基于双层预测机制的传感器网络目标跟踪算法

针对层次型传感器网络的目标跟踪问题,提出了一种基于双层预测机制的目标跟踪算法,由传感器节点执行Bayes估计算法进行目标位置的预测与更新(微观预测);由簇头进行基于曲线运动方程的目标位置预测(宏观预测),并用预测结果对传感器节点所得到的目标位置进行更新,提高了目标位置计算精度.仿真结果表明,算法具有较好的跟踪精度和能量效率.     

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.     

无线传感器网络中基于微粒群算法的优化覆盖机制

建立了无线传感器网络节点覆盖优化数学模型,设计了一种基于二进制随机多目标微粒群优化(SMOPSO)算法.根据最大化覆盖网络目标函数和最小化传感器节点的利用率目标函数进行优化算法操作,以达到降低网络冗余,延长网络生存时间的效果.仿真实验结果表明,本文提出的无线传感器网络优化覆盖方法能够满足节点利用率低、覆盖率高的要求.     

地震微观前兆预报网络系统设计研究(2-2)——基岩传感器网络和数据融合技术

提出了地震微观前兆预报网络系统设计的一整套详细方案,包括地震模型、基岩传感器网络和数据融合、信号处理技术和网络系统设计.本文介绍传感器网络和智能数据融合技术,包括:(1)无线传感器网络.(2)多传感器数据融合技术;(3)多平台多传感器目标跟踪解耦相关算法;(4)传感器网络中合作信号和信息处理技术;(5)传感器网络中分布目标的分类和跟踪.     

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.     

无线传感器网络中基于信息收益的多目标跟踪

目标跟踪是无线传感器网络应用的一个很重要的研究领域,节点有限的通信能力、处理能力、存储能力限制了传统跟踪算法的使用.为了提高网络的信息感知能力和降低能量消耗,提出基于信息收益的加权质心多目标跟踪算法,算法通过信息收益函数选取并唤醒节点对目标感知,通过传感器属性信息解模糊,并利用加权质心算法实现了多目标跟踪.仿真结果表明,与Bayes滤波协同多目标跟踪算法相比,算法虽精度略低,但时间复杂度低、失跟率低、实时性高,总体性能优于前者.     

基于操作风险的双模式传感器管理方法

基于风险理论研究同时面向目标检测和跟踪任务的传感器管理方法.首先,定义目标检测风险和目标跟踪风险,并分别给出计算方法;其次,以目标检测风险最小化为目标函数建立传感器部署模型,以目标检测风险和目标跟踪风险之和最小化为目标函数建立传感器调度模型,且该调度发生在前面传感器部署模型建立的传感器网络之上;然后,针对模型的求解,同时为克服基本人工蜂群算法易陷入局部最优的缺点,提出改进的双向轮盘赌-粒子群-蜂群算法;最后,通过仿真验证模型和算法的有效性.     

基于SIM-GM-PHD的低可观测目标跟踪算法

针对多传感器协同探测多个低可观测目标问题,提出一种基于强度叠加的多传感器高斯混合概率假设密度(SIM-GM-PHD)滤波器,并提出目标状态的两步提取策略.首先,利用跟踪门对预测强度函数中每个高斯分量选择有效传感器集合;然后,利用各传感器量测数据更新其对应的高斯分量,叠加所有传感器的局部后验强度以及全局漏检强度得到融合后验强度;最后,提出目标状态的两步提取策略对目标的个数与状态进行估计.仿真结果验证了所提出算法的有效性.     

星凸形多扩展目标跟踪中的传感器控制方法

针对多扩展目标跟踪中的传感器控制问题, 本文基于有限集统计(FISST)理论与随机超曲面模型(RHM), 利 用多伯努利(MBer)滤波器提出有效的传感器控制策略. 首先, 文中给出多扩展目标跟踪中基于信息论联合目标形状 估计优化和目标运动状态估计优化的传感器控制方法的求解思路. 其次, 给出RHM容积卡尔曼高斯混合(GM)势均 衡多扩展目标多伯努利滤波算法的具体实现过程. 然后, 结合GM密度间的柯西施瓦兹(Cauchy-Schwarz)散度提出 相应的传感器控制决策方法. 此外, 详细推导了扩展目标势的后验期望(PENET)的GM实现, 并提出以GM–PENET 为评价函数的传感器控制方法. 最后, 通过构造随机星凸形多扩展目标的跟踪优化仿真实验验证了本文所提传感 器控制方法的有效性.     

基于传感器多模式调度的智能目标跟踪算法

智能目标具有反跟踪的特性,其运动状态的改变与跟踪传感器的工作模式相关.为了实现对智能目标的有效跟踪,文中提出一种基于传感器多模式调度的交互多模型跟踪算法.首先,为智能目标构建混合多模型集,描述其与传感器模式相关的智能运动特性;其次,提出一种传感器多模式调度策略,综合考虑了一步预测的目标跟踪误差、二步预测的目标运动趋势误差以及传感器模式的辐射代价3方面因素,据此构建传感器多模式调度的目标函数;最后,将传感器多模式调度与交互多模型跟踪算法相结合,通过"模式调度→交互跟踪→模式调度"的闭环结构,提高了对智能目标跟踪的自适应性.仿真结果表明,与选择固定传感器模式的方法和基于近视策略的传感器模式调度方法相比,文中方法实现了对智能目标的持续精确跟踪.     

Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor

In this paper, we study the problem of joint underwater target detection and tracking using an acoustic vector sensor (AVS). For this challenging problem, first a realistic frequency domain simulation is set up. The outputs of this simulation generate the two dimensional FRequency–AZimuth (FRAZ) image. On this image, the random finite set (RFS) framework is employed to characterize the target state and sensor measurements. We propose to use the Bernoulli filter, which is the optimal Bayes filter emerged from the RFS framework for randomly on/off switching single dynamic systems. Moreover, to increase the performance of detection and azimuth tracking in low signal-to-noise ratio (SNR) scenarios, a track-before-detect (TBD) measurement model for AVS is proposed to be used with the Bernoulli filter. Sequential Monte Carlo (SMC) implementation is preferred for the Bernoulli filter recursions. Extensive simulation results prove the performance gain obtained by the proposed approach both in estimation accuracy and detection range of the system.     

Autonomous Bayesian Search and Tracking,and its Experimental Validation

We present a technique that uniformly controls a team of autonomous sensor platforms charged with the dual task of searching for and then tracking a moving target within a recursive Bayesian estimation framework. The proposed technique defines the target detectable region, and uniformly formulates observation likelihoods with detection and no-detection events. The unified likelihood function allows the proposed technique to update and maintain the target belief, regardless of the target detectability. For unified search and tracking (SAT), the proposed technique further predicts the belief in a finite-time horizon, and decides control actions by maximizing a unified objective function consisting of local and global measures derived from the predicted belief. Using the objective function, the proposed technique can smoothly change its control actions even during transitions between SAT. The numerical results first show successful SAT by the proposed technique in tests using a sensor platform with different detectability and comparison with conventional searching techniques under different prior knowledge, and then identifies the superiorities of the proposed technique in SAT. The experimental results finally validate the applicability and extendability of the proposed technique via coordinated SAT in a field experiment.     

Cooperative distributed target tracking algorithm in mobile wireless sensor networks

The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...     

多站时差无源定位传感器网络优化

在可移动无源传感器网络中,观测器与目标的相对几何关系对定位精度有重要影响。为提高对运动目标的定位跟踪精度,提出一种基于时差无源定位几何稀释精度的移动平台实时布站方法。首先推导出二维时差无源定位方法下的带有基线长度和基线偏角的GDOP表达式,将其作为目标优化函数,使用加权离散搜索优化算法求解网内各观测器每一时刻的最佳观测位置,并在此最佳位置对目标进行量测,完成目标运动分析。该方法通估计和优化相结合实现移动平台无源传感器网络的实时优化部署,仿真证明该算法一定程度上解决了时差无源定位算法的定位模糊问题,提高了对运动目标的跟踪精度。     

基于CPCRLB和一致性算法的分散式传感器管理

针对无线传感器网络中的目标跟踪问题,基于条件后验克拉美—罗下界（CPCRLB）提出一种分散式传感器节点管理方法．基于一致性策略给出一种CPCRLB的分布式迭代算法,并且基于分布式粒子滤波器给出该算法的数值逼近实现．对层次结构的无线传感器网络,将CPCRLB作为传感器管理的准则,基于平均一致性给出一种迭代的局部搜索算法,实现了无线传感器网络下观测节点的分散式在线选择．仿真结果表明了基于CPCRLB的分散式传感器管理方法在目标跟踪精度方面的有效性．     

FCM-AFSA的无线传感器网络多目标跟踪节点任务分配方法

多目标跟踪是无线传感器网络重要应用之一。提出了基于离散人工鱼群算法的无线传感器网络多目标跟踪节点任务分配方法。该方法首先利用类间距阈值的模糊C均值聚类算法,估计监测区域可能出现的目标数量和目标位置;再根据任务分配的目标函数,使用改进的离散人工鱼群算法优化目标函数,从而得到任务分配方案,并同其他算法进行比较。仿真实验结果表明,该方法比最近邻方法、MEM方法以及粒子群算法的能耗有所降低,任务分配时间比最近邻方法、MEM方法以及粒子群算法有所减少。因此,所提出的改进算法能有效地提高无线传感器网络的综合性能,满足实际应用的需求。     

Distributed estimation for adaptive sensor selection in wireless sensor networks

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.     

Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise

This article addresses the problem of tracking a manoeuvring target in a wireless sensor network (WSN) consisting of distance-measuring sensor nodes. In order to cope with target manoeuvres, an interacting multiple model (IMM) filter is applied to estimate the position and velocity of the target. The distance-dependent measurement error of sensors is formulated as both additive and multiplicative noise in the observation equation. To deal with nonlinearities in the process and observation equations and also to solve the problem of multiplicative measurement noise, a new particle filter (PF)-based IMM approach is developed. Furthermore, the multiple-model posterior Cramér-Rao lower bound (PCRLB) is derived in the presence of both additive and multiplicative noise and it is used to perform a sensor selection algorithm to reduce energy consumption in WSN nodes. Simulation results show the effectiveness of the proposed IMMPF and sensor selection algorithms in target tracking.