## 1 Introduction
The RSSI-based approach, however, suffers from the impact of multipath reflections and non-line-of-sight (NLOS) effects, which limit its accuracy in complex indoor environments. While RTT ranging provides meter-level precision between mobile terminals and Wi-Fi access points (APs), it requires support for the Wi-Fi Fine Time Measurement (FTM) protocol, which is currently only available on a small subset of mobile devices. Additionally, the performance of RTT can degrade in high-traffic scenarios or areas with dense personnel [17]. Fingerprinting algorithms have emerged as an alternative method that does not require knowledge of the position of local wireless stations. Instead, these algorithms acquire RSSI vectors from prior indoor environments to provide location information. Fingerprinting offers universal positioning performance and is not limited by the density of users or hardware support. However, it requires a large database of prior RSSI fingerprints and is affected by the quality of the matching algorithm used. Furthermore, Wi-Fi fingerprinting may struggle to maintain accuracy in open spaces with degenerate features [18]. Overall, while WPS offers several advantages for indoor localization, each approach has its limitations. The RSSI-based approach suffers from multipath and NLOS effects, while the RTT approach requires specialized hardware support and may degrade in high-traffic scenarios. Similarly, while fingerprinting algorithms offer universal positioning performance, they require a large database and may suffer from degraded performance in open spaces with degenerate features. The acoustic positioning system (APS) is a promising technology for indoor localization that calculates the distance between a signal transmitter and receiver by measuring the propagation time of sound waves. This mode of operation is similar to that of the Global Navigation Satellite System (GNSS) and offers several advantages, including high-precision, good compatibility, and a high-capacity. As such, APS provides new possibilities for indoor positioning using consumer-grade smartphones [19]. One of the main advantages of APS is that it can utilize the built-in microphone of smartphones to achieve high-precision positioning without requiring any additional hardware modifications. This feature makes it a cost-effective solution for indoor localization. The technology has the ability to achieve decimeter-level ranging, sub-meter level positioning, and wide signal coverage, offering superior precision compared to other indoor positioning systems. However, the performance of acoustic ranging is also impacted by factors such as multipath propagation, NLOS, and complex pedestrian motion modes [20]. These factors can lead to errors in the distance calculation and ultimately affect the accuracy of the system. Therefore, further research is needed to develop algorithms that can effectively mitigate the impact of these factors on the performance of the APS. In summary, the APS is a promising technology for indoor localization that leverages the built-in microphone of smartphones to achieve high-precision ranging and positioning. However, the impact of multipath propagation, NLOS, and complex pedestrian motion modes must be considered when deploying APS-based systems, and future research should focus on developing algorithms to address these challenges. Towards the large-scale indoor spaces, the cost of APS will increase with the area of real-world scenes. In some cases, the combination of different levels of positioning systems becomes an essential requirement. The mobile terminals integrated Micro Electromechanical System (MEMS) sensors are usually adopted to integrate or combine the existing positioning sources to achieve multi-source fusion-based localization purposes [21].