非视距环境下的超宽带室内定位算法

为了进一步提高超宽带技术在非视距室内环境中的定位精度，研究了抑制非视距误差的定位算法。首先，对非视距环境下的TDOA定位模型进行重构；其次，推导出非视距情况下均方根时延拓展的统计模型，获得附加时延参数的估计值，对TDOA测量误差参数校正；最后，通过最小二乘法初步估计出目标节点位置，将其作为粒子群算法的初始值进行智能粒子群算法求最优解，惯性权重在迭代中按照高斯函数的策略变化。仿真结果表明本文提出的优化算法可有效减弱非视距误差在复杂室内环境中定位的影响，进一步提高定位精度和算法的收敛速度。

UWB indoor location algorithm in NLOS environment

In order to improve the location accuracy of Ultra Wide Band (UWB) in non-line of sight (NLOS) environment, a method to degrade NLOS error was proposed. Firstly, a TDOA (time difference of arrival) position model in NLOS environment was reconstructed. Secondly, statistical model of root mean square delay expansion under non-line-of-sight conditions was deduced and the mean and variance of the NLOS error were obtained, and the measurements of TDOA were reconstructed. Finally, the least square method is employed to get the initial location of the PSO (particle swarm optimization) and the PSO method is used to estimate the location of the objective node. And The inertia weight is based on Gaussian function in the iterative process of PSO. The simulation results show that the proposed optimization algorithm can effectively suppress NLOS errors in the complex indoor environment and further improve the positioning accuracy and convergence rate of the algorithm.