Indoor Positioning Algorithm for WLAN Based on KDDA and SFLA-LSSVR

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.