In recent years there has been a surging requirement for precise and effective indoor localization measurements due to the emergence of innovative technologies such as the Internet of Things (IoT) [1] and smart home devices. Traditional localization technologies like GPS have been proven not to be precise in indoor environments. A new approach based on WiFi RTT (Round Trip Time) called FTM protocol (Fine Time Measurement) has gained popularity over the last years due to its low-cost implementation and wide distribution especially indoors. Accuracy issues within FTM have been pointed out by studies. In an environment where the signal bounces into walls and buildings, the signal is received through different paths. As the sampling interval of the radios is below the delay between the different paths, the receiver often sees a mixture of the signals over different paths. Therefore, it is not possible to distinguish the direct path (if it exists) from the reflected signals. The receiver measures a mixture of different signals with different amplitudes, phases, and probably also different frequencies. Recent studies analyzed the increase in accuracy by choosing different burst sizes and multiple positions. As expected, increasing the number of bursts in an FTM frame leads to a reduction in the error. However, this comes at the cost of consuming more channel time. The increase in accuracy is not directly proportional to the amount of channel time required to perform it. This thesis' objective will be to develop an algorithm capable of dynamically adapting its parameters to ensure, simultaneously, channel time optimization and precision in real-case scenarios.