基于因子图和联合消息传递的无线网络协作定位算法  

Localization algorithm based on factor graph and hybrid message passing for wireless networks

作　　者：崔建华[1,2] 王忠勇[3] 张传宗[3] 张园园[3] CUI Jianhua WANG Zhongyong ZHANG Chuanzong ZHANG Yuanyuan(National Digital Switching System Engineering ＆ Technological Research Center, Zhengzhou Henan 450002, China School of Physics and Electronic Information, Lnoyang Normal University, Lm~ang Henna 471934, China School of Information Engineering, Zhengzhou University, Zhengzhou Henan 450001, China)

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002 [2]洛阳师范学院物理与电子信息学院,河南洛阳471934 [3]郑州大学信息工程学院,郑州450001

出　　处：《计算机应用》2017年第5期1306-1310,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61571402;61401401)~~

摘　　要：针对现有基于消息传递算法的无线网络节点定位算法复杂度和通信开销过高的问题,提出一种基于测距的、低复杂度低协作开销的联合消息传递节点定位算法。所提算法考虑参考节点位置的不确定性以减少误差累积,并将消息约束为高斯函数以降低通信开销。首先,根据系统的概率模型和因子分解设计因子图;然后,根据状态转移模型和测距模型的特点,分别使用置信传播和平均场方法计算预测消息和协作消息;最后,在每次迭代过程中,通过非线性项的泰勒展开将非高斯置信消息近似为高斯函数。仿真分析表明,所提算法的定位性能与基于粒子的SPAWN算法接近,但节点间传输的信息由大量粒子变为均值向量和协方差矩阵,同时计算复杂度也大幅降低。Concerning the high computational complexity and communication overhead of wireless network node localization algorithm based on message passing algorithm, a ranging-based hybrid message passing node localization method with low complexity and cooperative overhead was proposed. The uncertainty of the reference nodes was taken into account to avoid error accumulation, and the messages on factor graph were restricted to be Gaussian distribution to reduce the communication overhead. Firstly, the factor graph was designed based on the system model and the Bayesian factorization. Secondly, belief propagation and mean filed methods were employed according to the linear state transition model and the nonlinear ranging model to calculate the prediction messages and the cooperation messages, respectively. Finally, in each iteration, the non-Gaussian beliefs were approximated into Gaussian distribution by Taylor expansions of the nonlinear terms. The simulation results show that the positioning accuracy of the proposed algorithm is eompareable to that of Sum-Product Algorithm over a Wireless Network （SPAWN）, but the information transmitted between nodes decreases from a large number of particles to mean vector and eovariance matrix, and the eomupational complexity is also dramatically reduced.

关 键 词：近似贝叶斯推理 因子图 置信传播 平均场方法 无线传感器网络 协作定位 

分 类 号：TN911.23[电子电信—通信与信息系统]