论设计出符合传感器通信特征的定位算法(location in probability maximum with evolutionary algorithm,简称PMEA)求解概率可能性最大的位置坐标点,并用随机过程在数学上证明了算法的收敛性.最后,通过对实际公开数据集的实验,证实所提出的概率模型和PMEA算法确实能够提高RSS测距定位的精度.     

三维传感网空间RSS与AOA混合测量的精确定位方法

由于位置坐标参数的增加,三维传感网空间的定位难度较二维平面有所增大.单一的依靠接收信号强度(RSS)确定节点位置坐标的方法将使定位的不确定性增加,定位误差也较大.新型的阵列与智能天线的出现为节点间的到达角度(AOA)测量提供了方便,为此本文提出了一种三维传感网空间RSS与AOA混合测量的精确定位方法.将采用混合测量建立的非线性优化模型转化为线性方程,分别提出了节点位置坐标估计的非约束线性最小二乘(ULLS)及约束线性最小二乘(CLLS)方法.仿真测试了所设计算法的有效性,分析了不同测量噪声对位置坐标估计误差的影响.仿真表明所设计的ULLS和CLLS方法的计算速度快,相比于ULLS方法,采用约束后的CLLS方法的定位误差更小.在较小测量噪声范围内,ULLS和CLLS估计方法具有较高的稳定性和定位精度.     

移动锚节点定位WSNs中无标识节点算法研究

针对无线传感器网络(WSNs)无标识节点的定位问题,引入移动锚节点收集节点的接收信号强度(RSS)数据序列,利用无监督的聚类算法分析数据确定节点个数,依据锚节点运行的不同驻点,提取最强RSS信号进行圆环交叉搜索并标识覆盖网格重叠区域,再利用极大值(EM)算法筛选出可能含有未知节点的区域,最后用改进的粒子群优化(PSO)算法最终确定符合聚类个数的最优未知节点坐标.实验仿真结果表明:该算法在未知节点稀疏分布情况下,可以准确地估算未知节点个数和位置坐标.     

基于三个定向天线RSS的二维到达角估计

在室内定位设计中,接收信号强度（RSS）容易受到发射功率、信号衰减和多径环境等因素的影响。在锚节点天线高于被测节点天线的应用条件下,提出一种基于RSS的二维到达角（AoA）估计方法。将三个定向天线作为锚节点的波束切换天线且三个波束的方向图重叠,在测向技术中的和差法理论基础上,从多径信号分析和克拉美罗下界推导的角度出发,论证当AoA处于方向图重叠区域时RSS差分处理对于多径和噪声干扰具有一定程度的抑制作用,并研究该抑制效果与波束切换天线参数之间的关系。仿真结果表明,在单个锚节点条件下,该方法在约150 m²的定位区域范围内获得的平均AoA估计误差为4.3°,具有较高的AoA估计和定位精度。     

基于DBSCAN-GRNN-LSSVR算法的WLAN异构终端定位方法

针对WLAN室内定位系统中异构终端(指纹库终端和测试终端)引起的定位偏差过大的问题,提出一种基于DBSCAN-GRNN-LSSVR算法的解决方案。该文使用最小二乘支持向量回归机(LSSVR)构建指纹库终端接收信号强度(RSS)和物理坐标位置的映射关系模型;列出校准点处异构终端采集的RSS值,得到散点图;用基于密度聚类方法剔除边界点和噪声点;用广义回归神经网络构建异构终端RSS的映射函数;通过LSSVR模型定位测试点的位置。实验结果表明,与只用LSSVR算法相比,测试终端定位精度提高18-40%,有效解决了定位偏差过大的问题。     

基于WSNs的RSS无源被动定位算法评述

与传统有源定位技术相比,接收信号强度(RSS)定位技术无需目标携带任何电子标签,在需快速部署、紧急救灾等方面的应用前景广阔.阐述了RSS无源被动定位原理;详细介绍了RSS无源被动定位技术的几种定位算法;总结了当前算法存在不足之处,并展望了RSS无源被动定位技术的研究趋势.     

