中文摘要：

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.

英文摘要：

To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is proposed.According to the amount of prior information known about the probabilities and distribution parameters of the NLOS error distribution, three different cases of the maximum a posterior（MAP） localization problems are introduced. The first case is the idealized case, i. e., the range measurements in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known. The probability of a communication of a pair of nodes in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known in the second case. The third case is the worst case, in which only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing（ML-SA）-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization algorithm for every case. The root mean square error（RMSE）of the location estimate of the NBP-based localization algorithm is reduced by about 1. 6 m in Case 1, 1. 8 m in Case 2 and 2. 3 m in Case 3 compared with the ML-SA-based localization algorithm. Therefore, in the NLOS environments,the localization algorithms can obtain the location estimates with high accuracy by using the NBP method.