The non-line of sight (NLOS) propagation caused by the building shielding and background interference will lead to large error when applying Ultra-Wideband (UWB) technology to indoor positioning. In order to reduce the positioning error, a joint simulated annealing algorithm and a bat algorithm is proposed in the paper. First, based on the Bat Algorithm (BA), transform the problem of the target into the problem of difference between the estimated position and the actual position to establish an iterative optimization model. Then introduce random perturbation factor to improve the search ability of the bat individual; Aiming at BA is easy to fall into local optimum, a Simulate Anneal (SA) algorithm is integrated to improve the classification and search ability of the algorithm. Finally, we use Chan-Taylor algorithm to determine the accurate position of the target. The research result shows that proposed algorithm improves positioning accuracy by 11∼23cm which can effectively improve the positioning accuracy of the positioning algorithm under the LOS/NLOS environment.

将超宽带（UWB）技术应用于室内定位时，由建筑物屏蔽和背景干扰引起的非视线（NLOS）传播将导致较大的误差。为了减小定位误差，提出了联合模拟退火算法和蝙蝠算法。首先，基于蝙蝠算法（BA），将目标问题转化为估计位置与实际位置之间的差异问题，以建立迭代优化模型。然后引入随机扰动因子以提高蝙蝠个体的搜索能力；针对BA容易陷入局部最优的问题，集成了模拟退火算法，以提高算法的分类和搜索能力。最后，我们使用Chan-Taylor算法确定目标的精确位置。研究结果表明，该算法将定位精度提高了11〜23cm，可以有效提高LOS / NLOS环境下定位算法的定位精度。