# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 2 Related Works
### Wireless-Signals-Based Positioning Solution  
Wi-Fi and BLE localization systems, which usually contain approaches based on wireless ranging and fingerprinting, are regarded as effective and practical approaches for wireless positioning. For BLE-assisted localization methods, additional BLE nodes are usually required to be deployed due to the limited number of local stations. In addition, traditional BLE RSSI-based ranging methods have degrading localization performance in large-scale indoor spaces due to signal transmission power, and most mobile terminals do not support combined methods such as the angle of arrival (AOA) and the angle of departure (AOD),or the interface is not open. As a result, the BLE proximity detection approaches and the fingerprinting approach are commonly applied as the positioning solutions in large-scale indoor environments. [PERSON] et al. [32] analyzed the effect of pedestrian swing arms on PDR performance and used an RSSI-based multipoint localization algorithm to decrease cumulative errors. [PERSON] et al. [33] proposed an integrated Wi-Fi and BLE positioning framework by using an influential BLE layout plan and hierarchical topological fingerprinting (HTF), which enabled the proposed deployment strategy and HTF based on optimal localization performance. [PERSON] et al. [34] developed a pedestrian-targeted indoor localization solution that used BLE beacons and smartphone integrated sensors. A trilateration approach of improved RSSI analysis provided the accurate initial location of PDR, and a light-weight fingerprinting approach was proposed to decrease the orbital drift of the PDR. [PERSON] et al. [6] presented a 3D indoor localization framework based on the integration of BLE nodes and low-cost sensors, which incorporates INS and PDR mechanization as well as quasi-static magnetic-field recognition. A DTW-based BLE landmark recognition algorithm was applied to enhance the detection performance, and the adaptive unscented Kalman filter was finally applied to realize the meter-level positioning performance in selected experimental sites. For Wi-Fi based localization methods, the crowdsourced fingerprinting approach is considered to be an efficient way to autonomously construct a navigation database in large-scale indoor areas; the performance of the generated crowdsourced navigation database depends on the quality of the collected daily-life motion traces. Currently, the new Wi-Fi FTM protocol is supported by a variety of mobile devices and wireless stations, and has proven to be applicable to be a meter-level location source for large-scale indoor areas. The combination of Wi-Fi fingerprinting and Wi-Fi FTM provides an autonomous and precisely controllable solution for large-scale indoor localization. [PERSON] et al. [35] proposed a secure crowdsourced indoor-positioning scheme that incorporated an adversarial sample discriminator, BERT-AD, and an indoor positioning model, BERT-LOC, to detect fake fingerprinting results and malicious beacons during the online positioning phase. [PERSON] et al. [36] compared the major characteristics affecting the performance of Wi-Fi ranging and designed the NLOS recognition model to decrease the influence of NLOS transmission, and they further combined Wi-Fi FTM with multiple-motion-modes integrated PDR mechanization and realized high accuracy in NLOS/LOS mixed indoor environments [37]. In addition, they extended the 2D scene to a 3D positioning scene and used a traceless partial filter to combine the distance-estimation and landmark-detection results. The final experimental results indicated that the meter-level localization performance was stably acquired by the proposed 3D integrated framework [10]. In general, the traditional fingerprinting-based approaches require the off-line phase of database generation, which is labor-consuming; the crowdsourcing-based algorithm can provide a more efficient method of database construction, while the effective evaluation and integration of crowdsourced trajectories is essential.