# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 1 Introduction
The authors assessed system performance through real-world experiments, comparing their approach to traditional fingerprinting methods and PDR alone. The results showed that the proposed system, using a minimal number of RPs, significantly enhanced localization accuracy compared to other methods. [PERSON] et al. [13] proposed a real-time indoor localization system utilizing Bluetooth Low Energy transmitters and smartphones as receivers. The method employs Radial Basis Functions (RBF) for accurate indoor positioning. Implemented within an Android application, the system supports offline data collection. The indoor area model is constructed directly on the smartphone, enabling real-time position estimation during the online phase. This approach allows for continuous model updates, incorporating new signal and position data. The adaptability of RBF interpolation permits the system to accommodate changes in the indoor environment, such as new furniture, without the need for complete retraining [14]. Additionally, [PERSON] et al. explored the reduction in calibration efforts in indoor localization by using BLE beacons [15]. [PERSON] et al. [16] developed a system to construct a Bluetooth Low Energy fingerprint during an offline phase. [PERSON] and [PERSON] [17] suggested a deep neural network to analyze the distribution of RSSI samples during the offline phase. In the online phase, the user's position is estimated by comparing real-time Received Signal Strength Indicator values with the stored fingerprint using K-nearest neighbors and Weighted K-nearest neighbors algorithms. [PERSON] et al. (2018) developed an indoor localization approach using Android mobile devices. Experiments conducted in various indoor environments, including a corridor and a sports hall, demonstrated the system's ability to accurately determine the user's position. The study focused on evaluating the precision of fingerprint-based positioning, utilizing RSSI reference maps generated from measurement data. It is established that the human body interacts with BLE signals ([PERSON] et al., 2018). The conductive properties of the human body cause it to scatter longer radio signal wavelengths and reflect or attenuate shorter wavelengths, thereby affecting radio signal propagation between a transmitter and receiver ([PERSON] et al., 2018). This can lead to inaccurate RSSI measurements, resulting in erroneous position estimates in RSSI fingerprinting-based localization ([PERSON] et al., 2018). [PERSON] et al. (2018) employed the fluctuations caused by human body shadowing from three different BLE sources to detect human bodies. [PERSON] et al. (2018) tackled the challenge of body shadowing in indoor localization using RSSI-based fingerprinting. A solution is proposed that includes angle estimation through landmarks, a decision tree classifier for motion mode detection, and an RSSI compensation model to correct body-induced signal distortions. [PERSON] et al. suggested a data-augmentation method to improve indoor location estimation using radiofrequency fingerprinting ([PERSON] et al., 2018). The approach employs a Conditional Generative Adversarial Network with Long Short-Term Memory to generate synthetic fingerprint data that enhances sparse training datasets. The experimental results demonstrate an improvement in localization accuracy compared to non-augmented fingerprinting data. [PERSON] et al. (2018) investigated the impact of human presence and movement on the accuracy of RSSI-based localization techniques, which are susceptible to significant errors due to signal strength variations caused by human interference. A comparative experimental analysis was conducted on different localization methods under conditions with and without human movement. These findings highlight the necessity for further refinement of these methods to mitigate the effects of human-induced RSSI fluctuations in practical localization applications.