基于递归算法的最短跳数路径的RSS测距算法

多跳无线传感网络中的多类应用均需要准确的定位算法.为了降低定位成本,常采用基于接收信号强度(RSS,received signal strength)测距,为此,提出基于递归算法的最短跳数路径的RSS测距算法RFSPR(recursive function shortest path-based ranging).RFSPR算法首先利用递归函数搜索源节点与目的节点间所有具有最短跳数的路径,然后通过RSS测量这些最短路径的距离,最终将所有最短路径距离的平均值作为源节点与目的节点间距离的估计值.最后,将RFSPR算法与现存的同类算法进行了对比分析.实验结果表明RFSPR算法具有更低的测距误差.     

基于接收信号强度的非视距检测与修正算法

为了减少非视距（NLOS）误差对基于到达时间（TOA）的无线定位系统性能的影响,本文提出一种采用接收信号强度（RSS）与TOA测量值相结合的方法对含有NLOS误差的TOA测量值进行检测并修正。在视距（LOS）传播的TOA与RSS之间关系已知的前提下,利用定位基站得到的TOA与RSS测量值,计算TOA测量值中含有NLOS误差的可能性,并利用该可能性对TOA测量值进行修正。该方法在不增加通信次数的情况下,大大提高了定位精度。本文最后在一个无线定位系统上实现了该算法,并进行了对比实验。实验结果表明,该算法不需对多次定位结果进行统计,即可有效降低NLOS误差对系统性能的影响,适用于对功耗要求苛刻的场合。     

基于采样滤波的信号矢量分解移动定位算法

以接收信号强度(received signal strength,简称RSS)的测距技术为基础,借助移动传感器网络(MSN)中MCL类粒子滤波定位算法的采样、过滤方法,并融入物理中力的分解和合成的思想,提出了一种信号矢量分解的采样滤波移动节点定位算法.该算法通过建立直角坐标系,分解合成移动节点、样本点与信标节点间的信号矢量,利用误差圆环采样,比较移动节点与样本点的信号合矢量进行滤波,将合矢量模差绝对值最小的样本点坐标的均值作为移动节点的坐标.仿真结果表明,在同样的实验条件下,该算法的定位精度明显高于相比较的其他算法,且该算法不需要添加额外的硬件设备.     

转换坐标卡尔曼滤波算法分析

在非线性系统的目标跟踪中,常用的滤波算法是扩展卡尔曼滤波算法（EKFA）,但这种滤波算法常常会发散,近年来,Bar-Shalom等人在二维平面上提出了转换坐标卡尔曼滤波算法（CMKFA）,并通过仿真实验证明了在非线性系统的目标跟踪中此算法优于扩展卡尔曼滤波算法（EKFA）,文中对转换坐标卡尔曼滤波算法进行了深入的研究,通过理论推导证明了当状态方程和测量方程满足一定条件时,转换坐标卡尔曼滤波算法是去偏转换测量值的线性无偏最小方差估计算法。     

Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel non-convex estimator based on the least squares (LS) criterion. The derived non-convex estimator tightly approximates the maximum likelihood (ML) one for small noise levels. We show that the developed non-convex estimator is suitable for distributed implementation, and that it can be transformed into a convex one by applying a second-order cone programming (SOCP) relaxation technique. We also show that the developed non-convex estimator can be transformed into a generalized trust region sub-problem (GTRS) framework, by following the squared range (SR) approach. The proposed SOCP algorithm for known transmit powers is then generalized to the case where the transmit powers are different and not known. Furthermore, we provide a detailed analysis of the computational complexity of the proposed algorithms. Our simulation results show that the new estimators have excellent performance in terms of the estimation accuracy and convergence, and they confirm the effectiveness of combining two radio measurements.     

