# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 1 Introduction  
Indoor localization technology is indispensable for Internet of Things services. It can be applied to a wide range of fields, such as navigation for smartphone users, resource management in smart factories, and autonomous driving of robots, among others. Outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. Several studies have been conducted in order to estimate locations in GNSS-denied environments. In particular, the method of using the received signal strength (RSS) from Wi-Fi communication is promising because it fits most commercial products [1, 2, 3]. Indoor localization methods that use RSS can be divided into two groups: fingerprint-based methods and range-based methods [2]. The fingerprint-based methods are based on a pattern-matching scheme. The RSS patterns from multiple access points (APs) at a specific location were recorded in the offline stage. In the online stage, the measured RSS pattern is compared with a recorded database to match a location. The offline stage requires time and effort for site surveys and is vulnerable to channel changes (e.g., AP location changes and furniture structure changes) [3]. Range-based localization methods estimates the distance between individual APs and a target node and specify a location through geometric inference. The main problem in range-based localization using RSS is the ranging error. The RSS ranging error result from the non-line-of-sight (NLOS) channel condition, antenna direction, and time-varying obstruction by crowds. Among these, the NLOS effect of indoor wall structures and obstacles is the most important factor. The shadowing effect caused by wall structures makes RSS ranging difficult, resulting in position inaccuracy [4, 5]. Therefore, improving the ranging accuracy is a key factor for range-based localization. Typically, RSS ranging is based on a path-loss model (PLM), which represents the relation between the RSS and the distance between communication nodes. The representative model is the two-slope model presented in the IEEE 802.11 standard [6]. This is an expression for an universal indoor space and does not reflect a site-specific environment. Therefore, the ranging accuracy based on this model is limited in practical cases. To solve this problem, studies have been conducted on the adjustment of the parameters of the PLM [7; 8; 9; 10] and the correction of ranging results through the detection of NLOS by using RSS changes over time or characteristics of differences in RSS in dual-frequency bands. The method of adjusting the PLM's parameters to fit into a specific environment improves the statistical ranging accuracy; however, there is a limit to reflecting complex indoor spaces with severe NLOS conditions. The method that uses the time-varying RSS has a disadvantage in terms of time consumption. The dual-band scheme is based on the wave propagation characteristics in an NLOS environment with a carrier frequency. In practice, many off-the-shelf Wi-Fi APs and mobile devices are compatible with dual-band operation. In addition, many approaches use deep learning techniques. Most localization algorithms that use deep learning can be regarded as data-driven methods, as they involve a neural network model that is trained with RSS input and the corresponding position labels. Replacing the entire localization process with a deep learning model causes problems regarding vulnerability to environmental changes, similarly to fingerprint-based methods. In this study, we propose a hybrid approach, which applies data-driven deep learning to only the ranging part of a model-based localization algorithm. By performing nonlinear regression using a neural network for the dual-band RSS, the ranging accuracy was significantly improved compared to that of the rule-based method.