摘要
针对蒙特卡罗定位算法在无线传感网络中定位精度和取样效率不高的问题,提出一种基于高斯-马尔科夫移动模型改进的蒙特卡罗定位算法.通过分析车间移动资源的移动规律,引入高斯移动模型预测,减少取样区域,优化滤波算法,提高取样效率和定位精度.仿真结果表明,当节点移动时,该算法的取样效率和定位精度均有所提高.
To improve the location accuracy and sampling efficiency of Monte Carlo localization algorithm in wireless sensor network,an improved Monte Carlo location algorithm based on Gauss Markov moving model is proposed.By analyzing the moving rules of the mobile resources in the workshop,the Gauss mobile model is introduced to predict the sampling area,optimize the filtering algorithm,and improve the sampling efficiency and positioning accuracy.Simulation results show that the sampling efficiency and location accuracy of the proposed algorithm are improved when the node moves.
出处
《淮海工学院学报（自然科学版）》
CAS
2017年第3期20-24,共5页
Journal of Huaihai Institute of Technology:Natural Sciences Edition
基金
广东省高等职业技术教育研究会立项课题(GDGZ16Y079)
中国职业技术教育学会教学工作委员会研究课题(1710570)
云浮市科技局科技计划项目(201702-2)
关键词
移动节点
蒙特卡罗定位算法
取样效率
定位精度
mobile node
Monte Carlo location algorithm
sampling efficiency
location accuracy
