Device-free passive wireless localization system with weighted transferable discriminative dimensionality reduction method

Abstract

Device-free passive wireless indoor localization has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services. In this paper, we propose a novel device-free passive wireless localization system with a weighted transferable discriminative dimensionality reduction method (termed TLLOC). It utilizes the channel state information (CSI) extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices. To cope with the problem of reduced localization accuracy caused by the unpredictable nature of CSI over time that ignored by most previous CSI-based localization works, a novel weighted transferable discriminative dimensionality reduction (termed WTR) method combining transfer learning and dimensionality reduction is proposed. The WTR method constructs a low-dimensional latent space, which can simultaneously improve the discrimination of training samples and narrow the distribution divergence between the training samples and the test samples, further enhancing the performance of our system. Experimental results are presented to confirm that TLLOC can effectively improve localization accuracy while saving a great amount of the calibration cost, compared with the other existing methods in a representative indoor environment.