Abstract
Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.
Highlights
With the development of location based services (LBS), localization has become one of the essential modules of wireless mobile devices
A variety of wireless indoor localization techniques have been widely applied into different indoor scenes, such as Wi-Fi [1]-[4], RFID [5], Ultra Wide-Band (UWB) [6], other light signals [7], etc
With the encoding hidden layer, it generates a target output h when it is fed with input x, and x is a column vector of measured received signal strength (RSS) values from detectable access point (AP) and it is same as the traditional fingerprint pattern
Summary
With the development of location based services (LBS), localization has become one of the essential modules of wireless mobile devices. Machine learning algorithms make little contribution to the reduction of localization errors at the cost of the computational complexity, which seriously occupy the computational recources of other applications on the mobile device and weaken the endurance capacity of mobile devices Despite these advantages, Wi-Fi fingerprint-based schemes still face challenges of improvement of localization accuracy. Several methods, including deterministic and probabilistic methodologies, were proposed for fingerprint-based indoor localization. Deterministic localization methods combine several Reference Points (RPs) to estimate location of the target based on the selection of the shortest distance between the real-time signal and fingerprint in multi-dimensional space [12][13]. The autoencoder network shows powerful ability to extract the features from a serial of fluctuated RSS values
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