一种新的面向目标跟踪的传感器节点调度策略

在用于目标跟踪的无线传感器网络中,传感器节点的电池能量有限,跟踪质量和网络生命周期是两个冲突的优化目标。在目标的移动过程中,如何选择合适的传感器节点子集,在指定时刻被唤醒,是延长网络生命周期和保证跟踪质量的关键。一种能量高效的传感器节点调度机制被提出,该机制综合考虑跟踪质量和网络生命周期,根据目标可能的运动区域选择需要启动的传感器节点子集。仿真试验结果证明该调度策略是高效的和节能的。     

传感器网络中一种基于两阶段睡眠调度的目标跟踪协议

移动目标跟踪是传感器网络的一个重要应用.网络中传感器节点协作发现日标并将对目标的感知信息反馈给用户.为了有效地减少网络能耗和提高跟踪质量,文中提出一种基于两阶段睡眠调度的目标跟踪协议.该协议将整个跟踪过程划分成两个阶段,根据各阶段对节点密度要求的不同,分别采用不同的睡眠调度机制.文中进一步对所提出协议进行优化,在保证跟踪质量的同时最小化系统能耗.最后用36个传感器结点验证了所提出协议的有效性.     

面向目标跟踪的传感器网络调度方法

对面向移动目标跟踪任务的传感器网络调度方法进行了研究。从单任务跟踪精度和总体任务完成情况两方面设计调度指标,采用扩展卡尔曼滤波器实现目标跟踪并计算跟踪精度,进而建立了该问题的混合整数调度模型。针对模型复杂度较高的特征,提出一种基于局部解空间跳出机制的改进型遗传算法并进行求解。仿真结果表明该算法针对该问题具有较高的求解性能。     

分布式动态一致性非线性目标跟踪策略研究

针对集中目标跟踪和分层目标跟踪中心节点通信瓶颈以及容错性能差的不足, 提出了一种分布式动态一致性非线性目标跟踪策略。目标状态初始化由网络节点采用加权最小二乘法完成。整个跟踪过程采用动态成簇策略, 分阶段选择并唤醒任务节点检测目标并执行分布式一致性扩展卡尔曼滤波策略完成目标的状态估计, 其余节点进入休眠状态从而能降低系统的能耗。从跟踪误差和能量两个方面, 与集中目标跟踪算法相比, 仿真结果表明所提算法与集中卡尔曼滤波相比, 跟踪精度相当, 适用于要求高可靠度的非线性跟踪。此外分布式的工作方式使得节点仅需与邻居交换数据并在局部完成状态估计, 消除集中式结构中心节点的瓶颈, 以保证部分传感器节点的损坏不会影响到全局任务的完成。     

基于能量优化的无线传感器网络动态分簇目标跟踪

针对无线传感器网络动态分簇目标跟踪中的数据碰撞与簇首选择过程导致能耗过高问题,提出一种基于能量优化的无线传感器网络动态分簇方法。首先,构建时分竞选传输模型,主动避免动态簇内数据碰撞,降低节点能耗;然后,基于能量信息与跟踪质量,提出能量均衡的最远节点调度策略,优化簇头节点调度;最后,根据加权质心定位算法,完成目标跟踪任务。实验结果表明：在节点随机部署的环境下,所提方法对于非线性运动目标的平均跟踪精度为0.65 m,与多目标跟踪动态簇员选择方法（DCMS）相当,比分布式事件定位动态分簇目标跟踪算法（DELTA）提高了45.8%;能量消耗方面,与DCMS和DELTA相比,所提方法的动态跟踪簇能量消耗有效降低了61.1%,延长了网络寿命。     

异构无线传感器网络对移动目标的连续跟踪

针对固定节点组成的传统无线传感器网络在进行目标跟踪时存在的能耗过高和覆盖空洞问题,提出在传统传感器网络中引入少量移动性节点组成异构传感器网络进行目标跟踪的方案.基于较传统0/1监测模型更为实际的概率监测模型,提出一种协同调度移动节点和固定节点工作的算法来对移动目标进行跟踪.移动节点对目标实施近距离的移动式跟踪,减少了处于活跃状态的固定节点数量,节约了能耗.此外,移动节点可以移动进入空洞监测目标,解决了传统网络不能监测覆盖空洞中的目标的问题.基于NS2的实验结果表明所提出的跟踪方法可以大幅度减少固定节点的能耗并提高跟踪质量,证明了其有效性.     

