# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 2 Related Works
### MEMS Sensor-Based Positioning Solution  
There are two main MEMS sensor-based localization methods targeting pedestrians: INS and pedestrian dead-reckoning (PDR) structures. Both INS and PDR can obtain real time position, velocity, and attitude (PVA) information about the user for localization purposes. Normal PDR mechanization typically consists of four essential stages: gait recognition [20], step-length update [21], heading calculation [22], and location update [23]. Typically, PDR mechanization is the most widely used solution for pedestrian navigation, although the performance of PDR is limited by the cumulative errors of low-cost sensors, complex handheld and motion patterns, and indoor magnetic disturbance [24; 25]. An alternative approach to pedestrian navigation is the strap-down inertial navigation system (SINS), through which real-time 3D attitude and location can be obtained; however, the accuracy of SINS is also constrained by sensor errors and SINS cannot be applied for a long period of time [26].  
To limit the cumulative and drifting errors of SINS, various methods have been applied. The first applications of zero-velocity update technology (ZUPT) and zero-angular rate update (ZARU) were in the foot-mounted positioning system (FPS), which can significantly improve the accuracy of FPS. The performance of ZUPT and ZARU degrades when applied to mobile terminals, due to variation in handheld and walking styles of pedestrians, so other constraints are required in this case.  
[PERSON] et al. [27] analyzed the influencing factors of ZUPT performance and designed corresponding simulations and experimental analyses, with final experimental results demonstrating that the rate random walk of the gyroscope contributed most to the detection performance. [PERSON] et al. [28] proposed a novel zero-velocity cycle detection algorithm, which achieved ZUPT detection accuracy by extracting and removing pseudo-zero velocity cycles and pseudo-motion periods of 87.24%.  
The accuracy of the traditional PDR approach in the real world is usually constrained by low accuracy with respect to walking speed, cumulative errors and heading deviations due to magnetic disturbance, and changeable handheld modes. Previous researchers have developed and carried out integrated experiments to reduce errors in the various components.  
[PERSON] et al. [22] developed a novel two-step traceless Kalman filter that performed attitude error correction in two steps by estimating the magnetic vector and gravity vectors. In addition, the hidden Markov model (HMM) was applied to enhance the identification accuracy of ZUPT cycles and realize more accurate heading estimation and dead-reckoning performance.  
[PERSON] et al. [29] used a machine learning (ML) approach to classify changeable handheld modes of mobile terminals and to increase the precision of PDR by selecting appropriate gain values. [PERSON] et al. [30] focused on the pocket mode and swing mode of smartphones and analyzed the relationship between rotational motion, walking states, and heading information. An enhanced rotation method and a single-point algorithm were developed for heading estimation in pocket mode and swing mode, respectively, which effectively improved the final performance.  
[PERSON] et al. [31] proposed a novel algorithm for pedestrian walking-speed estimation by considering four different handheld modes of smartphones. Feature vectors were extracted from the collected motion data provided by the integrated sensors, and then the classified handheld modes were tightly integrated using an adaptive gait-recognition approach. The final experimental results proved that the presented framework realized a classification precision of 98.85% and that speed-estimation error was less than 0.061 m/s.