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为了减少定位精度上由于NLOS误差造成的影响,基于非参数信任传输(NBP)方法建立一种在NLOS环境下的定位算法.根据NLOS误差的分布概率及分布参数的先验信息量,给出了3种不同情况下定位问题的最大后验概率.第1种情形为理想化情形,即已知NLOS环境下的距离测量及相应的NLOS误差分布参数.在第2种情形中,仅已知任意2个节点之间的通信处于NLOS环境下的概率及相应的NLOS误差分布参数.第3种情形为最差情形,仅获得测量误差的信息.将所提算法与基于最大似然退火法(ML-SA)的定位算法进行了比较,仿真结果表明:在每种情形下所提算法获得的定位精度都远超过基于ML-SA的定位算法.在3种不同情形下基于NBP定位算法的位置估计均方根误差比基于ML-SA的定位算法分别降低了1.6,1.8和2.3 m左右.因此,在NLOS传输环境下,采用NBP的定位算法可获得较高的定位精度.
In order to reduce the impact of NLOS error on locating accuracy, a non-parametric trust transfer (NBP) method is proposed to establish a locating algorithm in NLOS environment.According to the distribution probability of NLOS error and the prior information of distributed parameters, The maximum posteriori probabilities of the localization problem under three different conditions are as follows: The first case is the idealized case, that is, the distance measurement under the known NLOS environment and the corresponding NLOS error distribution parameter.In the second case, only any The probabilities of the two nodes communicating under NLOS and the corresponding NLOS error distribution parameters are shown in Fig. 3. The worst case scenario and the only measurement error information are obtained in the third case.The proposed algorithm is compared with MLLS -SA) are compared. The simulation results show that the localization accuracy of the proposed algorithm outweighs the ML-SA-based localization algorithm in each case. The location estimation based on NBP localization algorithm in three different scenarios The root-mean-square error is reduced by about 1.6, 1.8 and 2.3 m respectively compared with the ML-SA-based localization algorithm. Therefore, NBP localization algorithm can obtain high positioning accuracy under the NLOS transmission environment.