基于改进的洪泛广播和粒子滤波的无线传感器网络节点定位  

Node localization based on improved flooding broadcast and particle filtering in wireless sensor network

作　　者：赵海军[1] 崔梦天[2] 李明东[1] 李佳[1] 

机构地区：[1]西华师范大学计算机学院,四川南充637009 [2]西南民族大学计算机科学与技术学院,成都610041

出　　处：《计算机应用》2016年第10期2659-2663,2669,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61379019);西华师范大学基本科研业务费专项基金资助项目(14C002)~~

摘　　要：针对目前移动无线传感器网络定位问题存在的不足,提出了一种基于改进的洪泛广播机制和粒子滤波的节点定位算法。对于一个给定的未知节点,首先采用改进的洪泛广播机制,从离它最近的锚节点得到的有效平均跳距来计算出它到它的所有邻居节点的距离。然后采用一种差分误差校正算法,以减小平均跳距中由于多跳累积造成的测量误差;其次,采用粒子滤波和虚拟锚节点来减小预测区域,得到更有效的粒子预测区域,从而进一步减小对未知节点位置的估计误差。仿真结果表明,所提算法与定位算法DV-Hop、蒙特卡罗Baggio(MCB)和基于测试的蒙特卡罗定位(MCL)相比,能够有效地抑制冗余广播和减小与节点定位相关的消息开销,以较低的通信成本实现较高精度的定位性能。Aiming at the shortage of current mobile Wireless Sensor Network （WSN） localization, a localization algorithm based on improved flooding broadcast mechanism and particle filtering was proposed. For a given unknown node, firstly, by the improved flooding broadcast mechanism, the effective average hop distance of an unknown node from its closest anchor node was used to calculate the distances to its all neighbor nodes. Then a differential error correction scheme was devised to reduce the measurement error accumulated over multiple hops for the average hop distance. Secondly, the particle filter and the virtual anchor node were used to narrow the prediction area, and more effective particle prediction area was obtained so as to further decrease the estimation error of the position of unknown node. The simulation results show that compared with DV-Hop, Monte Carlo Baggio （MCB） and Range-based Monte Carlo Localization （MCL） algorithms, the proposed positioning algorithm can effectively inhibit the broadcast redundancy and reduce the message overhead related to the node localization, and can achieve higher-accuracy positioning performance with lower communication cost.

关 键 词：移动无线传感器网络 广播机制 粒子滤波 节点定位 通信开销 估计误差 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]