## 1 Introduction  
### Problem and Scope  
Not only is the application scope of and demand for indoor sensor networks gradually increasing, but their core Internet of Things (IoT) devices are becoming ubiquitous technology as well. The commercial value of these sensors and devices and the number of developers engaged in their development are both expanding. Meanwhile, researchers are continuously improving their performance and crucial supporting development tools. Developers and researchers are actively sharing their ideas or code in academic journals or magazines, technical communities, online websites, or conferences, thus objectively promoting their development. Nowadays, indoor sensor networks are used in many settings, such as indoor multimedia exhibitions [1], campus teaching applications [2], agricultural applications [3], and medical auxiliary services [4]. To enable these systems to better achieve their main tasks, it may be imperative to improve their key processes according to the actual demands on indoor sensor networks [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Among them, some IoT devices require that users be in an effective and reasonable zone due to the limitations of the sensors. Once the user's location moves outside this area or range, the system or the corresponding IoT device will not be able to play its due role. Therefore, if the user is not supposed to be there at that moment, a necessary notification needs to be issued in time. To inform the real-timeposition of users promptly, an indoor localization system based on acoustic signals with a specific frequency band can be adopted when the requirements for positioning accuracy are not too strict. It is also necessary to interact with other core modules of the indoor sensor system, so as to improve the robustness of the original sensor network and promote the users' experience [7]. In recent years, pedestrian dead reckoning (PDR) [8], Bluetooth [9], ultra-wideband [10], and acoustic signals [11] have been widely used in localization systems and achieved fabulous results while generating considerable economic benefits. Among these core technologies, acoustic signals are becoming more and more popular because of their lower development cost, relatively high positioning accuracy, and marvelous portability compared with some other technologies. Meanwhile, the development of acoustic signals has begun to take shape in many outstanding aspects, which have also had multiple social impacts. As these systems [5; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] have gained more powerful functions, they are receiving more and more attention. The following takes localization systems based on TOA and TDOA as an example to analyze their core, stories, advantages, or disadvantages. Regarding TOA, [PERSON] et al. have proposed the Cricket System [12]. Its positioning accuracy is relatively precise, reaching 2 cm. However, the NLOS scenario has overt limitations, and there are difficulties in transplanting the system objectively. [PERSON] proposed the Beep System [13], which is compatible with several smartphones. After measuring, it is expected to achieve a positioning accuracy of within 60 cm. With the development of semiconductor and communication technology, the performance of smartphones is constantly improving, and their scope is also expanding. On this basis, [PERSON] and other designers implemented the BeepBeep System [14], proposing a two-way interactive ranging method that does not require a time synchronization facility and finishes the positioning process based on low-frequency band acoustic signals, achieving positioning accuracy of 3 cm. However, the delay periods caused by multiple interactions are so long that this system cannot support multiple users in a timely manner. During the main process, the user also has to tolerate annoying noises, and the positioning effect in NLOS scenarios is still poor. The standards and ideas for the chosen acoustic signal frequency band in these systems have greatly influenced subsequent developers.