Angle-of-arrival localization based on antenna arrays for wireless sensor networks

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices.A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.     

Moment feature-based three-dimensional tracking using two high-speed vision systems

When considering real-world applications of robot control with visual servoing, both three-dimensional (3-D) information and a high feedback rate are required. We have developed a 3-D target-tracking system with a 1-ms feedback rate using two high-speed vision systems called Column Parallel Vision (CPV) systems. To obtain 3-D information, such as position, orientation and shape parameters of the target object, a feature-based algorithm has been introduced using moment feature values extracted from vision systems for a spheroidal object model. Also, we propose a new 3-D self-windowing method to extract the target in 3-D space using epipolar geometry, which is an extension of the conventional self-windowing method in 2-D images.     

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC.     

Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments

In anchor-free environments, where no devices with known positions are available, the error growth of autonomous underwater vehicle (AUV) localization and target tracking is unbounded due to the lack of references and the accumulated errors in inertial measurements. This paper aims to improve the localization and tracking accuracy by involving current information as extra references. We first integrate current measurements and maps with belief propagation and design a distributed current-aided message-passing scheme that theoretically solves the localization and tracking problems. Based on this scheme, we propose particle-based cooperative localization and target tracking algorithms, named CaCL and CaTT, respectively. In AUV localization, CaCL uses the current measurements to correct the predicted and transmitted position information and alleviates the impact of the accumulated errors in inertial measurements. With target tracking, the current maps are applied in CaTT to modify the position prediction of the target which is calculated through historical estimates. The effectiveness and robustness of the proposed methods are validated through various simulations by comparisons with alternative methods under different trajectories and current conditions.      

The 3-D Hough shape transform

The 3-D Hough shape transform is described which is used for the localization in space of 3-D objects defined in terms of the spatial organization of their features.     

A novel feature-based tracking approach to the detection, localization, and 3-D reconstruction of internal defects in hardwood logs using computer tomography

A novel feature-based tracking approach based on the Kalman filter is proposed for the detection, localization, and 3-D reconstruction of internal defects in hardwood logs from cross-sectional computer tomography (CT) images. The defects are simultaneously detected, classified, localized, and reconstructed in 3-D space, making the proposed scheme computationally much more efficient than existing methods where the defects are detected and localized independently in individual CT image slices and the 3-D reconstruction of the defects accomplished via correspondence analysis across the various CT image slices. Robust techniques for defect detection and classification are proposed. Defect class-specific tracking schemes based on the Kalman filter, B-spline contour approximation, and Snakes contour fitting are designed which use the geometric parameters of the defect contours as the tracking variables. Experimental results on cross-sectional CT images of hardwood logs from select species such as white ash, hard maple, and red oak are presented.     

一种用于无线传感器网络的新颖协作定位方法

提出了一种用于无线传感器网络系统的分布式协作目标定位方法。该方法利用传感区域单个传感器的测量能量来检测目标,根据该能量测量值建立一种分布式协作定位模型,从而获得目标的估计位置。大量的仿真实验显示：该方法能有效地定位目标,而且降低了处理复杂度。因此它对于无线传感器网络来说是一种适合的定位方法。     

A novel approach to 3-D gaze tracking using stereo cameras

A novel approach to three-dimensional (3-D) gaze tracking using 3-D computer vision techniques is proposed in this paper. This method employs multiple cameras and multiple point light sources to estimate the optical axis of user's eye without using any user-dependent parameters. Thus, it renders the inconvenient system calibration process which may produce possible calibration errors unnecessary. A real-time 3-D gaze tracking system has been developed which can provide 30 gaze measurements per second. Moreover, a simple and accurate calibration method is proposed to calibrate the gaze tracking system. Before using the system, each user only has to stare at a target point for a few (2-3) seconds so that the constant angle between the 3-D line of sight and the optical axis can be estimated. The test results of six subjects showed that the gaze tracking system is very promising achieving an average estimation error of under 1 degrees.