# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
###### Abstract
If the map is large, it will take a long time to match with a certain probability of matching failure (depending on the efficiency and complexity of the algorithm) [24]. In addition, the magnetic field strength is also time-varying [6], and is also vulnerable to the impact of environmental topology [6]. Based on the above analysis, Wi-Fi, UWB, visible light, geomagnetic and other positioning technologies have certain limitations in actual indoor scenes. In ordinary Wi-Fi fingerprint positioning technology, there are problems such as poor positioning accuracy and stability. In Wi-Fi RTT technology, the information on underlying hardware and software needs to be provided by the mobile phone supplier. At the same time, it is difficult to eliminate multipath at the communication level. In UWB technology, high positioning accuracy is easy to achieve. However, its cost will also increase. In most smartphones, the UWB module is not integrated inside the smartphones. At the same time, poor positioning accuracy will occur in some electromagnetic interference scenarios. For visible light positioning technology, there is a significant computational requirement on the mobile phone end. Poor real-time performance and vulnerability to environmental lighting are still the main problems that have limitations on practical applications. In geomagnetic positioning technology, the establishment of a fingerprint database is very time-consuming and laborious. At the same time, positioning performance is unstable over time. In a word, decimeter-level positioning accuracy and mass mobile phone access are still the core issues of indoor positioning. Researchers are now investigating the possibility of using acoustic signals for indoor positioning [25; 26; 27; 28]. In the article [29], an actual linear frequency modulation (LFM) signal was applied as the positioning source (LFM signal is also called chirp signal). In the system, chirp acoustic signals are emitted by smartphones, which limits the capacity of smartphone users. In the article [30], the author estimated the TOA of the chirp signal. However, the authors did not achieve positioning for smartphones. In the article [31], the author usesindoor acoustic fingerprints to achieve room-level positioning and differentiation. However, the actual application is greatly affected by environmental noise because of lacking an acoustic base. In the article [32], a chirp signal increasing linearly in frequency is used to code the one. Chirp signal decreasing linearly is used to code the zero. When the acoustic node information is decoded, it is considered that the smartphone and the acoustic node are in the same area. However, the system cannot obtain TDOA information. So, decimeter-level positioning cannot be achieved. In the article [33], the author proposed a transmission scheme of time division multiple access (TDMA) plus frequency division multiple access (FDMA). However, the number of acoustic nodes in this scheme is limited because of hardware conditions. It is difficult to deploy acoustic nodes in practical applications. In the article [34; 35; 36; 37; 38], the authors use a microphone array module to locate the target. However, the system is suitable for robot platforms and is not suitable for users in daily life. In the article [39], the authors apply the measurements of TDOA as a fingerprint to locate the sound source. The capacity of users is limited in such systems. At the same time, the TDOA fingerprint positioning method requires collecting fingerprint information in advance, which is time-consuming and laborious. In the article [40], laser signals are utilized to approximately synchronize the acoustic base station with the mobile phone. However, the laser signal is affected by sight distance conditions, and the practical application of this system is not reliable. In this paper, a smartphone indoor positioning system based on acoustic TDOA measurement is presented, and its performance is evaluated by static positioning experiments.