摘要
针对RSSI室内指纹定位算法中参考点误匹配和位置搜寻问题,提出了一种基于模糊聚类和猫群算法的室内定位方法。首先,采用模糊聚类代替传统硬聚类算法,并根据聚类中参考点的隶属度对聚类中心的RSSI特征进行合理估算,不但增加了参考点之间的差异性,而且减小了特征匹配的复杂度;其次,利用猫群算法在靠近最优解处收敛速度快的特点,同时在算法中加入投食机制以增加算法局部搜索的能力,实现了稳定快速的区域化位置搜寻。实验结果表明,与传统算法相比,所提算法可以提高12.5%的定位精度。
The received signal strength indication(RSSI) based indoor fingerprinting positioning algorithm has problems of reference-points error-matching and location discovery. To solve these problems, a fuzzy clustering and regional cat swarm based positioning method is proposed. Firstly, the fuzzy clustering is used to accomplish clustering and estimate RSSI feature of the cluster center instead of the traditional hard clustering algorithm. In this way, the fuzzy clustering based two-level matching can increase the difference between reference points, and reduce the complexity of feature matching. Then, the cat swarm optimization is utilized due to the fast convergence near the optimal solution, which is suitable for the location discovery based on the regions obtained by the two-level matching method. Simultaneously, a feed mechanism is designed to improve the local search capability and the convergence speed of the cat swarm optimization. Compared with traditional algorithms, experimental results show that the proposed algorithm can improve the positioning accuracy by 12.5%.
作者
李昂
付敬奇
沈华明
孙泗洲
Li Ang;Fu Jingqi;Shen Huaming;Sun Sizhou(School of Mechatronic and Automation,Shanghai University,Shanghai 200444,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第1期185-194,共10页
Chinese Journal of Scientific Instrument
关键词
室内定位系统
接受信号强度指示
位置指纹定位算法
模糊聚类
猫群算法
indoor positioning system
received signal strength indication
fingerprinting based positioning algorithm
fuzzy clustering
cat swarm optimization
