能量平衡的无线传感器网络目标跟踪研究
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摘要
无线传感器网络（Wireless Sensor Networks, WSNs）是一种由大量低成本无线连接的传感器组成的自组织网络。WSNs作为一种现代物理网络系统的使能技术，能在各种感知区域内长时间地提供高精度的时空监测，已广泛应用于民用与军事中，如目标跟踪、基础设施监控、生态环境监测和战场监视等。可是，由于传感器节点有限的能量、计算、通信和处理能力，WSNs必须依靠动态地管理和调度网络资源来进行协同信号与信息处理以延长网络寿命和降低维护成本。
本课题以WSNs中的目标跟踪为应用背景，从目标跟踪信号处理以及节点资源调度两方面展开研究工作。
1）针对无线传感器网络中测量噪声与目标距离相关的跟踪问题，引入一个具有乘性噪声的距离测量模型，据此开发一种基于贝叶斯估计的目标跟踪算法。其次针对具有区域性的感知能力的有向传感器，如摄像头/视频传感器、超声传感器和红外传感器，根据有向传感器的原始测量值和目标感知及本身的方向信息，开发一种有向传感器网络的分布式目标跟踪算法。
2）针对无线传感器网络能量不平衡影响网络寿命的问题，引入一个新的基于节点剩余能量标准差的能量平衡指标，据此提出了一种能量平衡的最优分布式成簇机制，将其转化为多目标约束优化问题，并采用具有最佳个体保存机制的自适应遗传算法求解最优解。
3）针对无线传感网络NP-hard节点调度问题，基于距离测量模型下UKF跟踪算法，定义了一个新的网络寿命概念，并建立了其与能量平衡指标的理论关系，据此开发一种启发式的多项式复杂度的能量平衡调度算法，能够保障期望的目标跟踪精度，同时显著地延长无线传感器网络的寿命。其次针对测量噪声与目标距离相关的跟踪问题，一些启发式能量平衡的调度算法也被提出。
4）针对无线传感器网络多目标跟踪中节点调度问题，采用三点定位跟踪算法，建立一种基于面积和法的精度模型。通过将能耗模型与精度模型结合，构造出综合性能指标函数，指导节点调度，并运用遗传算法实现此选择。最后对多目标跟踪中复杂目标运动模型、测量模型和其相关贝叶斯统计估计进行展望。
Wireless sensor network (WSN) consists of a large number of low cost sensor nodesinterconnected via wireless channels to form an ad hoc wireless network. The WSN tech-nology promises high resolution spatial and temporal monitoring of expansive sensingfeld over extended duration, and hence is an enabling technology of modern cyber physi-cal systems. WSN has found numerous important applications including target tracking,infrastructure monitoring, habitat sensing, and battlefeld surveillance. Due to limitedon-board battery energy reserve, computation, processing and communication capabili-ties, WSNs must rely on collaborative signal and information processing to dynamicallymanage sensor resources and efectively process distributed information in order to pro-long the operating life-span and to reduce maintenance cost.
This PH.D dissertation is based on the target tracking application in WSNs, focuseson the target tracking signal processing and nodes resource scheduling.
1) For target tracking with distance-dependent measurement noise in wireless sensornetworks, a distance-dependent measurement error of sensors is modeled as a multiplica-tive noise. Then, a novel bayesian target tracking algorithm is proposed. To deal withtracking in directional sensors of a special angle of sensing range, like cameras/videosensors, infrared sensors, ultrasonic sensors, based on the original measurements anddirectional sensing range information of directional sensors that detect the target, a dis-tributed target tracking algorithm in directional sensor networks is proposed.
2) As the energy imbalance of wireless sensor networks has an impact on the networklifetime, a novel energy-balanced optimal distributed clustering mechanism is proposedby introducing a new energy-balanced metric based on the standard deviation of residualenergy of nodes. Then, it is transformed into a multi-objective constrained optimizationproblem, and an adaptive genetic algorithm with Elitist mechanism is employed to resolvethis problem.
3) The nodes scheduling in WSNs is a NP-Hard problem, based on a sensor-to-targetdistance estimation observation UKF tracking algorithm, a novel task-specifc defnitionof the network lifespan is given, and the theoretical connection between the energy balancemetric with WSN lifespan is established. Then, a novel, polynomial time heuristic energy balanced nodes scheduling method is proposed that are capable of delivering desiredtracking performance while signifcantly extending the WSNs lifespan. To deal with targettracking with distance-dependent measurement noise in WSNs, some similar heuristicenergy balanced nodes scheduling algorithms are presented.
