## 1 Introduction  
In recent years, the accelerated growth of Internet of Things (IoT) devices, along with rapid development in machine learning (ML), has substantially increased the demand for location-based technologies [1]. The advancement of indoor mobile robots, driven by ongoing innovations in sensor technologies and information systems, presents substantial economic opportunities in sectors such as industry, logistics, and transportation [2]. Mobile robots are progressively replacing manual tasks related to cargo handling, monitoring, and hazardous operations, while ensuring continuous automation and unmanned control [3]. In open environment, GPS technology is predominantly used to determine location [4, 5]. However, its effectiveness diminishes indoors, as satellite signals are significantly weakened when passing through walls, roofs, and other building structures. This leads to a reduction in accuracy and can even make device localization impossible. An alternative approach is the use of Low Earth Orbit (LEO) satellite systems, which, being closer to Earth, provide location services that support GPS functions [6, 7]. With a stronger signal compared to high-orbit satellites, LEO systems are promising for improved coverage and accuracy in dense urban environments. However, their ability to penetrate buildings remains limited, potentially requiring additional solutions for accurate indoor positioning. Researchers have proposed various types of infrastructure for indoor positioning systems, including wireless sensor networks (WSNs), optical systems, radio frequency technologies, and others. Currently, commonly used indoor positioning technologies encompass visible light communication (VLC), infrared (IR), light detection and ranging (LiDAR), and computer vision [8; 9; 10]. Although LiDAR and computer vision are effective technologies, they demand high-cost hardware and substantial computing resources. In contrast, IR and VLC systems face limitations due to the short operational range and their dependence on external lighting conditions and transmission angles [11]. These factors reduce their reliability in environments with variable lighting and intricate room geometries [12]. Wireless technologies employed for node localization include Wi-Fi, Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), Radio Frequency Identification (RFID), Zigbee, and LoRaWAN [13; 14; 15; 16; 17]. Among the above, LoRaWAN emerges as a promising low-power solution, which is particularly beneficial for mobile devices. In comparison to Wi-Fi, LoRaWAN provides more stable indoor connectivity due to its high signal penetration through obstacles, while global navigation satellite systems (GNSS) and Wi-Fi are not optimal in terms of power consumption for outdoor and indoor applications. Our study investigates the use of LoRa technology for indoor localization using RSSI fingerprinting. Although LoRa technology has been widely studied for outdoor use, research on its indoor application remains limited. In the articles [18; 19], the authors identified LoRa as one of the promising new technologies for indoor localization. [PERSON], [PERSON], et al. compared LoRa technology with popular wireless technologies such as BLE and WiFi for indoor localization by analyzing their performance in various indoor line-of-sight and non-line-of-sight scenarios [20]. The authors concluded that LoRa is a viable choice for indoor localization, especially in scenarios requiring low power consumption and wide coverage. In wireless sensor networks, there are localization methods based on parameters such as angle of arrival (AoA) and time of arrival (ToA) [21; 22], which have limitations connected with high equipment cost and insufficient accuracy in the presence of interference and multipath signals. Although [23; 24] discuss localization approaches that estimate the phase difference using the AoA identification using I/Q and phase interferometry, there are some nuances in their use indoors.