# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 2 Technology of Indoor Localization
### UWB Module  
The BU01 UWB module with the DW1000 chip, shown in Figure 5, is designed for high-precision indoor positioning and real-time tracking. Operating within a wide frequency range of 3.1 GHz to 10.6 GHz, UWB technology can achieve centimeter-level accuracy, making it ideal for applications where precise localization is essential. This wide frequency range helps mitigate interference and multipath effects, ensuring reliable performance even in complex environments like hospitals and industrial facilities. The BU01 module is widely used in RTLSs for tracking patients, staff, and assets with an accuracy of up to 7 cm [56].  
UWB-based localization systems rely on key components such as anchor nodes (ANs), which are fixed reference points with known coordinates, and UWB tags, which are carried  
Figure 4: ZigBee shield module.  
Figure 5: BU01 UWB module (DW1000).  
by users or attached to objects. These systems use the time of arrival (TOA) method to measure the time taken for signals to travel from the tag to the anchors, determining the tag's position through trilateration. This process allows for highly accurate positioning, even in NLOS conditions, where obstacles may block direct signal paths. To address challenges like NLOS conditions and computational complexity, deep learning techniques, particularly long short-term memory (LSTM) networks, are integrated with UWB systems. LSTM networks, a type of recurrent neural network (RNN), process sequential data effectively, making them suitable for handling time-series distance measurements [39]. In a UWB-LSTM system, TOA-based distance measurements are collected by UWB anchors and initially processed through trilateration to estimate the user's position. These estimates are further refined by an LSTM network with two layers, improving accuracy and reducing localization errors [56].  
The LSTM model is optimized with a learning rate of 0.0001, the Nadam optimizer for balancing speed and accuracy, and the mean squared error (MSE) loss function to minimize prediction errors. A batch size of 5 ensures efficient memory use and faster convergence, while 256 hidden nodes provide optimal complexity for accurate predictions. Simulation results show that the UWB-LSTM system significantly outperforms conventional methods like linearized least square estimation (LMSE), fingerprint estimation (FPE), and maximum likelihood estimation (MLE), achieving a mean localization error of just 7 cm, even in NLOS conditions [56].  
The BU01 UWB module and UWB-LSTM systems offer numerous benefits, including high accuracy, adaptability to dynamic environments, and the ability to process large datasets efficiently. These systems are highly applicable in healthcare settings for the real-time tracking of patients and assets, improving emergency response and patient safety [56]. In industrial environments, UWB-based systems can track machinery, tools, and personnel with precision, optimizing workflows and safety. Additionally, in emergency services, UWB technology helps locate firefighters or rescue teams in GNSS-denied environments, enhancing mission success and safety. In conclusion, the combination of the BU01 UWB module with LSTM deep learning networks provides a robust, accurate, and efficient solution for indoor localization. This approach effectively addresses challenges like NLOS conditions, interference, and computational complexity, making it suitable for critical applications in healthcare, industry, and emergency response [57].