摘要
在复杂的室内环境下,存在多径,噪声及非视距(NLOS)的影响,当CHAN算法在菲涅尔区遮挡的范围大于50%时,其定位精度会降低。Taylor算法虽然定位精度高且有较好的鲁棒性,但是过于依赖初始位置,因此提出了一种改进的CHAN,CHAN-Taylor级联混合加权算法。通过残差对测量的到达时间差(TDOA)中的非视距误差进行剔除,将筛选出的值作为Taylor的初始值,进一步通过Taylor算法定位。通过CHAN、CHAN-Taylor分别获得估计值,对两次估计值进行加权得出最终标签位置估计。通过实验分析论文提出的算法定位精度比CHAN-Taylor级联算法定位精度提升了7.5%,并且具有良好的鲁棒性。
In the complex indoor environment,there are multipath,noise and non line of sight(NLOS)effects. When the occlusion range of Chan algorithm in Fresnel region is greater than 50%,its positioning accuracy will be reduced. Taylor algorithm has high positioning accuracy and good robustness,but it is too dependent on the initial position. Therefore,an improved Chan,Chan-Taylor cascade hybrid weighted algorithm is proposed. The non line of sight error in the measured time difference of arrival(TDOA)is eliminated by residual error,and the filtered value is taken as Taylor’s initial value,which is further located by Taylor algorithm. The estimated values are obtained by Chan and Chan-Taylor respectively,and the final tag position estimation is obtained by weighting the two estimated values. Through experimental analysis,the positioning accuracy of the proposed algorithm is7.5% higher than that of the Chan-Taylor cascade algorithm,and has good robustness.
作者
方李林
王建新
张汉
FANG Lilin;WANG Jianxin;ZHANG Han(Xi'an University of Science and Technology,Xi'an 710600)
出处
《计算机与数字工程》
2022年第12期2625-2629,共5页
Computer & Digital Engineering
关键词
非视距
混合加权
残差
non line of sight
hybrid weighted
residual
