# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 1 Introduction  
With the popularization of wireless communication equipment and the improvement of location service technology, people are looking for more accurate location recognition technology in more extensive and complex applications [1; 2]. Global navigation satellite systems (GNSS) play an important role in outdoor navigation due to their positioning ability, but their use is limited in indoor environments because the signal is blocked by houses and the inhabitants inside. In order to satisfy the needs of indoor location-based services (LBS), such as ultrasound [3], ultra-wideband [4], geomagnetic fields [5], images [6], light [7], inertia systems [8] and wireless signals [9] are used for indoor positioning. As infrastructure for widely deployed public networks, Wi-Fi access point (AP) networking hardware devices are now found in nearly every indoor area where people conduct their daily activities, which provides a huge application space for indoor location services. Therefore, Wi-Fi-based indoor positioning technology has become a preference for different kinds of indoor positioning technologies due to its low cost, low power consumption and wide availability in mobile devices [10]. As with other wireless technologies, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and received signal strength (\(RSS\)) approaches are used to achieve Wi-Fi positioning. Unlike the other three methods of calculating user coordinates through geometric relationships, the Wi-Fi fingerprint location method based on \(RSS\) uses a matching algorithm to determine location with the character of signal strength. As a result, this method is less affected by the influence ofenvironmental change, and there is no need to know the AP location, which explains why it has become mainstream Wi-Fi location technology [11]. However, wireless signal strength is not stable in complex indoor environments. Affected by multipath interference, _RSS_ may fluctuate greatly, which will reduce its positioning accuracy. At present, the advancement of micro-electro-mechanical-systems (MEMS) technology has led to more sensors in smartphones [12]. Researchers propose that combining a multi-sensor scheme with Wi-Fi data will profoundly improve the positioning accuracy [13; 14]. However, equipment, such as accelerometers, gyroscopes and compasses, is prohibitively expensive for most customers, and the use of multiple sensors will inevitably increase power consumption. At the same time, users only need to know their outline coordinates and cooperate with the visual inspection to determine their location in daily life. Thus, it is more in accordance with users' requirements to obtain rapid and accurate positioning information by relying on fewer sensors in the public environment. Since Wi-Fi positioning has a huge advantage due to the wide deployment of its infrastructure, the focus is on how to improve its positioning performance in buildings. Machine learning has the ability to mine data features, which is a very promising technology for solving the problem of Wi-Fi signal mismatch, and has been the focus of much recent research. In this paper, a robust and accurate Wi-Fi fingerprint location recognition method based on deep neural network is put forward, and its main contributions are the following:  
1. We use the stacked denoising autoencoders (SDAE) in a deep neural network (DNN) to perform Wi-Fi location recognition. Compared to the traditional Wi-Fi fingerprint location method, the proposed method can fully obtain feature representations from noisy _RSS_, reduce the dimension of the Wi-Fi signal and improve positioning accuracy and robustness. In addition, we use regularization techniques to avoid the model overfitting offline, and take into account the geometric distribution of fingerprint points in order to estimate the user's position and further ensure accuracy during the positioning stage. 2. We construct a dynamic constrained Kalman filter (KF) based on the speed at which users inside a building move.