# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Strategic Insights: Indoor Localization in Military Applications
#### 3.3.4 BLE Indoor Positioning for UAVs in Military Operations  
[PERSON] et al. [64] proposed a system that utilizes a BLE beacon-based IPS specifically designed for small Unmanned Aerial Systems (UASs) or drones operating in environments where GNSS signals are unavailable. The system aims to deliver accurate, real-time localization using low-cost and energy-efficient components. Figure 25 demonstrates the optimization of positioning using BLE beacon triangulation. The architecture consists of BLE beacons, an onboard receiver, and a computer for data processing.  
Figure 24: Schema of the positioning system in a chemical workshop.  
Figure 25: Optimizing positioning with BLE beacon triangulation.  
The system uses BLE beacons (nRF51822) as fixed transmitters placed at known positions throughout the indoor environment. These beacons broadcast signals at regular intervals, sending advertisement packets that include a unique ID and the RSSI value. Figure 26 explores the architecture of the system framework, providing a detailed overview of its components. The onboard receiver is an Arduino Nano 33 BLE microcontroller, which is mounted on the drone to capture these BLE signals. The Arduino Nano 33 BLE is equipped with a 32-bit ARM Cortex-M4 processor and supports BLE communication, making it suitable for real-time positioning due to its low power consumption and efficient processing capabilities.  
The localization methodology relies on RSSI-based distance estimation and trilatteration to determine the drone's position. First, the onboard receiver collects the RSSI values from the surrounding BLE beacons. These \(RSSI\) values represent the strength of the signals received and are used to estimate distances between the drone and each beacon. The conversion from \(RSSI\) to distance is performed using a logarithmic path-loss model that accounts for signal attenuation. The formula used for this conversion is:  
\[d=10\frac{A-RSSI}{10n}, \tag{1}\]  
where \(d\) is the estimated distance, \(A\) is the reference \(RSSI\) value at 1 m, and \(n\) is the path-loss exponent reflecting the characteristics of the indoor environment.  
Once the distances are estimated, the system employs trilatteration to compute the drone's position. Trilateration uses distance measurements from at least three BLE beacons with known coordinates to determine the precise location of the drone. To further improve accuracy, the system applies a 1D Kalman filter to the RSSI values, which helps reduce noise, signal drift, and multipath interference. The Kalman filter smooths the data, resulting in more stable and reliable distance measurements. The filtered RSSI values are processed by the trilateration algorithm to estimate the drone's position in real time. These calculations were carried out on a connected computer using MATLAB (MathWorks, Natick, MA, USA) and RealTerm (RealTerm Open Source Terminal), which handled signal processing and position estimation.  
The data are transmitted between the Arduino and the computer via a USB connection, and the RealTerm software is used to log data for post-processing and analysis.  
The system was tested in an indoor environment with BLE beacons deployed at specific locations. The results demonstrated that applying the Kalman filter significantly improved localization accuracy. The positioning error was reduced from over 50 cm to less than 20 cm after filtering. This BLE-based IPS offers a low-cost, energy-efficient solution for real-time drone localization in GNSS-denied environments, making it suitable for applications in industrial, military, and emergency response settings. Future improvements may include integrating BLE with additional technologies like Wi-Fi and inertial measurement units (IMUs) to further enhance accuracy and robustness.  
Figure 26: Exploring the architecture of the system framework.