Implementation and Evaluation of a Mobile Indoor Positioning System Using the Frequency Switching Model

The increasing demand for indoor IoT services has heightened the need for precise indoor localization techniques. This paper presents the Mobile Access Point-based Model (MAPM), which uses the Received Signal Strength Indicator (RSSI) to accurately estimate positions and address the challenges posed by non-line-of-sight (NLOS) conditions in indoor environments, where signal reflections and multipath effects hinder localization accuracy. Key contributions include an innovative strategy that integrates telecommunication processes with data processing for efficient system communication, a hardware implementation using the Universal Software Radio Peripheral (USRP) platform and MATLAB Simulink for software development, and a novel evaluation model with a frequency switch algorithm that enables communication between a single user-side USRP and two access points, resulting in highly accurate RSSI measurements with errors of less than 10 cm in most NLOS scenarios. The MAPM’s performance in mitigating NLOS effects is demonstrated through both simulations and real-world experiments, validating its effectiveness in improving indoor positioning accuracy.