【作者】 李峰荣；

【导师】 刘贵喜；

【作者基本信息】 西安电子科技大学 ， 控制理论与控制工程， 2010， 硕士

【摘要】 无线传感器网络有望在很多领域取得广泛的应用,包括环境监测、智能农业、目标探测和跟踪、机器人应用等。并且该技术有望嵌入到因特网中,实现外部世界与人类社会的信息交换,拓宽人类获取信息的方式。无线传感器网络因其低功耗、低成本、自组织性、可扩展性、隐蔽性等优点而将在监视和跟踪领域发挥越来越重要的作用。本文首先介绍了无线传感器网络的组成、拓扑结构、路由协议、应用前景等方面的知识。接着描述了无线传感器网络中常用的节点定位算法。使用多维标度定位算法对无线传感器网络中的节点进行定位,并且根据不同的仿真参数给出定量的分析结果。最后,给出了扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和粒子滤波算法的推导过程和仿真分析。针对无线传感器网络中的多传感器融合目标跟踪,提出一种混合滤波算法,称为扩展混合粒子滤波算法(EM-PF)。该算法使用了一个混合的粒子传播方案。在使用粒子滤波算法对无线传感器网络中的节点测量信息进行融合时,粒子滤波器中的一部分粒子使用从扩展卡尔曼滤波状态传播获得的高斯分布作为建议分布进行粒子传播,而剩余的另一部分粒子则简单地使用状态转移先验分布进行粒子传播。无线传感器网络中的融合跟踪仿真结果表明,和纯粒子滤波算法相比,在仿真速率相当的情况下,混合滤波算法明显提高了跟踪精度和稳定性。更多还原

【Abstract】 Wireless sensor networks are expected to find a wide variety of applications in many areas, including environmental monitoring, intelligent agriculture, target detection and tracking, robotics applications and so on. The technology of wireless sensor networks is expected to be embedded into the Internet, so as to facilitate information exchange between the external world and human beings, and broaden the way people seek information. Because of the merit of low power consumption, low cost, high capacity of self-organizing, scalability and imperceptibility, wireless sensor networks will play a more and more important role in the field of surveillance and tracking.This thesis gives an introduction of the knowledge of wireless sensor networks’ components, architecture, routing protocols, application prospects and so on at the beginning. After that, some node localization algorithms frequently used in wireless sensor networks are described. Multidimentional scaling map algorithm is employed to achieve node localization in wireless sensor networks, and quantitative analysis depends on different simulation parameters is provided. Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented. Aiming at multisensor fusion based target tracking applications in wireless sensor networks, a mixed algorithm is proposed, called extended-mixed particle filter(EM-PF). The algorithm utilizes a mixed particle propagation scheme. In the process of multi-sensor measurement fusion using a particle filter in a wireless sensor network, a certain number of particles in the particle filter are propagated by using a Gaussian distribution obtained from an extended Kalman filter as the proposal distribution, while the rest of the particles are simply propagated by using the state transition prior distribution. Simulation results of fusion-tracking in wireless sensor networks show that, with a similar simulation speed, the mixed filter significantly increases the tracking accuracy and robustness as compared with the results obtained by the pure particle filter.更多还原