# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 1 Introduction
This method usually does not have high positioning precision if there are few geomagnetic features. But the particle filter algorithm makes up for this shortcoming and has high positioning accuracy, which is also a typical single-point-based fingerprint positioning algorithm [24; 25]. In related research, [PERSON] et al. [26] used the Hausdorff distance and Pearson correlation coefficient [27] to control the initial position error and accelerate the convergence speed of the filter. In [28], an adaptive particle filter algorithm was proposed in geomagnetic positioning. The number of particles in the experiment can adjust according to the different features and the average positioning accuracy was 0.51 m. [PERSON] et al. [29] combined the Wi-Fi signal with magnetic field data and designed a two-pass bidirectional particle-filtering process to improve precision, which achieved a mean location accuracy of 0.9 m in the office environment. Although geomagnetic positioning technology based on the particle filter can achieve high accuracy, it suffers from the particle degradation problem. Increasing particle numbers is one of the approaches to solve this problem, for example, the number of particles in [28] even reaches 3000. A large number of particles will result in a large computational load and limit its application on smart terminals. Therefore, how to better solve the particle degradation problem is the key to improving the performance of the particle filter. In this paper, we choose the SPMP method for geomagnetic positioning because it is more flexible than the SBMP method. Five magnetic features data are extracted to ensure the reliability of geomagnetic fingerprint and the accuracy of the geomagnetic multi-features positioning algorithm. Moreover, the Mahony complementary filter (MCF) algorithm is applied to calculate the heading angles in PDR technology. In order to optimize the particle degradation problem, we use the gene mutation method in the genetic algorithm [30] and the method of reconstructing the particle set by combining the geomagnetic positioning results to update the particle set. Lastly, we integrate the PDR and the geomagnetic positioning by using the genetic-particle filter (GPF) algorithm and carry out several experiments to evaluate its performance. The contributions can be summarized as follows:1. We transform the geomagnetic data into the geographic coordinate system (GCS) and extract five geomagnetic features data. These features data can improve the specificity of geomagnetic fingerprint and the accuracy of the devised geomagnetic multi-features positioning algorithm. 2. We design the optimization mechanism for the particle degradation problem, which utilizes the genetic mutation method to increase the diversity of the resampled particles and uses the particle set reconstruction method by combining the geomagnetic positioning results to improve the particles' reliability. The proposed mechanism better ameliorates the particle degradation problem;
3. We propose the fusion-positioning method that utilizes the PDR positions as the system state and uses the geomagnetic positions as the system observation based on the genetic-particle filter. The mean positioning error is 1.72 m and the root mean square error is 1.89 m.  
The remainder of the paper is organized as follows: Section 2 gives a detailed description of each module, such as PDR technology; Section 3 evaluates the performance of the proposed methods; Section 4 summarizes the conclusions and discussion.