# Multi-Information Fusion Indoor Localization Using Smartphones
###### Abstract
In addition, the pedestrian motion information is captured by motion sensors such as accelerometers, gyroscopes, and magnetometers installed in smartphones. Pedestrian dead reckoning achieves good localization accuracy in a short time, which is suitable for the real-time localization tracking of fast-moving users. However, the PDR algorithm relies on other positioning techniques to provide an accurate initial location, and there are cumulative positioning errors for long-term localization. Therefore, PDR technology is generally integrated with other indoor localization technologies [26]. Overall, it is difficult to trade off compatibility, cost, and accuracy for indoor localization. Most current localization systems are infrastructure-based, which makes them impossible to implement on a large-scale application. More efforts must be made to determine how to achieve localization accuracy, compatibility with smartphones, cost, and real-time performance in indoor localization systems. These issues also create more challenges with regard to positioning, which can be summarized as follows:  
* How can the tradeoff of cost and precision be mitigated in indoor environments? High precision often requires high costs, but in real applications users typically hope to achieve high precision performance at low cost. Therefore, low cost and high precision are the core key research topics in localization. * How can heterogeneous equipment be achieved? The application scope of indoor localization is related to the requirements for device heterogeneity. A good method must be developed for different devices. Therefore, device heterogeneity remains a great challenge. * How can the generality of indoor localization be achieved? Both environments and human behaviors have a strong influence on positioning. Therefore, eliminating this interference remains a challenge for localization. In localization methods, research has focused on scene analysis algorithms and triangulation algorithms. Scene analysis algorithms include the offline stage and online stage. In the offline stage, a fingerprint database is constructed in the whole scene in advance. In the online stage, localization is achieved to find the location of the maximum probability by matching the fingerprint database. [PERSON] et al. [27; 28] proposed KD-CNN-IPS and TAKKD-CNN-IPS to achieve good performance, respectively. However, the fingerprint database needs to update once the scene changes, it requires large manpower. The triangulation algorithm includes the time of arrival (ToA) [29; 30], angle of arrival (AoA) [31; 32], and time difference of arrival (TDoA) [33; 34]. The time of arrival requires accurate timing synchronization at the beacons and the target; it brings a great challenge for the high-cost and real-time localization performance. The angle of arrival estimation is achieved through the angles at which the signal arrives from the target to the beacons; it requires large antenna arrays and has a high-cost [10; 11; 12]. The time difference of arrival estimates the time differences at the beacons; the target location is the intersection of many hyperbolic curves from the time differences. It has low calculation complexity and low cost; no time synchronization is required. Among TDOA-based location algorithms, the CHAN algorithm [35; 36] does not need an initial value and can reach the lower limit of Cramero. To address these challenges, we propose a low-cost and high-precision indoor pedestrian tracking method based on the compatibility of ultrasonic signals and smartphones. In this method, we fuse the localization approach based on ultrasonic signals and the improved PDR method. Data transmission and reception are achieved using smartphones, and ultrasonic localization is implemented using the CHAN algorithm. The proposed method mitigates the accidental errors in ultrasonic localization and the cumulative errors of PDR. The primary contributions of study paper are as follows:  
* A dynamic improved PDR method. In this article, we propose a dynamic improved PDR method.