MATLAB Simulation of RSSI and TOA Localization Algorithms

In this article, we present MATLAB simulation code implementations for RSSI (Received Signal Strength Indicator) and TOA (Time of Arrival) localization algorithms. We begin by discussing the working principles of RSSI-based localization, which estimates distance by measuring signal strength attenuation using logarithmic path loss models. The MATLAB implementation typically involves calculating distance estimates from RSSI values using path loss exponent calibration and solving the positioning problem through least-squares or trilateration methods.

Next, we explore TOA localization algorithms that determine position by measuring signal propagation time between transmitters and receivers. The MATLAB simulation incorporates time synchronization mechanisms and employs techniques like circular lateration with nonlinear least-squares optimization to resolve position coordinates from multiple time measurements.

We provide comparative analysis of both algorithms' advantages and limitations: RSSI's hardware simplicity versus susceptibility to multipath fading, and TOA's higher accuracy requirements for precise time synchronization. Practical application scenarios include indoor positioning systems using RSSI for IoT devices and TOA-based solutions for UWB precision tracking.

The article concludes with implementation recommendations including signal calibration procedures, error mitigation techniques for non-line-of-sight conditions, and optimization approaches for improving localization accuracy in real-world deployments.