面向消防现场的无线传感器.doc
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面向消防现场的无线传感器,摘要近年来，各种自然灾害与人为灾害频繁发作，而复杂的灾害现场对救援提出了更高要求。建立消防现场监控系统可为消防现场指挥调度和救援提供依据,而对监控节点和消防员的定位则是建立消防现场监控系统的一个关键问题。随着无线传感器网络（wsn）在众多场合的成功应用，将便携式wsn节点快速部署在消防现场使现场情况的监测和对消防员的定...
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摘 要
近年来，各种自然灾害与人为灾害频繁发作，而复杂的灾害现场对救援提出了更高要求。建立消防现场监控系统可为消防现场指挥调度和救援提供依据,而对监控节点和消防员的定位则是建立消防现场监控系统的一个关键问题。随着无线传感器网络（WSN）在众多场合的成功应用，将便携式WSN节点快速部署在消防现场使现场情况的监测和对消防员的定位成为可能。而在运用WSN实现定位的过程中，必须结合具体应用特点解决相关关键问题。
从消防现场环境复杂的特点出发，结合无线通信的传播特性，构建基于超宽带技术的消防现场WSN，改进了基于接收信号强度（RSSI）的加权质心定位算法，并对定位结果进行优化。主要研究内容如下：
（1）针对室内和野外两种应用场合，分别建立两种超宽带信道衰减模型，分析测距误差情况；并针对室内测距误差大的特点，提出采用信标节点相互定位的方法校正测距误差，研究结果表明，该方法可有效保证测距精度。
（2）针对加权质心定位算法简单易行，但定位精度低的问题，在有效提高测距精度的前提下，改进了基于RSSI的加权质心定位算法，并分别采用粒子群算法和量子粒子群算法对定位结果进行优化。研究结果表明，采用粒子群算法和量子粒子群算法优化后的加权质心定位算法定位精度较高，并且不易受环境因素的影响，可以有效提高定位精度和稳定度。
（3）针对信标节点需求过多造成的高成本问题，采用递增式定位过程，在信标节点数量不足时，将已定位的未知节点转化为次级信标节点参与定位，有效地减少定位中信标节点的数量，降低成本。仿真结果表明：在粒子群优化或量子粒子群优化的加权质心定位算法的基础上，在同等定位率的情况下，定位过程采用递增式，可有效减少信标节点数。
关键词 消防现场；超宽带；无线传感器网络；定位；优化算法
Abstract
In recent years, various natural and man-made disasters occurred frequently; complex disaster field put forward a higher requirement for rescue. Establish the fire monitoring system provides a basis for fire fighting field command scheduling and rescue, and localization for monitor node and fireman is the key issue to establish the fire monitoring system.
With the rapid development of wireless sensor network (WSN) successfully applied on numerous occasions, the fire monitoring and the localization have become possible because of the portable WSN node rapidly deploying in fire site. However, in the use of WSN to achieve the fireman positioning process must be combined with specific application characteristics to solve relate problems.
In the light of characteristics of the complex fire scene environment and wireless communication propagation characteristics, established the fire site WSN based on UWB technology, improved the weighted cancroids localization algorithm based on the received signal strength (RSSI), and optimized the positioning results. The main research contents are as follows:
(1)According to the different environment of fire happened, considering the fire scene include the indoor and outdoor environment, modeling the fire scene ultra-wideband communication channel model respectively, analyzed the ranging error of UWB channel loss model under various environments. In view of the problem of big ranging and localization error under industrial environment, beacon node mutual positioning and ranging error correction method was proposed, research results shows that this method effectively ensure the distance measurement accuracy.
(2)In view of the problem of weighted cancroids localization algorithm is simple and easy to realize, but have lower accuracy; in the premise of improving the ranging accuracy efficiently, improved the weighted cancroids localization algorithm based on RSSI ranging, the particle swarm algorithm and quantum-behaved particle swarm optimization positioning were applied to optimize the positioning results, research results shows that this method has higher accuracy, And are not easily affected by environmental factors, could improve the localization accuracy and stability.
(3)Incremental positioning process was proposed to overcome the problem of high cost caused by the demanding of more beacon nodes. When there was not enough beacon nodes to localization, the nodes of which were unknown nodes turned into secondary beacon node participated in localization, decreased the beacon nodes number efficiently, and reduced the cost. Simulation results shows that at the basis of the particle swarm and quantum-behaved particle swarm optimization weighted cancroids localization algorithm， at the condition of the same localization rate, incremental method was applied during the positioning could reduce the beacon nodes efficiently.
Key Words The fire scene; ultra-wideband; wireless sensor networks; positioning; optimization algorithm
目 录
摘 要 I
ABSTRACT III
第1章 绪论 1
1.1背景及意义 1
1.2 国内外定位技术研究现状 2
1.3 论文研究思路及内容安排 4
第2章 面向消防现场的WSN定位系统整体设计 6
2.1 引言 6
2.2 消防现场的特点 6
2.3 无线传感器网络的结构和特点 7
2.4 面向消防现场的WSN定位系统整体设计 8
2.5 面向消防现场定位系统关键技术 10
2.5.1 超宽带技术 10
2.5.2 测距技术 13
2.5.3 定位技术 16
2.6 小结 20
第3章 基于RSSI的加权质心定位算法及..