4) For nodes scheduling in multi-target tracking of WSNs, a three point locationis adopted, and a location accuracy model based on area sum method is constructed.Combining the energy consumption model and the location accuracy model, the compre-hensive performance index function was created to guide nodes scheduling, and geneticalgorithm was used to implement the selection. Finally, the outlook about complex targetmotions and observation models as well as its corresponding bayesian statistical estima-tions is reviewed.
本课题以WSNs中的目标跟踪为应用背景，从目标跟踪信号处理以及节点资源调度两方面展开研究工作。
1）针对无线传感器网络中测量噪声与目标距离相关的跟踪问题，引入一个具有乘性噪声的距离测量模型，据此开发一种基于贝叶斯估计的目标跟踪算法。其次针对具有区域性的感知能力的有向传感器，如摄像头/视频传感器、超声传感器和红外传感器，根据有向传感器的原始测量值和目标感知及本身的方向信息，开发一种有向传感器网络的分布式目标跟踪算法。
2）针对无线传感器网络能量不平衡影响网络寿命的问题，引入一个新的基于节点剩余能量标准差的能量平衡指标，据此提出了一种能量平衡的最优分布式成簇机制，将其转化为多目标约束优化问题，并采用具有最佳个体保存机制的自适应遗传算法求解最优解。
3）针对无线传感网络NP-hard节点调度问题，基于距离测量模型下UKF跟踪算法，定义了一个新的网络寿命概念，并建立了其与能量平衡指标的理论关系，据此开发一种启发式的多项式复杂度的能量平衡调度算法，能够保障期望的目标跟踪精度，同时显著地延长无线传感器网络的寿命。其次针对测量噪声与目标距离相关的跟踪问题，一些启发式能量平衡的调度算法也被提出。
4）针对无线传感器网络多目标跟踪中节点调度问题，采用三点定位跟踪算法，建立一种基于面积和法的精度模型。通过将能耗模型与精度模型结合，构造出综合性能指标函数，指导节点调度，并运用遗传算法实现此选择。最后对多目标跟踪中复杂目标运动模型、测量模型和其相关贝叶斯统计估计进行展望。
Wireless sensor network (WSN) consists of a large number of low cost sensor nodesinterconnected via wireless channels to form an ad hoc wireless network. The WSN tech-nology promises high resolution spatial and temporal monitoring of expansive sensingfeld over extended duration, and hence is an enabling technology of modern cyber physi-cal systems. WSN has found numerous important applications including target tracking,infrastructure monitoring, habitat sensing, and battlefeld surveillance. Due to limitedon-board battery energy reserve, computation, processing and communication capabili-ties, WSNs must rely on collaborative signal and information processing to dynamicallymanage sensor resources and efectively process distributed information in order to pro-long the operating life-span and to reduce maintenance cost.
This PH.D dissertation is based on the target tracking application in WSNs, focuseson the target tracking signal processing and nodes resource scheduling.
1) For target tracking with distance-dependent measurement noise in wireless sensornetworks, a distance-dependent measurement error of sensors is modeled as a multiplica-tive noise. Then, a novel bayesian target tracking algorithm is proposed. To deal withtracking in directional sensors of a special angle of sensing range, like cameras/videosensors, infrared sensors, ultrasonic sensors, based on the original measurements anddirectional sensing range information of directional sensors that detect the target, a dis-tributed target tracking algorithm in directional sensor networks is proposed.
2) As the energy imbalance of wireless sensor networks has an impact on the networklifetime, a novel energy-balanced optimal distributed clustering mechanism is proposedby introducing a new energy-balanced metric based on the standard deviation of residualenergy of nodes. Then, it is transformed into a multi-objective constrained optimizationproblem, and an adaptive genetic algorithm with Elitist mechanism is employed to resolvethis problem.
3) The nodes scheduling in WSNs is a NP-Hard problem, based on a sensor-to-targetdistance estimation observation UKF tracking algorithm, a novel task-specifc defnitionof the network lifespan is given, and the theoretical connection between the energy balancemetric with WSN lifespan is established. Then, a novel, polynomial time heuristic energy balanced nodes scheduling method is proposed that are capable of delivering desiredtracking performance while signifcantly extending the WSNs lifespan. To deal with targettracking with distance-dependent measurement noise in WSNs, some similar heuristicenergy balanced nodes scheduling algorithms are presented.
4) For nodes scheduling in multi-target tracking of WSNs, a three point locationis adopted, and a location accuracy model based on area sum method is constructed.Combining the energy consumption model and the location accuracy model, the compre-hensive performance index function was created to guide nodes scheduling, and geneticalgorithm was used to implement the selection. Finally, the outlook about complex targetmotions and observation models as well as its corresponding bayesian statistical estima-tions is reviewed.
引文
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