## 1 Introduction  
The global positioning system (GPS) is a widely used tool for positioning services, and many smart devices are equipped with GPS functionality. However, GPS signals require a clear line-of-sight (LOS) to the satellites for accurate communication. In indoor environments or areas with significant obstructions, GPS signals are often blocked by barriers such as buildings, resulting in poor signal reception and diminished positioning accuracy [1]. Consequently, research into wireless sensor-based indoor positioning systems (IPS) has become increasingly prevalent in recent years [2, 3]. Various technologies have been explored for indoor positioning, including radio frequency identification (RFID), infrared sensors, Zigbee, Wi-Fi, Bluetooth, and other non-visual sensors. These systems can transmit data and work with algorithms to achieve positioning even in non-line-of-sight (NLOS) environments. For instance, the LANDMARC system [4] uses RFID tags deployed at fixed indoor locations as reference points. A reader collects the received signal strength (RSS) values from these tags, compares them with those from a target tag, and calculates the target's coordinates. Due to the inherent fluctuations in RSS, the positioning accuracy is limited. An improved method proposed in [5], which involved testing in a 10 \(\times\) 10 m indoor space using 4 readers and 20 reference tags, resulting in a positioning error of approximately 40-100 cm. Another approach, described in [6], utilized infrared sensors to detect motion. When an object moves within a sensor's detection range, the sensor outputs a \"1\" more than twice within ten seconds; otherwise, it outputs \"0\" once every ten seconds. In an 8 \(\times\) 4 mtest space with 10 infrared sensors, the collected data was standardized into activity strength (AS) parameters. These were analyzed using a neural network's multi-layer perceptron (MLP), a conditional probability disperser, and a support vector machine (SVM) to estimate the presence and rough position of a target. The method could detect the target's area with an accuracy range between 0.92 m and 0.99 m, though precise localization was not achieved. In [7], Zigbee-based positioning also used RSS values but faced similar accuracy challenges due to signal interference. To address this, the authors proposed two corrective techniques: the neighbor area majority vote priority correction (NAMVPC) and the environment parameters correction (EPC). NAMVPC used coded area references from sensors in different rooms to determine the target's zone, while EPC leveraged historical data to refine current estimates. The final average positioning error was approximately 40 cm. Wi-Fi-based systems also utilize RSS-based fingerprinting methods, as detailed in [8]. These systems involve two phases: offline sampling and real-time positioning. In the offline phase, RSS data is recorded to build a reference database. During real-time use, current RSS values are compared to this database to estimate the user's location. In an indoor 20 \(\times\) 12 m space, this method yielded an average error of about 1.5 m. Bluetooth-based indoor positioning, described in [9], employed Bluetooth 4.0 devices installed on the ceiling as anchor points. When a target device enters the area, the system selects the three anchor points with the strongest signals and uses triangulation for localization. Despite the method's simplicity, it achieved a positioning error between 50 cm and 1 m.  
All of the above technologies primarily rely on RSS for indoor positioning, which is susceptible to signal attenuation, obstruction, and multi-path interference, limiting accuracy. These systems often require the deployment of multiple reference points and extensive calibration through database construction to improve performance. In contrast, UWB technology has emerged as a promising solution for indoor positioning.