## 1 Introduction
The system combines the spatial attributes of CSI to improve the FIFS system. The CSI-MIMO system uses the amplitude difference and the phase difference matrix of adjacent subcarriers as the unique fingerprints of the sample points to achieve more accurate positioning than FIFS. The paper [15] presents a deep learning-based method for indoor positioning by utilizing transmission channel quality metrics, including RSSI and CSI. Figure 1: The position estimation methods between range-based and range-free algorithms. However, most of the current algorithms are range-free-based using RSI and CSI. These positioning methods are currently based on the similarity of the indoor environment during the positioning and training phases. These positioning methods do not consider the factors that change the indoor environment during the positioning phase, that is, the difference between the indoor environment during the positioning phase and the environment when the database is established. For example, a fingerprint database was established in an unmanned indoor environment, but location matching was performed when the number of people in the room increased. In the case of a large difference in indoor environment between the positioning and training phases, if fingerprint matching is still performed in the traditional way, the positioning error will inevitably increase, and the accuracy rate will also be greatly reduced. Based on the reasons above, we propose the indoor ranging algorithm based on RSSI and CSI that are simultaneously processed by the extended Kalman filter (EKF) to address the complex indoor situation. In this paper, we analyze the different characteristics of RSSI and CSI and propose a novel indoor ranging algorithm. We use two different granularities of RSSI and CSI to realize regional ranging and precise positioning respectively and make the best use of the advantages of different granularity information as much as possible. EKF [16] is used to filter nonlinear attenuation ranging model based on RSSI and CSI. The proposed algorithm breaks the limitations of traditional RSSI or CSI-based fingerprint positioning and can be applied to online positioning in any indoor environment. The main contributions of this paper are:  
1. A cross-layer approach including MAC layer and physical layer that enable fine-grained indoor ranging in WLANs. Our proposed method includes two parts, RSSI-based ranging model and CSI-based ranging model. 2. Indoor ranging research based on RSSI and CSI in the environment of high-load Access Point (AP). This paper demonstrates the feasibility of this method in a high-load AP environment. 3. The method we propose is also the first one that uses extend Kalman filtering to combine RSSI-based signal attenuation model and CSI-based ranging model to perform distance estimation. 4. The experimental evaluation in two representative indoor environments to confirm the feasibility of our design and its effect on the ranging results. The experimental results show that the proposed algorithm outperforms existing algorithms. The rest of this paper is organized as follows: Section 2 gives a preliminary of the basics of RSSI, CSI and EKF. In Section 3, the proposed method is described in detail; We use extended Kalman filter to estimate the distance through the nonlinear relationship between CSI, RSSI and distance. The experimental experiment and result are detailed in Section 4. Finally, conclusions and the future research are presented in Section 5.