## 2 Related Method and Work  
### Dual-Receiving Acoustic Signal Processing  
As for indoor sensor systems, their performance is commonly limited by the parameters of sensors. For example, Kinect [1; 2; 3; 4; 23], Leap Motion [24], RealSense [25], Halcon [26], and so on need to redesign the on-site deployment scheme according to the equipment parameters or the environment. This limitation is negative but unavoidable. Due to the effective viewing range or orientations from the depth camera or other IoT devices, it will produce a smaller effective area even if all of the equipment is deployed as well as possible. Meanwhile, users are seldom aware of whether their locations are needed by the indoor sensor system or not in time. Therefore, indoor localization systems need to monitor the user's position and find real-time coordinates. Thus, judging by the indoor localization notifications, users can be placed within the effective range of the system with their cooperation, thereby making the indoor sensor system more effective. Take the system built by Kinect V2 as an example [2; 4]. The angle between the user and the camera of Kinect V2 in the real-world environment is not always proper. When the localization and notification process is not introduced, more training sets need to be produced and used for different orientations. This will definitely impose more development costs and is not conducive to the promotion of the system. At the same time, the effect of different kinds of sample sets for one independent situation is also unilateral. Therefore, an adaptive indoor localization system that can assist the indoor sensor network in notifying the user of their location is necessary, which will also improve the robustness of the indoor sensor system or the cognitive IoT networks. In addition, as for the research on the cognitive IoT network, it is also considerable to rearrange a reasonable signal processing method according to the scenarios or the equipment limitations. Especially for the cognitive IoT networks that need large-scale deployment, if the researchers build a complete data processing relationship or a fixed multi-modal model cognitive relationship, it will be too time-consuming [27; 28]. In fact, the cognitive ability of some multimodal data is not always clear, so it is necessary to combine the cognitive IoT network theory with the adaptive signal processing method to redesign the process more suitably under this circumstance [29]. For the acoustic anchors in some indoor localization systems [17; 20; 27; 28; 29; 30; 31; 27], researchers have tried to correct the positioning errors by using a variety of machine learning models or different compensation, selection, and correction mechanisms for NLOS scenarios [19; 20; 21; 5; 16; 15; 16; 17; 22]. The theoretical basis for the compensation or the correction mechanism for NLOS scenarios comes from two standpoints: one is to compensate for the loss according to the statistical error caused by NLOS scenarios. The second method is to adjust the algorithm process in the whole system when necessary. In detail, the first method comes from physical models and the holistic analysis of the environments, which is often burdensome to implement or popularize. Meanwhile, the second method is often more commonly used. Its foundation comes from the unexpected conclusions after experiments: for those systems similar to the Linloc System or ZKLocPro System, if no more than one NLOS acoustic signal is deleted in one localization loop, and only LOS acoustic signals are used for positioning, the positioning accuracy will be improved in the indoor localization system [5; 22]. Therefore, the second method is supposed to be adopted to complete the correction in the NLOS scenario. The core research objects are the interaction process between the main anchor and the other four acoustic anchors in each localization loop and the acoustic signal obtained by the recording process.