一种非视距环境下改进的组合定位算法

Improved Combined Positioning Algorithm in NLOS Environment

为解决基于UWB技术的室内定位算法在测距时存在非视距误差以及异常值,而导致定位精度以及稳定性下降问题,文中提出了一种能够鉴别非视距环境以及消除非视距误差的组合定位算法。该算法通过二项假设检验鉴别非视距环境,在扩展的卡尔曼滤波算法中的待估计的状态向量之中引入非视距转换因子,利用迭代算法消除非视距误差对定位估计的影响,最后通过CTK算法对定位进行精确计算。实验结果表明,该算法针对非视距环境在约95%的定位结果中的误差仅为0.04m左右,相比于CTK算法,定位精度效果提升了13%左右,定位结果更加准确,可以更好满足室内移动机器人的定位要求。

The indoor positioning algorithm based on UWB technology has non-line-of-sight errors and outliers,which lead to a decrease in positioning accuracy and stability.In order to solve this problem,the paper presents a combined positioning algorithm that can identify non-line-of-sight environments and eliminate non-line-of-sight errors.The algorithm identifies non-line-of-sight environments by binomial hypothesis testing.The non-line-of-sight conversion factor is introduced into the state vector to be estimated in the extended Kalman filter algorithm,and the influence of non-line-of-sight errors on location estimation is eliminated by the iterative algorithm.Finally,the location is calculated accurately by CTK algorithm.Experimental results show that in the non-line-of sight environments,the error of the proposed algorithm is just about 0.04min about 95%of the positioning results.Compared with CTK algorithm,the proposed algorithm can improve the positioning accuracy by about 13%,and the more accurate results can better meet the positioning requirements of indoor mobile robots.