移动无线传感网络的分布式协作定位的研究

针对移动无线传感网络,并基于到达时间TOA(Time-of-Arrival)的测距模型,分析利用传感节点的移动信息进行定位问题,提出基于二阶锥规划的分布式定位算法。推导最大似然ML(Maximum Likelihood)定位估计表达式;考虑到基于ML的协作定位的非凸性,选用二阶锥规划SOCP(Second-order Cone Programming)松驰技术求解。为了降低计算成本,采用分布式策略实施SOCP算法。实验数据表明,该算法减少了均方根误差,提高了定位精度。

DISTRIBUTED COOPERATIVE LOCALIZATION FOR MOBILE WIRELESS SENSOR NETWORK

For mobile wireless sensor network, we studied the problem of localization using mobile information of sensor nodes based on the time-of-Arrival(TOA) model. Then we proposed distributed localization algorithm based on second-order cone programming. The expression of maximum likelihood(ML) location estimation was derived. Considering the non-convexity of ML-based cooperative localization, we used the second-order cone programming(SOCP) relaxation technique to solve the problem. In order to reduce the computing cost, a distributed strategy was adopted to implement the SOCP algorithm. The experimental data show that the algorithm reduces the root mean square error and improves the localization accuracy.