MATLAB Simulation for RSSI-Based Localization: Code Implementation and Analysis

This article presents a comprehensive MATLAB simulation framework for RSSI-based localization systems, with detailed implementation of RSSI-to-distance conversion algorithms. The core methodology employs log-distance path loss models to transform received signal strength indicators into estimated distances using the formula: distance = 10^((measured_power - RSSI)/(10 * path_loss_exponent)). The simulation architecture includes three key modules: signal propagation modeling using friis transmission equation, distance estimation through maximum likelihood optimization, and position calculation via trilateration algorithms with least squares refinement. We demonstrate practical MATLAB implementations including data preprocessing functions for outlier removal, calibration routines for environment-specific parameters, and visualization tools for error analysis. The code structure features modular design with separate functions for signal generation, anchor node configuration, and localization engines, enabling easy adaptation to different deployment scenarios. Through this technical exploration, we aim to provide researchers with a complete understanding of RSSI localization principles while offering reusable code components for rapid prototyping. The discussion covers practical considerations such as multipath mitigation techniques, calibration methodologies for different environments, and performance evaluation metrics including mean positioning error and cumulative distribution functions.