基于LTS算法改进的抑制NLOS误差的室内定位

Improved indoor positioning based on LTS algorithm for suppressing NLOS error

针对传统的最小截断平方(LTS)算法利用硬阈值决定位置、计算基站,重非视距(NLOS)环境中不能分离出具有最小偏差基站的缺陷,提出了一种新的改进方法。首先对测量距离进行卡尔曼滤波处理,然后对基站进行有序分组,选择所有基站组合中具有最小残差的基站集作为最终位置计算集合。实验表明:该方法在NLOS环境下与传统的LTS算法和最小二乘估计(LSE)算法相比定位精度分别得到了24.1%和53.3%的提高。

Aiming at the defect that the traditional least trimmed squares(LTS)using hard threshold to determine location and calculate base station,in the non-line of sight(NLOS)environment,base station with the smallest deviation cannot be separated.A new improved method is proposed.Firstly,the Kalman filtering process is performed on the measured distance,then the base station is orderedly grouped,and the base station set with the smallest residual among all base station combinations is selected as the final position calculation set.Experiments show that the proposed method has improved positioning precision by 24.1%and 53.3%,compared with the traditional LTS algorithm and least square estimation(LSE)algorithm in the NLOS environment.