基于PMCL方法的大规模无线传感器网络移动定位机制

A mobile localization scheme based on PMCL method for large-scale WSNs

针对传统的蒙特卡洛(MC)移动定位方法迭代次数较多、样本数量易衰减、观测值多样性差等缺点,提出了改进的群蒙特卡洛定位(PMCL)机制:采用两重限制手段降低迭代次数;采用蒙特卡洛群对重采样进行精确匹配。基于节点密度、锚节点分布比例、样本数量等参数,对PMCL机制进行理论分析和实验仿真。PMCL机制与其他的基于序列蒙特卡洛(SMC)机制进行性能比较。结果表明:PMCL机制的延时性能得到了较大提高,且在节点移动速度较低、节点密度较大的情况下,其定位准确度也优于其他的定位机制。

Aiming at shortcomings of more iterative times,sample size tends to decay and diversity of observed values is poor of traditional Monte Carlo（ MC） mobile localization,an improved localization method for mobile wireless sensor networks（ WSNs） based on population MC localization（ PMCL） method is proposed. Two-constrains are proposed to decrease iterative times and PMC is introduced to match resampling precisely. Based on node density,anchor node distribution proportion and sample number and other parameters,theoretical analysis and experimental simulation on PMCL scheme are carried out. Performance comparisons of PMCL with other sequential Monte Carlo（ SMC）-based schemes are carried out. Simulation results show that delay of PMCL scheme has some superiority to those of other schemes,and accuracy is improved in some cases of lower node moving velocity and larger node density.