Wireless Localization Based on Deep Learning: State of Art and Challenges

Abstract

The problem of position estimation has always been widely discussed in the field of wireless communication. In recent years, deep learning technology is rapidly developing and attracting numerous applications. The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models. Consequently, wireless localization based on deep learning has attracted extensive research during the last decade. The research and applications on wireless localization technology based on deep learning are reviewed in this paper. Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods. Technical details helpful for enhancing localization ability are also mentioned. Finally, some problems worth further research are discussed.

A Sustainable, Interactive Elderly Healthcare System for Nursing Homes: An Interdisciplinary Design

“…Machine learning methods are thriving and now applied well in the online-stage matching (Li, X. et al [45] and Ye, Y.-X. et al [46]). It is hard to judge which method is the state-of-the-art matching algorithm; each algorithm has its scope of application, and the pros and cons for an algorithm to be selected should be seriously considered according to the need.…”

A Survey of Indoor Localization Systems in Multi-Floor Environments

“…The commonly used DL models in the literature include Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder (AE). For more details about using DL in localization, the reader can refer to [24], [25].…”

Direct Position Determination with Single Sensor Based on Signal Periodicity

“…, K can be obtained by fitting covariance matrix of received data to (12) in a weighted least squares sense. However, a multidimensional search is essential for estimating target position p and phase noise vector simultaneously, which has dominant computational complexity [23]. To solve the problem, an iteration optimizing scheme is adopted here.…”

“…Generally, C k is chosen to be the inverse of the asymptotic covariance of the residuals [23]. Substitution of ( 13) into ( 14) yields p � arg min…”