近年来，各种自然灾害与人为灾害频繁发作，而复杂的灾害现场对救援提出了更高要求。建立消防现场监控系统可为消防现场指挥调度和救援提供依据,而对监控节点和消防员的定位则是建立消防现场监控系统的一个关键问题。随着无线传感器网络（WSN）在众多场合的成功应用，将便携式WSN节点快速部署在消防现场使现场情况的监测和对消防员的定位成为可能。而在运用WSN实现定位的过程中，必须结合具体应用特点解决相关关键问题。
从消防现场环境复杂的特点出发，结合无线通信的传播特性，构建基于超宽带技术的消防现场WSN，改进了基于接收信号强度（RSSI）的加权质心定位算法，并对定位结果进行优化。主要研究内容如下：
（1）针对室内和野外两种应用场合，分别建立两种超宽带信道衰减模型，分析测距误差情况；并针对室内测距误差大的特点，提出采用信标节点相互定位的方法校正测距误差，研究结果表明，该方法可有效保证测距精度。
（2）针对加权质心定位算法简单易行，但定位精度低的问题，在有效提高测距精度的前提下，改进了基于RSSI的加权质心定位算法，并分别采用粒子群算法和量子粒子群算法对定位结果进行优化。研究结果表明，采用粒子群算法和量子粒子群算法优化后的加权质心定位算法定位精度较高，并且不易受环境因素的影响，可以有效提高定位精度和稳定度。
（3）针对信标节点需求过多造成的高成本问题，采用递增式定位过程，在信标节点数量不足时，将已定位的未知节点转化为次级信标节点参与定位，有效地减少定位中信标节点的数量，降低成本。仿真结果表明：在粒子群优化或量子粒子群优化的加权质心定位算法的基础上，在同等定位率的情况下，定位过程采用递增式，可有效减少信标节点数。
关键词 消防现场；超宽带；无线传感器网络；定位；优化算法
Abstract
In recent years, various natural and man-made disasters occurred frequently; complex disaster field put forward a higher requirement for rescue. Establish the fire monitoring system provides a basis for fire fighting field command scheduling and rescue, and localization for monitor node and fireman is the key issue to establish the fire monitoring system.
With the rapid development of wireless sensor network (WSN) successfully applied on numerous occasions, the fire monitoring and the localization have become possible because of the portable WSN node rapidly deploying in fire site. However, in the use of WSN to achieve the fireman positioning process must be combined with specific application characteristics to solve relate problems.
In the light of characteristics of the complex fire scene environment and wireless communication propagation characteristics, established the fire site WSN based on UWB technology, improved the weighted cancroids localization algorithm based on the received signal strength (RSSI), and optimized the positioning results. The main research contents are as follows:
(1)According to the different environment of fire happened, considering the fire scene include the indoor and outdoor environment, modeling the fire scene ultra-wideband communication channel model respectively, analyzed the ranging error of UWB channel loss model under various environments. In view of the problem of big ranging and localization error under industrial environment, beacon node mutual positioning and ranging error correction method was proposed, research results shows that this method effectively ensure the distance measurement accuracy.
(2)In view of the problem of weighted cancroids localization algorithm is simple and easy to realize, but have lower accuracy; in the premise of improving the ranging accuracy efficiently, improved the weighted cancroids localization algorithm based on RSSI ranging, the particle swarm algorithm and quantum-behaved particle swarm optimization positioning were applied to optimize the positioning results, research results shows that this method has higher accuracy, And are not easily affected by environmental factors, could improve the localization accuracy and stability.
(3)Incremental positioning process was proposed to overcome the problem of high cost caused by the demanding of more beacon nodes. When there was not enough beacon nodes to localization, the nodes of which were unknown nodes turned into secondary beacon node participated in localization, decreased the beacon nodes number efficiently, and reduced the cost. Simulation results shows that at the basis of the particle swarm and quantum-behaved particle swarm optimization weighted cancroids localization algorithm， at the condition of the same localization rate, incremental method was applied during the positioning could reduce the beacon nodes efficiently.
Key Words The fire scene; ultra-wideband; wireless sensor networks; positioning; optimization algorithm
目 录
摘 要 I
ABSTRACT III
第1章 绪论 1
1.1背景及意义 1
1.2 国内外定位技术研究现状 2
1.3 论文研究思路及内容安排 4
第2章 面向消防现场的WSN定位系统整体设计 6
2.1 引言 6
2.2 消防现场的特点 6
2.3 无线传感器网络的结构和特点 7
2.4 面向消防现场的WSN定位系统整体设计 8
2.5 面向消防现场定位系统关键技术 10
2.5.1 超宽带技术 10
2.5.2 测距技术 13
2.5.3 定位技术 16
2.6 小结 20
第3章 基于RSSI的加权质心定位算法及..
