基于神经网络的UWB室内定位算法

定位技术的迅速发展,使它渗透到了人们的生产生活中,因此,对定位技术的完善和提升变得尤为重要.一些传统定位技术,如红外线、超声波、蓝牙、RFID等,由于室内环境的复杂性,导致定位精度有所损失.UWB作为一种无载波通信技术,其诸多优点可以使它在视距传输中获得良好的定位效果,但是实际环境中的非视距传输,会使其受到影响而产生误差.采用TOA定位方法,辅以神经网络算法,可在寻求优化解的过程中不断减小误差,实验仿真表明,该算法在复杂的室内传输中具有较高的定位精度.

Application of Neural Dynamic Algorithm for Ultra Wide Band Indoor Positioning Systems

The indoor positioning technologies are developing rapidly as the requirements of people are changing constantly. However, traditional positioning technologies, like infrared, ultrasound, Bluetooth and RFID, cannot play a significant role in real-time indoor positioning. Ultra wide band indoor positioning technology, a non-carrier communication technology, is used in line-of sight transmission research for its many advantages. To address the errors induces by the obstacles in non-line-of-sight transmission, this paper proposes the neural dynamic algorithm to optimize the indoor positioning problem. The simulation results also show that the proposed algorithm can obtain high positioning accuracy in complex indoor transmission environment.