基于BP神经网络的实时动态参数定位方法

Real-time positioning method with dynamic parameters based on BP neural network

为减小测距技术中的非视距误差并解决定位模型中存在的问题,提出一种实时动态参数定位方法。基于人工神经网络算法,利用多个参考节点获取的测量值的非视距(NLOS)误差,使测量值RSSI接近视距(LOS)环境下的测量值;通过该区域内选定的参考节点之间的相互通信实时动态地估算出环境参数值。实验结果表明,该算法缩减了在RSSI测距技术中的非视距误差,并能根据实际环境条件实时动态地调整定位模型的参数,有效提高定位精度。

To minimize none line of sight error in ranging techniques and to solve problems in locating method ,a novel locating method based on real-time dynamic parameters was presented .The artificial neural network algorithm was utilized for correcting measured value’s non-line of sight error produced by multiple reference nodes ,which made the processed RSSI value close to the RSSI value measured by stadia .And the environmental parameters were estimated real-timely through the communication be-tween the reference nodes in the selected region .Experimental results show that this algorithm not only reduces the NLOS er-ror ,but also adjusts the transmission parameters of the model dynamically according to the actual environmental conditions .It improves the positioning accuracy effectively .