BLS-Location: A Wireless Fingerprint Localization Algorithm Based on Broad Learning

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

Automated summary

This paper presents a novel indoor wireless fingerprint localization algorithm based on a broad learning system, which utilizes channel state information to overcome the problems of data loss, noise interference, and time-consuming offline training. Experimental results show that the algorithm outperforms several machine learning algorithms and existing methods in terms of training time reduction and accuracy.