# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 1 Introduction  
Precise indoor positioning plays an important role in the field of urban navigation by providing continuous location-based services (LBS) in urban areas covered by global navigation satellite systems (GNSS). A range of indoor positioning technologies, such as Wi-Fi [1], fifth-generation mobile communication technology (5G) [2], ultra-wideband (UWB) [3], acoustic sources [4], light sources [5], Bluetooth Low Energy (BLE) [6], and smartphone integrated sensors [7], have been developed to achieve the realization of positioning performance with different levels of accuracy. Wi-Fi-based indoor localization is more feasible for the indoor positioning of wide-area targets due to its low cost, easy maintenance and wide coverage. Normally, a Wi-Fi positioning system (WPS) contains two main approaches: ranging [8] and fingerprinting [9]. IEEE 802.11-2016 improves the positioning ability of WPS by proposing the Wi-Fi fine time measurement (FTM) protocol, which is estimated to achieve meter-level Wi-Fi distance-measurement precision between different smart terminals and corresponding wireless stations [10]. At this stage, only a fraction of Wi-Fi access points (APs) or mobile terminals are updated with the FTM protocol; thus, the fingerprinting-based approach is still important for providing location ability in large-scale indoor areas. TraditionalWi-Fi fingerprinting typically involves a point-to-point collection method, which is labor-consuming and inefficient [11]. The crowdsourcing-based Wi-Fi fingerprinting collection approach is developed using public daily-life trajectories, and the accuracy of the generated navigation database is subject to the performance of raw collected trajectories [12], the number of reference points [13], and the bias of different devices [14]. Micro-electro-mechanical system (MEMS) sensors can determine an accurate short-term localization performance of pedestrians, while the positioning error can accumulate over time. In addition, artificial interference can affect the performance of MEMS sensor-based heading estimations and final location updates [15; 16]. Wi-Fi FTM and fingerprinting approaches can realize more consistent long-term accuracy, but they are susceptible to inaccuracy in complex indoor scenes, including multipath propagation and non-line-of-sight (NLOS). Therefore, to improve the precision of indoor pedestrian tracking, a multi-source fusion approach is usually adopted to combine the superiorities of existing location sources to achieve precision-controllable indoor localization performance through the organic combination of different positioning sources [17; 18; 19]. To conquer the above challenges, this paper proposes an intelligent 3D indoor positioning framework based on the combination of Wi-Fi, BLE, QR code, and MEMS sensors (the 3D-WBQM framework), which enables autonomous and precisely controlled indoor positioning performance in large-scale indoor spaces. The contributions of this work are summarized below:  
1. To enhance the performance of MEMS sensor-based dead reckoning, an enhanced inertial odometry was developed that combines the inertial navigation system (INS) mechanization with multiple-sensors-originated observations and can significantly eliminate the effects of magnetic interference and external accelerations. 2. Three different location sources (Wi-Fi FTM, BLE, and QR) were selected as the landmark points that can provide an absolute reference for inertial odometry in large-scale indoor environments, and a dynamic time-warping (DTW) method was developed to further improve the performance of landmark detection and fingerprinting database generation. 3. An enhanced multilayer perceptron network (MLP) was proposed to perform an autonomous error evaluation of crowdsourced trajectories by extracting the spatiotemporal features of the collected trajectories, which can effectively improve the efficiency of the final Wi-Fi fingerprinting database construction and enhance the performance of database matching. 4.