面向目标跟踪的传感器网络睡眠调度协议

移动目标跟踪是无线传感器网络中的一项重要应用,将睡眠调度机制引入到目标跟踪算法中可以大大降低能耗。针对目标跟踪的实际需求,提出一种面向目标跟踪的传感器网络睡眠调度协议。根据目标跟踪不同阶段,分别设计了目标跟踪前和跟踪过程中传感器节点的睡眠调度机制;另外给出了目标丢失时,如何唤醒节点继续跟踪目标的调度策略。结果表明:该算法能够在保证跟踪质量的同时,降低跟踪能耗。     

无线传感器网络具有跟踪质量保证的节点选择算法

目标跟踪广泛地应用于无线传感器网络的各个领域.该文研究无线传感器网络目标跟踪中的节点选择问题,提出了具有跟踪质量保证的跟踪节点选择算法.该算法在保证给定目标跟踪可靠性要求的同时对网络生存期进行优化.文中首先分析了影响传感器节点生存期的3个因素,包括节点感知数据的可靠性、节点剩余能量以及节点通信和采样的能量消耗.在此基础上建立节点生存期函数,在满足用户给定目标跟踪可靠性要求的前提下选择使网络生存期最大化的节点参与目标跟踪.实验结果表明该文所提出的节点选择算法可以有效延长网络生存期.     

传感器网络下机动目标动态协同跟踪算法

对传感器网络下的机动目标跟踪问题提出一种分布式传感器节点动态分簇、协同跟踪算法. 通过在线优化目标跟踪的性能函数和通讯代价, 自适应地选择节点并动态分簇, 通过多传感器节点的协同感知以及信息融合提高了跟踪精度. 由于问题的非线性和传感器节点的随机性, 本文基于粒子滤波器在线预测和估计目标状态的概率分布, 使用混合高斯粒子滤波器以及选择最短路径用于传感器节点之间的信息交换节约了通讯能量, 通过一种有效的粒子方法逼近目标状态的预测方差以实现传感器节点的最优选择. 仿真结果表明, 与 IDSQ 算法相比较, 本文提出的动态分簇算法实现了对机动目标的高精度跟踪.     

无线传感器网络可扩展一致性目标跟踪算法研究

为提高机动目标跟踪性能,降低无线传感器网络的能量消耗,提出一种可扩展的动态平均一致卡尔曼滤波算法.根据预测的下一步目标位置,将无线传感器网络的节点动态组织成簇,多个传感节点协作执行目标的检测及分布式状态估计.给出三种可扩展动态一致卡尔曼滤波算法,即基于观测值、观测新息和估计值的一致性卡尔曼滤波,适应于不同情况的目标跟踪.簇中传感节点仅需接收邻居节点的信息,簇头节点负责下一步任务节点的选择并将当前状态估计值和对应的误差协方差发送给下一步的任务节点以减少整个网络的通信量.仿真结果表明,基于观测值、新息及估计值的分布一致卡尔曼滤波在跟踪精度方面与集中卡尔曼滤波性能相当,而其分布式结构决定了算法具有更强的鲁棒性和容错能力,能够提高系统的可靠性.     

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Novel sensor scheduling and energy-efficient quantization for tracking target in wireless sensor networks

This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally.     

基于无源传感器协同的机载雷达自适应辐射控制算法

随着现代战场中电子对抗的日益激烈,雷达的生存环境受到了严重威胁。射频隐身技术是一种提高雷达及其搭载平台战场生存能力的重要途径。文中采用一种基于交互式多模型（Interacting multiple model, IMM）和扩展卡尔曼滤波（Extended Kalman filter, EKF）的序贯滤波方法。该算法优先使用无源传感器进行目标跟踪,将滤波过程中的状态估计预测协方差与预先设定的协方差门限进行比较,当目标跟踪精度不满足要求时,开启雷达工作。同时根据目标运动状态自适应地调整雷达工作时的辐射能量,从而进一步减小目标跟踪过程中机载雷达的辐射总能量。仿真结果表明,本文算法可以有效地配置机载雷达工作参数,提升系统的射频隐身性能。     

