# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 1 Introduction
However, the standardized formula for position estimation is less accurate than fingerprinting and sensitive to signal instability and inaccuracy. Nevertheless, trilateration using RSSI is advantageous in certain environments, such as industrial warehouses where indoor structures or material positions are frequently changed. Indoor positioning for trilateration allows for a variety of applications and rapid positioning. However, trilateration causes significant error when the measured signals from APs are inaccurate. For successful indoor positioning with RSSI-based trilateration, it is essential to address the challenges of RSSI instability and inaccuracy. Additionally, if there are obstacles between a receiver and Aps, and the receiver is approaching one AP and moving away from the other APs, the receiver may obtain inaccurate signals from the APs; thus, RSSI-based trilateration may lead to inaccurate measurements. In this paper, we propose a proximity-based adaptive positioning (PAP) algorithm to reduce the location estimation error occurring in the proximity region to AP and enhance the positioning accuracy in RSSI-based trilateration indoor positioning. The PAP algorithm is an adaptive method that identifies whether the receiver is in proximity to a specific AP and adjusts the positioning technique accordingly based on the identified state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects RSSI to reduce RSSI instability and increase positioning accuracy. The adaptive location estimation step dynamically selects the positioning technique based on the identified proximity. If proximity to the AP is not confirmed, location is estimated from the existing trilateration techniques. Conversely, if this step identifies an AP as being in close proximity, the location is estimated by a single RSSI value collected from the nearest AP using Modified Proximity (MP) technology. Finally, the post-processing step applies an effective filtering algorithm to improve the indoor positioning accuracy. The main contributions of this paper can be summarized as follows:  
1. This paper calculates the optimal threshold, which is an evaluation standard for identifying proximity to a specific AP in the PAP algorithm, through quantitative measurements. By applying an optimal threshold to the PAP algorithm, indoor positioning accuracy is improved in regions with weak signals. 2. To reduce RSSI-based distance estimation errors, this paper finds the relationship between RSSI and distance from data collected in various environments with and without obstacles to signal reception and optimizes the parameters of LDPLM. 3. In this paper, we quantitatively evaluate signal filters to correct unstable RSSI and increase the accuracy of location estimation and design an effective filtering algorithm to improve indoor positioning accuracy. The paper is structured as follows: Section 2 describes the related research; Section 3 presents the proposed PAP algorithm; Section 4 shows experimental results; and finally, Section 5 provides conclusions and future research directions.