# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 1 Introduction  
With the development of modern society, people's demands for location services is becoming increasingly urgent [1]. The Global Navigation Satellite System (GNSS) [2] can provide users with a high-precision positioning service in the outdoor environment. However, in the indoor environment, because the GNSS signal is blocked by humans, buildings and many other factors, it becomes so weak that the positioning accuracy will be greatly reduced. In order to solve indoor positioning problems, many kinds of method have been studied, such as Wi-Fi positioning [3, 4], Bluetooth positioning [5, 6], ultra-wideband (UWB) [7, 8], radio frequency identification (RFID) [9], infrared [10], ZigBee [11], etc. Every positioning method is capable of providing an indoor location service for people, especially the UWB technology which can reach sub-meter accuracy in the indoor open areas [8]. But these approaches require additional signal transmitting devices, which means that people cannot obtain their position if there is no such equipment in the environment. Moreover, these devices are usually expensive which will make the cost very high if we apply these technologies to a large-scale environment. Pedestrian dead-reckoning (PDR) [12, 13] technology can use an accelerometer, gyroscope, and magnetometer to obtain continuous positions instead of installing the signal transmitting stations. But the positioning error will accumulate quickly because of the initial position error, heading angle estimation error and other measurement errors. Therefore, it is necessary to integrate PDR with other positioning methods to restrict error accumulation. In 2000, [PERSON] et al. [14] conducted an experimental analysis in the corridor environment and proposed that the indoor magnetic field can be used for indoor positioning. Then, experiments in [15] studied in detail the stability and distribution of an indoor magnetic field, pointing out that it is stable and reliable. All of these related research works provide a solid theoretical basis for the application of the magnetic field for indoor positioning. In recent years, researchers have found that utilizing indoor magnetic field can achieve high positioning precision at almost no investment that are safe and infrastructure-free compared with other technologies [16]. These characteristics make indoor geomagnetic positioning has great research value and also gradually become the research hotspot. Today there are many kinds of geomagnetic positioning methods, which can be divided into two main types: sequence-based magnetic matching positioning (SBMP) and single-point-based magnetic matching positioning (SPMP). The dynamic time-wrapping algorithm (DTW) [17] is a representative positioning algorithm of SBMP. In [18; 19], experimenters applied the DTW algorithm to magnetic matching by comparing the similarity of different trajectories and employed this magnetic positioning method on smartphones. The results show that the accuracy of 80% test positions is approximately 2 m. Generally speaking, the SBMP method has high positioning precision, but it relies on stable magnetic data sequences. However, the magnetic data sequence will generate large fluctuations due to the device heterogeneity. Even though some methods have been studied to solve this problem, they cannot be implemented in real-time positioning [20; 21; 22]. What is worse is that SBMP is restricted by the trajectory and walking speed [16; 23]. In other words, if participants want to get their position, they should walk on the trajectory where they collect magnetic field data before, which means that this method will perform poorly in the open area. By contrast with SBMP, the SPMP method uses the geomagnetic features data at the signal points rather than the magnetic sequence data and there is also no limitation to the speed and trajectory of pedestrian walking. Therefore, it is more flexible and easier to implement compared with SBMP method.