# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 2 Related Works
### Multi-Source-Fusion-Based Positioning Solution  
Integrated navigation techniques have become increasingly popular in complex indoor environments, due to their improved robustness and precision, compared with single-location sources. Existing integration approaches, such as the Kalman filter (KF), the extended Kalman filter (EKF), and the particle filter (PF), are applied as typical integration methods for multi-source integrated navigation and localization. For large-scale indoor positioning based on wireless signals and MEMS sensors, it is a challenge to efficiently design integration models for different positioning sources according to local environments. Furthermore, controlling the quality of each positioning source and adaptively adjusting the fusion weights in the integration model are a problems to be faced. [PERSON] et al. [17] developed a robust dead-reckoning (DR)/Wi-Fi fingerprinting/magnetic matching (MM) fusion framework using a three-step quality-control strategy to enhance the localization performance from the signal-source level. The estimated experimental results showed that the proposed approach significantly reduced the root mean square (RMS) of the localization error by percentages between 13.3% and 55.2% in the tested environments and motion modes. [PERSON] et al. [38] used a combination of a mobile-generated Wi-Fi navigation database and a corner-based geomagnetic matching approach to improve localization precision. Gauss-Newton algorithms were developed for trajectory optimization, and DTW and the Pearson correlation coefficient (PCC) were proposed for corner-matching algorithms. Finally, the proposed algorithm framework was evaluated on two open datasets, with good performance. [PERSON] et al. [39] proposed a large-scale acoustic indoor localization system that applied the integration of BLE nodes, acoustic modules, and MEMS sensors. They applied an integrated acoustic propagation framework combining frequency-division multiple access, time-division multiple access, and space-division multiple access, respectively, for acoustic signal enhancement, and the information from BLE and the floor plan was further fused by the enhanced PF. [PERSON] et al. [40] improved the performance of conventional PF by applying the optimal estimation results in particle swarm to provide a particle selection reference for state-vector estimation. In addition, an adaptive optimization firefly approach was presented to avoid the local-optimum problem. The experimental results could realize an average positioning error within 0.5 m.  
Based on the current literature, multi-source fusion is regarded as an effective way to implement LBS function in large-scale indoor areas, and existing multi-source-fusion-based positioning systems suffer from the following limitations that need to be addressed: (1) The MEMS sensor-based position and orientation system (POS) is affected by cumulative error and magnetic interference in complex indoor environments; (2) Traditional fingerprinting-based approaches require labor-consuming collection processes, while the performance of crowdsourcing-based approaches is limited by the uncontrollable accuracy of crowdsourced trajectories; (3) Effective and adaptive combinations of different location sources are needed to maintain positioning accuracy in complex and large-scale indoor spaces. In our work, Wi-Fi FTM, Wi-Fi fingerprinting, BLE, QR codes, and MEMS sensors are intelligently integrated to provide a large-scale positioning solution aimed at complex and various indoor scenes. Wi-Fi FTM is more suitably applied in indoor open areas to avoid NLOS and multipath propagation, while the Wi-Fi fingerprinting approach is more suitable for applications in narrow indoor areas than in open indoor areas.