# 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
#### 3.3.2 ZigBee-Based System for Military Applications  
[PERSON] et al. [62] proposed an indoor positioning system (IPS) that utilizes ZigBee technology combined with an ANN, specifically a Radial Basis Function Network (RBFN), to achieve accurate, low-cost, and energy-efficient localization. This system is designed for deployment in various environments, including healthcare facilities, industrial settings, and military operations, where reliable indoor positioning is crucial.  
The system architecture consists of several key components. ZigBee sensor nodes are strategically placed within the environment to serve as reference points. Figure 22 has the detailed information of the system function. These nodes measure the RSS or Link Quality Indicator (LQI) of signals transmitted by a target node, which can be a device carried by a person or attached to an object. The target node continuously broadcasts signals, which are received by the sensor nodes for localization purposes. ZigBee technology is chosen for its low power consumption, low cost, and mid-range communication capabilities, making it suitable for scalable and flexible deployments in environments with power constraints. The localization process is divided into two main phases: the offline phase and the real-time phase. In the offline phase, the system maps the environment by collecting RSS or LQI measurements at predefined sampling points. This dataset is used to train the Radial Basis Function Network (RBFN), allowing the network to learn the relationship between signal strength and the corresponding location. The RBFN adjusts its parameters, such as cluster centers and standard deviations, using a competitive learning algorithm to improve accuracy.  
In the real-time phase, the trained RBFN processes new RSS or LQI measurements from the ZigBee nodes to estimate the position of the target node. To enhance accuracy, the system applies signal preprocessing techniques, averaging the collected signal values to reduce the effects of environmental noise and temporary obstructions. This ensures that the data fed into the RBFN are stable and reliable. The system achieves an average localization error of 1.47 m under ideal conditions and 2.8 m in typical environments. The system is implemented using the FT-6250/FT-6251 ZigBee development kit, which features a 32-bit RISC processor and operates on the 2.4 GHz IEEE 802.15.4 communication protocol [62]. The software for the system is developed in C/C++, and data communication between nodes follows a polling approach to prevent signal congestion and ensure smooth data transmission.  
The network topologies supported by the system include the star, tree, and mesh topologies, allowing for flexible deployment based on the specific requirements of the environment. In the star topology, a central coordinator communicates with multiple end devices. In the tree topology, nodes are organized hierarchically, while in the mesh topology, nodes are interconnected, providing multiple communication paths and increasing the robustness of the system.  
This combination of ZigBee wireless technology and the RBFN algorithm offers a reliable, cost-effective, and scalable solution for indoor positioning. The system is well-suited to environments where the accurate and continuous tracking of assets, personnel, or equipment is essential, such as in healthcare facilities, industrial settings, and military operations.