New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments

Tracking performance in surveillance systems depends on two interrelated functions: track updating, the process of incorporating a new measurement into the track to update the system state estimate, and return-to-track correlation, the process of selecting which sensor return, if any, to use for track updating. Because of the presence of a number of targets in the same vicinity and the existence of clutter and false alarms, the correlation function is generally performed imperfectly. Since typical tracking filters such as the Kalman filter do not account for such correlation errors, degraded performance often results as well as unreliable and optimistic estimates of tracking accuracies. This paper examines and provides for optimizing the overall tracking process considering both the correlation and track update functions and their interaction. General equations for tracking performance of any arbitrary tracking filter used with a broad class of correlation algorithms in dense multitarget environments are developed. A new reoptimized tracking filter is derived which provides, from among a general class of tracking filters using a priori information on sensor return statistics, optimal performance in such environments and which reduces to the Kalman filter when environmental effects are eliminated. The new filter is compared parametrically to both the standard Kalman filter and a computationally simpler version of the optimal filter in terms of tracking accuracy and reliability of the calculated covariance matrix, over a spectrum of environmental conditions. At high densities of sensor returns, the new filter provides considerably improved tracking performance as well as uniquely reliable estimates of this performance.     

TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.     

紧耦合多传感器混合跟踪算法

在增强现实应用中实现对运动目标的准确跟踪是一个具有挑战性的任务。基于混合跟踪通过对多传感器信息的融合通常比单一传感器跟踪算法更为优越的特性,提出了一种新的紧耦合混合跟踪算法实现视觉与惯性传感器信息的实时融合。该算法基于多频率的测量数据同步,通过强跟踪滤波器引入时变衰减因子自适应调整滤波预测误差协方差,实现对运动目标位置数据的准确估计。通过标示物被遮挡状态下的跟踪实验结果表明,该方法能有效改善基于扩展卡尔曼滤波器的混合跟踪算法对运动目标位置信息预测估计的准确性,提高跟踪快速移动目标的稳定性,适用于大范围移动条件下的增强现实系统。     

基于改进Kalman滤波的可见光极小目标TBD跟踪方法

针对无人机可见光图像极小目标跟踪问题,本文提出一种基于改进卡尔曼滤波的 (Tracking before detection,TBD)跟踪方法。首先利用检测算法定位目标位置作为卡尔曼滤波的测量值,检测过程中的匹配相似度参数作为卡尔曼滤波测量噪声协方差矩阵的参照依据,其次利用卡尔曼滤波建立跟踪框架预测下一帧的目标位置,最后检测模块以预测位置为 参考位置进行局部搜索,完成整个检测跟踪过程。为了提高跟踪效率,本文根据检测和预测位置积累误差判决检测模式,误差超过门限值则采取全局检测模式消除积累误差,否 则使用局部检测模式,降低TBD跟踪算法的运算复杂度。仿真实验证明,本文方法可以有效检测跟踪极小目标,提高跟踪的实时处理能力。     

基于目标跟踪的自适应广义高阶CKF算法

针对容积卡尔曼滤波在系统状态突变时滤波精度下降的问题,结合广义高阶容积卡尔曼滤波和强跟踪滤波算法,提出了一种自适应广义高阶容积卡尔曼滤波（AGHCKF）方法。采用广义高阶容积准则和矩阵对角化变换,以提高算法的滤波精度和稳定性。引入强跟踪滤波,利用渐消因子在线修正预测误差协方差阵,强迫残差序列正交,以增强算法应对系统状态突变等不确定因素的能力。将提出的AGHCKF算法应用于带有未知状态突变的机动目标跟踪问题并进行数值仿真,结果表明,AGHCKF在系统状态突变时能保证较高的滤波精度,具有较强的鲁棒性和系统自适应能力。     

ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.     

机载雷达辅助无源传感器对杂波环境下机动目标跟踪

机载雷达辅助无源传感器对目标协同跟踪具有重要战术作用,而当前相关算法模型较为简单。为了贴近工程实际,提出一种机载雷达辅助无源传感器对杂波环境下机动目标的跟踪算法。该算法考虑了地球曲率和载机时变姿态等因素的影响,基于地心地固(ECEF)坐标系,联合交互多模型(IMM)和概率数据关联(PDAF)方法,以综合预测协方差的迹为控制变量来管理机载雷达的开关机。仿真结果表明,通过选择合适的控制门限,在节约辐射能量、提升生存能力的同时算法的跟踪性能并无明显下降,从而表明了所提出算法的有效性。