A robust localization algorithm in wireless sensor networks

Most of the state-of-the-art localization algorithms in wireless sensor networks (WSNs) are vulnerable to various kinds of location attacks, whereas secure localization schemes proposed so far are too complex to apply to power constrainedWSNs. This paper provides a distributed robust localization algorithm called Bilateration that employs a unified way to deal with all kinds of location attacks as well as other kinds of information distortion caused by node malfunction or abnormal environmental noise. Bilateration directly calculates two candidate positions for every two heard anchors, and then uses the average of a maximum set of close-by candidate positions as the location estimation. The basic idea behind Bilateration is that candidate positions calculated from reasonable (i.e., error bounded) anchor positions and distance measurements tend to be close to each other, whereas candidate positions calculated from false anchor positions or distance measurements are highly unlikely to be close to each other if false information are not collaborated. By using ilateration instead of classical multilateration to compute location estimation, Bilateration requires much lower computational complexity, yet still retains the same localization accuracy. This paper also evaluates and compares Bilateration with three multilateration-based localization algorithms, and the simulation results show that Bilateration achieves the best comprehensive performance and is more suitable to real wireless sensor networks.     

A robust localization algorithm in wireless sensor networks

Most of the state-of-the-art localization algorithms in wireless sensor networks (WSNs) are vulnerable to various kinds of location attacks, whereas secure localization schemes proposed so far are too complex to apply to power constrained WSNs. This paper provides a distributed robust localization algorithm called Bilateration that employs a unified way to deal with all kinds of location attacks as well as other kinds of information distortion caused by node malfunction or abnormal environmental noise. Bilateration directly calculates two candidate positions for every two heard anchors, and then uses the average of a maximum set of close-by candidate positions as the location estimation. The basic idea behind Bilateration is that candidate positions calculated from reasonable (i.e., error bounded) anchor positions and distance measurements tend to be close to each other, whereas candidate positions calculated from false anchor positions or distance measurements are highly unlikely to be close to each other if false information are not collaborated. By using ilateration instead of classical multilateration to compute location estimation, Bilateration requires much lower computational complexity, yet still retains the same localization accuracy. This paper also evaluates and compares Bilateration with three multilateration-based localization algorithms, and the simulation results show that Bilateration achieves the best comprehensive performance and is more suitable to real wireless sensor networks.     

无线传感器网络下基于压缩感知的多目标分层贪婪匹配定位

针对基于压缩感知（Compressive sensing,CS）的多目标定位问题,通过分析多目标场景中的隐含结构信息,本文提出一种层级的贪婪匹配追踪定位算法.该算法首先获得多目标在网格化空间中的可能位置作为全局估计层,然后利用该全局估计信息作为稀疏恢复层的输入信息,在网格化空间中重构多目标位置矢量.本文证明了文献中广泛采用的基于正交化的预处理方式实质上降低了信噪比（Signal to noise ratio,SNR）,从而降低了定位性能.本文通过全局估计,预先排除了不可能的位置,等效于从观测子空间中分离出信号子空间,从而降低了观测噪声的影响.通过理论分析与计算机仿真,表明所提算法具有线性复杂度且在相同信噪比下具有更高的定位正确率和定位精度.     

Iterative constrained weighted least squares estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of sensor position and velocity uncertainties

Sensor position and velocity uncertainties are known to be able to degrade the source localization accuracy significantly. This paper focuses on the problem of locating multiple disjoint sources using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) in the presence of sensor position and velocity errors. First, the explicit Cramér–Rao bound (CRB) expression for joint estimation of source and sensor positions and velocities is derived under the Gaussian noise assumption. Subsequently, we compare the localization accuracy when multiple-source positions and velocities are determined jointly and individually based on the obtained CRB results. The performance gain resulted from multiple-target cooperative positioning is also quantified using the orthogonal projection matrix. Next, the paper proposes a new estimator that formulates the localization problem as a quadratic programming with some indefinite quadratic equality constraints. Due to the non-convex nature of the optimization problem, an iterative constrained weighted least squares (ICWLS) method is developed based on matrix QR decomposition, which can be achieved through some simple and efficient numerical algorithms. The newly proposed iterative method uses a set of linear equality constraints instead of the quadratic constraints to produce a closed-form solution in each iteration. Theoretical analysis demonstrates that the proposed method, if converges, can provide the optimal solution of the formulated non-convex minimization problem. Moreover, its estimation mean-square-error (MSE) is able to reach the corresponding CRB under moderate noise level. Simulations are included to corroborate and support the theoretical development in this paper.     

区间范围内修正跳数值的Dv-hop定位算法

针对Dv-hop算法在估算跳数时引进较大误差的问题,提出了一种基于区间范围内修正跳数(RHWSR)的算法.根据Dv-hop算法定位过程,在平均每跳距离估算、未知节点到各参考节点之间距离的计算等两方面进行了改进,分析和仿真了不同通信半径与锚节点比率情况下的定位性能.结果表明,提出的改进措施可极大地提高节点定位精度.此外,...     

