# Multi-Information Fusion Indoor Localization Using Smartphones
## 6 Conclusions  
In this paper, we propose a CHAN-IPDR-ILS indoor localization system that incorporates CHAN estimation and an improved PDR method. In the CHAN-IPDR-ILS, ultrasonic localization is implemented using the CHAN algorithm. In the localization system, we propose a step length estimation model that estimates location using the previous two steps and current accelerations. To ensure the importance of the current state, the maximum importance for the previous step length is set. In addition, we propose a heading direction correction method that adds a correction angle and a compensation error. The proposed method can correct the heading direction and mitigate issues of equipment heterogeneity. Finally, in the fusion localization, the distance threshold is set to mitigate accidental errors during acoustic localization. Pedestrian localization is then determined based on the distance confidence level with acoustic estimation and improved PDR estimation. We conducted experiments at two trial sites in which the first scene covered approximately 96 m\({}^{2}\) and the second scene covered approximately 224 m\({}^{2}\). Two different height volunteers with a VivoY85a smartphone and an Honor60 smartphone captured data along the survey path at each scene, respectively. The experimental results demonstrate that the proposed method can achieve a higher location accuracy compared to existing methods using different smart devices. In addition, we compared the computational complexity of the proposed CHAN-IPDR-ILS in two scenes. The CHAN-IPDR-ILS has comparable performance with the CHAN estimation. The proposed localization system satisfies the public demand. The proposed CHAN-IPDR-ILS provides high generality and flexibility for different devices and scenarios and is compatible with smartphones in terms of low cost and high accuracy. With excellent localization performance, low cost, and short execution time, it can be concluded that the proposed CHAN-IPDR-ILS is a charming indoor localization method for practical deployment. Future research directions include more complex environments, such as more shadows and reflections. The smartphone is hung for answering calls, and is carried in the pocket, waist, etc. Inspired by deep learning, another direction may be considered based on a lightweight deep learning method.  
Conceptualization, S.Y.; Data curation, C.W.; Funding acquisition, S.Y. and J.X.; Methodology, J.X.; Project administration, Y.J.; Resources, X.L.; Software, X.L.; Writing--original draft, C.W.; Writing--review and editing, Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China: Grant 62061010, Grant 62161007, Grant 61936002, Grant 62033001, and Grant 6202780103; Guangxi science and Technology Project: Grant AA20302022, Grant AB21196041, Grant AB22035074, and Grant AD22080061; National Key Research and Development Program: Grant 2018 AA100305; Guilin Science and Technology Project: Grant 20210222-1; Bagui scholar program Fund (2019A40) of Guangxi Zhuang Autonomous Region of China; The Project of Improving the Basic Research Ability of Young and Middle-aged Teachers in Guangxi Universities (2022 KY0181); Guangxi Key Laboratory of Precision Navigation Technology and Application: Grant 202206 and Grant 202210; Innovation Project of Guangxi Graduate Education: YCSW2022291.  
Not applicable.  
**Data Availability Statement:** All test data mentioned in this paper will be made available on request to the corresponding author's email with appropriate justification.