关键词: 接收信号强度, 无线局域网, 室内定位, 核直接判别分析, 混洗蛙跳算法, 最小二乘支持向量回归机

Abstract: The time-varying received signal strength (RSS) degrades the indoor positioning accuracy in wireless local area network (WLAN). A novel indoor positioning algorithm based on kernel direct discriminant analysis (KDDA) and shuffled frog leaping algorithm and least square support vector regression (SFLA-LSSVR) is proposed to address the problem. Firstly the proposed algorithm employs kernel function strategy to map RSS signal to the field of nonlinear, which is sampled from each access point (AP), and extracts nonlinear features effectively, and reconstructs the positioning information, and discards the redundant positioning features and noise. Secondly, LSSVR algorithm is employed to build the mapping relation model between positioning features and physical locations, and SFLA is employed to optimize the parameters of the relation model, and then test points locations are predicted by using the relation model. Experimental results show that the positioning accuracy of the proposed algorithm is much superior to WKNN, ANN, LSSVR algorithm under the condition of the same sampling numbers, and the number of RSS signal which is sampled from each AP is significantly reduced in the same positioning accuracy, and the proposed algorithm is a WLAN indoor positioning algorithm with good performance.

Key words: received signal strength (RSS), wireless local area network (WLAN), indoor positioning, kernel direct discriminant analysis (KDDA), shuffled frog leaping algorithm (SFLA), least square support vector regression (LSSVR)