# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 2 Related Works  
In 2000, Microsoft developed RADAR, a radio frequency (RF)-based wireless local area network (WLAN) indoor positioning system for locating and tracking users inside buildings [16]. It used a k-nearest neighbors (KNN) approach, which used the European distance of signal space to match and locate, and became the gold standard of fingerprint positioning. Clustering-KNN [17] and improving weighted KNN [18] approached soon followed, but more time was needed to deal with the challenge of row data with the aim of improving accuracy. The traditional methods were unable to accomplish this because of the complex nature of the signal error mechanism [19]. In [20], the authors constructeda fingerprint system with the help of Wi-Fi channel State information (CSI), based on a neural network. The system achieved better accuracy, but because special equipment was required to obtain CSI, it was not considered a universal solution. Wi-Fi signals are increasingly being received in public places. However, it is a challenge to use this big data fully and effectively, although the rapid development of machine learning theory makes it possible because it requires less parameter tuning and offers better scalability. Support vector machine [21] and random forest [22] have been used to capture the correlation between signal strength and physical location. [PERSON] et al. [23] first used a DNN to realize floor classification based on Wi-Fi, but the solution could not estimate user location. In [24], the authors realized Wi-Fi fingerprint location based on deep learning by simulation, with location error less than 1 m. [PERSON] et al. [25] and [PERSON] et al. [26] proposed DNN-based solutions that extracted features from large noise signal samples for localization. They analyzed the impact of model parameters and data structures on positioning performance. With the gradual combination of crowdsourcing theory with wireless location, it is necessary to explore techniques for deep learning-based indoor positioning under the background of big data to obtain the better positioning accuracy and data utilization. Multipath and non-line-of-sight (NLOS) propagation inevitably leads to gross errors in the Wi-Fi positioning results in complex indoor environments. However, many constraints exist when it comes to obtaining information about user navigation, and some of them can be used to improve the positioning accuracy. A smoothed constrained fingerprinting solution has also been adopted in a system that integrates a dual-filter MEMS sensor and Wi-Fi fingerprinting [27]. [PERSON] et al. [28] combined the Pedestrian Dead Reckoning localization algorithm with the linear Kalman filter to achieve Wi-Fi fusion positioning results. [PERSON] et al. [29] used fingerprinting accuracy indicators (FAIs) to predict wireless and magnetic fingerprinting accuracy, and then used the FAI-enhanced EKF to improve the stability of multi-sensor fusion. In [30], [PERSON] et al. proposed an adaptive weighted KNN Wi-Fi location method, which improved the accuracy of single point positioning with the constraints of the user's orientation and the hybrid distance of the signal domain and position domain. In [26], the authors set up the CellinDeep system-based Wi-Fi signal, which used data augmenter techniques and mean filtering to address practical challenges caused by signal noise. [PERSON] et al. [31] used Kalman filtering to reduce drift and oscillation on a received signal strength indicator (RSSI) caused by the noise overlap-add in Bluetooth locations. Nevertheless, most of these methods rely on multiple sensors, and there has been relatively little research on a single Wi-Fi constraint location. Those Wi-Fi experiments that have taken place are based on discrete points.