通信学报Issue(1)：99-106,8.DOI:10.3969/j.issn.1000-436x.2014.01.012
基于高斯混合模型的非视距定位算法
GMM-based localization algorithm under NLOS conditions
摘要
Abstract
Aiming at indoor node localizations of WSN, a node localization algorithm, where priori-knowledge is not ne-cessary, was proposed. on basis of analyzing the error model, combined with Gaussian mixture model (GMM). By train-ing the distance measurements containing NLOS errors, the more accurate range estimations can be obtained. For higher localization accuracy, the particle swarm optimization (PSO) was introduced to optimize the expectation-maximization (EM)algorithm. Finally, by using the residual weighting algorithm to estimate the distance, the estimation coordinates of target nodes can be determined. The proposed algorithm was proved to be effective through simulation experiments.关键词
非视距/RSSI/残差加权算法/粒子群优化算法/高斯混合模型Key words
NLOS/RSSI/residual weighting algorithm/particle swarm optimization algorithm/Gaussian mixture model分类
信息技术与安全科学引用本文复制引用
崔玮,吴成东,张云洲,贾子熙,程龙..基于高斯混合模型的非视距定位算法[J].通信学报,2014,(1):99-106,8.基金项目
国家自然科学基金资助项目(61273078)@@@@The National Natural Science Foundation of China(61273078) （61273078）
