摘要
针对水声传感器网络中移动定位算法的误差和鲁棒性问题,提出两种蒙特卡罗移动定位算法:CRMCL(Circular Ring Monte Carlo Localization)和PRMCL(Particle Swarm Optimization for Circular Ring Monte Carlo Localization).CRMCL利用1跳锚节点构建圆形采样区域和圆环过滤器.通过定义样本密度得到合理的样本数,论证圆环参数与过滤区域面积的关系.通过仿真实验得到合理的圆环参数,并以此构建高效的过滤器,降低定位误差.PRMCL使用粒子群算法优化CRMCL过滤后的样本,降低了无效样本的数目,增强了算法的鲁棒性.仿真表明,在不需要额外硬件的情况下,CRMCL和PRMCL比蒙特卡罗及其改进算法误差小、鲁棒性强.
Aiming at error and robustness of localization algorithms in underwater acoustic sensor networks,we proposed two monte carlo mobile localization algorithms:circular ring monte carlo localization(CRMCL)and particle swarm optimization for circular ring monte carlo localization(PRMCL).CRMCL used one-hop anchor nodes to construct circular sampling area and ring filter.By defining sampling points density,we obtained reasonable sample number.The relationship between the ring parameter and the filtration area was demonstrated.Then the reasonable ring parameter was obtained through the simulation experiments,and an efficient filter was constructed to reduce the localization error.PRMCL used particle swarm optimization(PSO)algorithm to optimize the samples filtered by CRMCL,which reduced the number of invalid samples and improved the robustness of localization.The simulation results show that CRMCL and PRMCL have lower localization error and better robustness than monte carlo localization and other improved algorithms without additional hardware.
作者
郝诗雅
杨媛媛
董怡靖
赵晓旭
陈嘉兴
HAO Shi-ya;YANG Yuan-yuan;DONG Yi-jing;ZHAO Xiao-xu;CHEN Jia-xing(College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang,Hebei 050024,China;School of Mathematical Sciences,Hebei Normal University,Shijiazhuang,Hebei 050024,China;College of Engineering,Hebei Normal University,Shijiazhuang,Hebei 050024,China;Shijiazhuang Vocational College of Scientific and Technical Engineering,Shijiazhuang,Hebei 050800,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2021年第2期292-299,共8页
Acta Electronica Sinica
基金
国家自然科学基金(No.61771181,No.61701165,No.62071167)。
关键词
水声传感器网络
移动定位
蒙特卡罗
粒子群算法
圆环过滤器
underwater acoustic sensor networks
mobile localization
monte carlo localization(MCL)
particle swarm optimization(PSO)
ring filter
