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.
Highlights
While deep learning technology is gradually applied in the field of wireless localization, a work systematically summarizing, classifying, and discussing related results has not yet been reported to our best knowledge
For the application of deep learning technology in the field of wireless localization, the main purpose of this paper is to propose some problems solved by deep learning technology, summarize the typical deep learning models, explore the input forms and localization methods, and pay attention to the technical details in literature which can help to improve localization performance
In 2014, Zhang et al [5] proposed an indoor localization method based on the received wireless LAN WiFi signal strength using Deep Neural Network (DNN) and Hidden Markov Model (HMM), which modeled the indoor localization problem as a classification problem
Summary
In 2014, Zhang et al [5] proposed an indoor localization method based on the received wireless LAN WiFi signal strength using Deep Neural Network (DNN) and Hidden Markov Model (HMM), which modeled the indoor localization problem as a classification problem. In 2017, Zhang et al [9] proposed a new indoor fingerprinting localization system based on deep learning, combining received signal strength of WiFi and pervasive magnetic field to obtain richer fingerprinting, and investigated the indoor localization method based on deep neural networks in the form of classification and regression. In 2018, Liu et al [12] pointed out that, the current fingerprinting localization technology can obtain room level accuracy, the time-varying property of received signal strength caused a large position estimation error.
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