一种基于约束策略的无线传感器网络定位算法*

节点定位技术是无线传感器网络的关键支撑技术之一,对于无线传感器网络的基本理论方法和应用研究都具有重要意义。在深入研究分析距离无关定位算法的基础上,提出了基于约束策略的无线传感器网络定位算法。该算法无须测距,采用跳数估计节点间距离,并针对未知节点到锚节点距离计算中的不足,对锚节点的平均每跳距离作了修正;在估计未知节点坐标时,根据该未知节点通信范围内的锚节点对其所在位置进行约束。仿真结果表明,该算法具有较好的性能,比已有算法的定位精度有所提高。     

Joint estimation decision methods for source localization and restoration in parametric convolution processes. Application to accidental pollutant release

In this paper, the problem of pollutant source localization and flow estimation is addressed. Potential applications of this work include leakage of hazardous chemicals or industrial effluents coming from an accidental situation. It is tackled in a one-dimensional context such as river, tunnel, canal with the aid of a single remote sensor. The pollutant is assumed to be coming from one out of N possible sources. Measurements are the result of a parametric convolution integral. The task may be viewed as a conditional deconvolution which requires a priori knowledge. In order to reduce the set of solutions, a source flow model is considered which introduces time bounds of the accidental spill. A joint estimation decision is derived in a Bayesian framework in both cases: with and without source assumptions. Without source model, the algorithm is unable to recover far sources location. On the contrary, the proposed source model enables to balance decision and take into account near and far sources as well. The benefit for this kind of solution is shown practically in terms of localization quality.     

LAI：一种基于连通度的无线传感器网络节点定位算法

无线传感器网络的节点定位算法中,LCO算法将节点间具有连通度的通信连接视为对节点位置的约束,并由此确定节点的位置估计.提出了一种改进的分布式节点定位算法LAI(Localization with All Nodes and Iteration),结合不具有连通度的节点位置关系进行定位.仿真实验结果表明,改进算法相对于LCO算法,提高了定位估计的准确性,减小了对于锚节点的依赖程度.     

改进的无线传感器网络DV-Hop定位算法

为了解决DV-Hop算法定位精度低的问题,提出一种分轮优化的改进DV-Hop定位算法。首先通过跳数阈值限制锚节点广播信息的范围;其次用每轮锚节点的平均每跳距离误差来修正锚节点的平均每跳距离;然后通过共线度检测区域,找出适合定位的锚节点组;再用三边测量法计算出参与定位的每组锚节点组的定位结果,用所有锚节点组定位结果的均值作为未知节点的估计位置;最后把本轮定位的未知节点升级为新的锚节点,进行下一轮定位。仿真结果表明,改进算法在不增加额外硬件开销的基础上,减小了定位误差,有效地提高了定位精度。     

一种非视距环境下的目标定位算法

针对机器人、无人机和其他智能系统的位置信息,研究了非视距(non line of sight, NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中,通过引入平衡参数来抑制NLOS误差对定位精度的影响,并成功将定位问题的形式与一个广义信赖域子问题(generalized trust region subproblem,GTRS)框架进行耦合。与其他凸优化算法不同的是,本文没有联合估计目标节点的位置和平衡参数,而是采用了一种迭代求精的思想,算法可以用二分法高速有效地进行求解。 所提算法与已有的算法相比,不需要任何关于NLOS路径的信息。此外,与大多数现有算法不同,所提算法的计算复杂度低,能够满足实时定位的需求。仿真结果表明:该算法具有稳定的NLOS误差抑制能力,在定位性能和算法复杂度之间有着很好的权衡。     

基于平均跳距估计的改进DV-Hop定位算法

传统DV—Hop定位算法只考虑了最近一个锚节点估计的平均每跳距离,而单个锚节点估计的平均每跳距离值无法准确地反映网络的实际平均跳距,导致定位误差较大。针对这一问题,提出一种基于平均跳距估计的改进DV—Hop定位算法。改进算法在计算未知节点到各个锚节点距离时,考虑到离该未知节点最近的锚节点到其它锚节点的距离及跳数的不同,计算出不同的平均跳距,使其更接近于实际平均跳距。仿真结果表明,与传统DV—Hop算法相比,改进算法在不需要增加节点的硬件开销的基础上能更有效地提高定位精度,并且算法简单,计算量小,是无线传感器网络中节点定位的一种实用方案。     

A distributed continuous-time algorithm for network localization using angle-of-arrival information

This paper studies the angle-of-arrival (AOA) localization problem, namely, localizing networks based on the angle-of-arrival measurements between certain neighboring network nodes together with the absolute locations of some anchor nodes. We propose the concepts of stiffness matrix and fixability for the anchored formation graphs modeling the networks, and show that they provide a complete characterization of the AOA localizability as well as an explicit formula for the localization result. Moreover, a distributed continuous-time algorithm is proposed that converges globally to the correct localization result on fixable formation graphs. Performances of the proposed algorithm, e.g., convergence rate and robustness to communication delay, are characterized and optimized. Sensitivities of the localization results with respect to errors in AOA measurements and anchor node positions are also analyzed.     

