## 1 Introduction  
For positioning in outdoor environments, the Global Position System (GPS) [1; 2] can provide very accurate positioning results. Since acquiring a satellite signal inside a building is not possible, the GPS can not be applied to the purpose of indoor positioning. Therefore, it is of great significance to study the indoor positioning technology not relying on the GPS. In recent years, Wireless Local Area Network (WLAN)-based [3] positioning technology has become a research hotspot because of its widespread deployment and ease of use WLAN. In the design of a positioning system, the accuracy of ranging is one of the factors that must be considered. The most important reason for affecting the positioning accuracy in the indoor positioning system is the complex multipath transmission environment and the limited bandwidth of the wireless signal, which cannot effectively distinguish the signals transmitted through multiple paths. According to the position estimation method in wireless sensor networks, the ranging algorithm is divided into two categories: ranging-based algorithm and range-free algorithm. The former method includes Angle of Arrival(AOA) [4], Time of Arriva (TOA) [5], Time Difference of Arrival (TDOA) [6], Received Signal Strength (RSS) [7], Channel State Information (CSI) [8] etc., and calculate or estimate the distance between the node and the reference. The latter one uses the relationship of the geometric position information between the receiving node and the transmitting node to estimate the distance, including the fingerprint positioning method [9] and approximate estimation method [10]. Figure 1 shows the position estimation methods between range-based and range-free algorithms. Due to the complex indoor environment, RSSI is often affected by multipath effects and noise signals, and the positioning performance is not stable. With the availability of channel state information from the physical layer, Wi-Fi-based indoor positioning schemes have gradually shifted from adopting RSSI indicators to higher resolution CSI indicators. In recent years, commercial Wi-Fi devices (such as the Intel 5300 wireless network card) have begun to support the acquisition of CSI at the physical layer. CSI can characterize signals with finer granularity. By analyzing the transmission of different sub-channel signals separately, CSI can avoid the multipath effects and noise as much as possible. [PERSON] et al. [8] explore the frequency diversity of subcarriers in the Orthogonal Frequency Division Multiplexing (OFDM) system and propose a new method called fine-grained indoor localization (FILA), which uses CSI to establish the propagation model and fingerprint identification system at the receiver. FILA can achieve a median accuracy of 1.2 m in a corridor environment. In paper [11], [PERSON] et al. proposed a mobile cooperative tracking method based on extended RSSI ranging. The error estimation range of this algorithm was between 0.22 m and 2.84 m. The fine-grained indoor fingerprinting system (FIFS) [12] explores CSI of the physical layer which specifies channel status on all subcarriers for location fingerprint identification in WLAN. The system uses the amplitude and phase of CSI on multiple propagation paths to display a single position. In experiments, for over 90% of data points, the error of FIFS was within the range of 1.3 meters. In [13], a new indoor fingerprint recognition system based on deep learning using CSI is introduced called deep learning for indoor fingerprinting (DeepFi). This method based on three hypotheses of CSI, the DeepFi system architecture includes an off-line training phase and an online localization phase. In the living room environment, the mean error is 0.9245 m. In the laboratory environment, the mean error distance is 1.8081 m. In paper [14], [PERSON] et al. proposed a novel Wi-Fi fingerprint recognition system CSI-MIMO, which uses a multiple-input-multiple-output (MIMO) system to utilize frequency diversity and spatial diversity.