Abstract
With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.
Highlights
In recent years, with the rapid development and popularization of mobile Internet, the demand for Location-Based Services (LBSs) [1] has gradually increased, which makes it feasible to obtain and utilize location information through smartphones, tablets and other mobile terminals
Reference Points (RPs) are classified by the Affinity Propagation Clustering (APC) algorithm, which is regarded as the basis of the online phase
We propose a localization algorithm-based Wireless Local Area Network (WLAN) location fingerprinting for ensuring the high positioning accuracy and reducing energy consumption
Summary
With the rapid development and popularization of mobile Internet, the demand for Location-Based Services (LBSs) [1] has gradually increased, which makes it feasible to obtain and utilize location information through smartphones, tablets and other mobile terminals. WLAN-based algorithms, like Time-Of-Arrival (TOA) [14] or Angle-Of-Arrival (AOA) [15], RSS fingerprint positioning technology need not estimate too many parameters, which could be against indoor multipath propagation effectively and improve the accuracy of indoor positioning. To get the optimal clustering results, we use the theory of the APC algorithm to improve the clustering effect and reduce the probability of the improper initial clustering center in artificial selection [21,22] This algorithm is based on the similarity between two different data points, which does not require a special clustering number in advance. In view of computing power, storage capacity and limited energy of the mobile terminal, the purpose of this paper is to design an indoor positioning algorithm based on location fingerprinting to improve the positioning accuracy and verify the effectiveness of this algorithm.
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