[1]李刚,陈俊杰.一种基于测距的蒙特卡罗盒定位算法[J].东南大学学报(自然科学版),2012,42(6):1105-1110.[doi:10.3969/j.issn.1001-0505.2012.06.016] 　Li Gang,Chen Junjie.Range-based Monte Carlo localization boxed algorithm[J].Journal of Southeast University (Natural Science Edition),2012,42(6):1105-1110.[doi:10.3969/j.issn.1001-0505.2012.06.016] 点击复制 一种基于测距的蒙特卡罗盒定位算法() 分享到： var jiathis_config = { data_track_clickback: true };

42

2012年第6期

1105-1110

2012-11-20

文章信息/Info

Title:
Range-based Monte Carlo localization boxed algorithm

1 东南大学仪器科学与工程学院,南京 210096; 2 东南大学常州研究院,常州213164
Author(s):
1 School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2 Changzhou Academe, Southeast University, Changzhou 213164, China

Keywords:

TP393
DOI:
10.3969/j.issn.1001-0505.2012.06.016

Abstract:
Some common problems, such as low location accuracy and low sampling efficiency, are unavoidable in current node localization algorithms based on Monte Carlo localization(MCL)in mobile wireless sensor networks. To improve these issues, a range-based MCL boxed algorithm(RBMCB)is proposed. In the algorithm, a precise sample box is constructed through the range information. Meanwhile, the maximum sampling times is adaptively determined by Kullback-Leibler distance(KLD)sampling and weighted calculation is used for analyzing the sample range information. Finally, the weighted mean value of all samples is taken as the location estimation. Simulation results show that the proposed algorithm can enhance the location accuracy by 30% comparing to the MCB algorithm, and 10% comparing to the range-based MCL algorithm. Furthermore, the results also show that the proposed algorithm can achieve higher sampling efficiency. Thus, RBMCB can be applied to the circumstance where the high location accuracy and sampling efficiency are required.

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