# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 2 Related Works  
Studies for indoor positioning through RSSI have been proposed for optimizing the LDPLM, which represents the relationship between RSSI and distance. [PERSON] et al. [25] explain an error correction algorithm to optimize the path loss of the propagation model. [PERSON] et al. [26] propose a coefficient adaptation algorithm that calculates and adaptively updates the coefficients of the LDPLM through error estimation in the existing fixed coefficient model.  
For accurate RSSI-based indoor positioning, various methods have been proposed to correct signals containing noise [27; 28; 29]. [PERSON] et al. [27] removed an outlier to enhance the accuracy of the location information system. Outliers are detected using the modified Thompson Tau test, which identifies points significantly distant from the mean by more than the standard deviation in a set of coordinates. Coordinates identified as outliers are removed to improve location accuracy. [PERSON] and [PERSON] [28] utilized a KF (Kalman filter) to correct the RSSI while maintaining a fast response time of the signal to reduce the noise in raw RSSI. [PERSON] et al. [29] reduced RSSI through a weighted sliding window. The weighted sliding window assigns weights to each point in the window and corrects the estimate by summing the weights of all points. This method significantly reduces noise. [PERSON] et al. [15] minimized RSSI error through KF and path loss model-fitting methods and improved indoor localization accuracy through trilateration. When applying the RSSI correction techniques from these studies to real indoor positioning experiments, trilateration using three relatively stable RSSIs in the central region of the triangle allows for periodic and low-error-rate localization. However, the closer the receiver is to one AP, the farther away it becomes from the other two APs. At this time, the RSSI of the distant AP is very unstable, so trilateration produces unstable positioning results. Trilateration encounters challenges in accurately estimating the receiver's position in specific regions, leading to an identified issue of overall system-wide degradation in positioning accuracy.  
Recent research in wireless signal-based indoor positioning has explored fusion technologies using additional devices to address the issue of degraded accuracy in vulnerable regions [21; 30]. In [21], a positioning technology combining trilateration and dead reckoning is proposed. [PERSON] and [PERSON] [30] suggest a Hybrid Indoor Positioning System (IPS) using visible light communication and trilateration. However, fusion technologies often require additional devices and technical integration for the system, leading to increased costs and design complexity. Research has also been conducted to improve indoor positioning accuracy using multilateration, calculating the position based on the signals from four or more APs [31]. However, applying multilateration in RSSI-based indoor positioning makes the system more susceptible to noise and introduces the challenge of increased computational complexity during position estimation.  
Table 1 shows the comparisons of previous studies for trilateration-based indoor positioning through RSSI. Almost all existing studies employ correction methods for RSSI noises or removal methods for outliers. However, only a few studies present position correction methods.