基于Steffensen迭代和模糊信息的节点定位算法

为了提高无线传感器网络节点定位精度,提出了一种基于Steffensen迭代和模糊信息的节点定位算法.算法在模糊信息定位方法的基础上,通过引入Steffensen迭代求精提高节点定位精度.算法将锚节点分为静态锚节点和移动锚节点,利用移动锚节点不断的运动来辅助静态锚节点进行定位.首先利用节点间的模糊信息实现未知节点位置的粗略定位,然后利用Steffensen迭代对节点位置进行不断迭代求精,以实现未知节点的精确定位.通过仿真实验证明,相比3D-ADAL算法和改进的TOF测距算法,本文算法不仅降低了定位误差率,减小了网络的通信开销,还提高了节点定位效率.     

锚节点稀疏的传感器网络节点自定位算法

针对Euclidean算法中定位精度及覆盖率受锚节点密度影响较大的问题,提出一种改进的分布式节点自定位算法。该算法将初始定位精度较高的节点升级为锚节点,未知节点根据更新的锚节点位置信息循环求精,并通过估计坐标值的方差来控制循环求精过程中的循环次数。仿真实验显示,改进定位算法在锚节点密度较低的情况下能有效提高定位精度和覆盖率,明显降低了对锚节点密度的依赖程度。     

基于最大似然估计的加权质心定位算法

为解决无线传感器网络中节点自身定位问题,针对接收信号强度指示（received signal strength indication,RSSI）测距误差大和质心定位算法精度低的问题,提出一种基于最大似然估计的加权质心定位算法．首先通过计算将估计距离与实际距离之间的最大似然估计值作为权值,然后在权值模型中,引进一个参数k优化未知节点周围锚节点分布,最后计算出未知节点的位置并加以修正．仿真结果表明,基于最大似然估计的加权质心算法具有定位精度高和成本低的特点,优于基于距离倒数的质心加权和基于RSSI倒数的质心加权算法,适用于大面积的室内定位．     

基于AUPF的TDOA几何定位跟踪算法研究

使用无源时差（TDOA）定位技术确定无人机等小型辐射源目标的位置是当前研究的热点,针对时差定位算法较为复杂的实际情况,推导了时差双曲线的几何解,并提出了一种基于自适应无迹粒子滤波（AUPF）技术的移动目标定位跟踪方法。通过仿真对该方法在不同场景的应用效果进行了验证,进一步比较分析了算法的定位精度。结果表明,基于自适应无迹粒子滤波的时差几何定位跟踪算法可以在多种情况下较好地拟合出目标真实运动轨迹,实现对运动目标的定位跟踪,同时拥有更低的定位误差和更高的轨迹包容度,使用该方法可以显著提高对非合作移动辐射源目标的位置估计性能。     

一种基于动态跳数距离的有洞无线传感器网络定位方法

在无线传感器网络中,与距离无关的定位技术一直是一项挑战性的工作。尤其是在有洞的各向异性网络中,多}L节点之间的距离估算更是一个难点。针对有洞的无线传感器网络,提出一种新的距离无关定位方法,该方法可以较好地估算未知节点到参考节点之间的距离。其主要思想是,先佑算各信标节点对之间的平均单跳距离,然后选择平均单跳距离较大并且最短路径通过未知节点的信标节点对作为参考节点来估算未知节点的位置。新算法能够较好地滤除距离估算误差较大的信标节点作为参考节点。实验表明,新算法比以前的算法定位更准确。     

基于Monte Carlo的无线传感器网络定位算法

无线传感器网络节点自身定位技术是无线传感器网络关键技术之一。针对目前各种定位算法存在定位精度较低的问题,提出了一种基于Monte Carlo方法的定位算法,该算法利用粒子到锚节点的距离计算各粒子的权值,通过滤波不断更新粒子的集合,使粒子收敛到未知节点的位置。对非视线情况、不同锚节点个数、迭代次数及粒子数进行了定位过程仿真,并和极大似然估计定位算法进行了定位结果比较。结果表明:该算法充分利用了对节点位置估计的有效信息,一定程度上抑制了非视线误差的影响,定位精度高,稳定性好。     

Probabilistic Modeling and Recognition of 3-D Objects

This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.     

基于同心圆定位算法的改进算法研究

在分析了常用几种无线传感器节点定位算法的基础上,依据同心圆定位算法原理,提出环形定位算法。该算法的原理是利用锚节点通过一定规则做圆环,不断缩小未知节点的估算区域,直到得到包含未知节点的最小区域,取最小区域质心位置作为未知节点的估算坐标。对同心圆定位算法、环形定位算法及改进方案进行了对比仿真实验,结果表明,在锚节点比例达到5%,在20*20m2的仿真场景内部署1000个传感器节点、锚节点密度为5%时,同心圆定位算法误差为34.86%,环形定位算法定位误差为26.64%。在改进方案中,运用了多次划分圆环方法来提高定位精度。实验结果表明,改进后的算法在锚节点密度为5%时,定位误差降低到15.76%。