A Residual Neural Network Approach to Transmitter Localization

This article presents a method for locating a wireless signal source using signal strength measurements taken along the border of a 256\times 256 -m² area. This method leverages a deep residual neural network (ResNet) to predict the location of the transmitter within the area of interest. This approach reduces data collection and computational overhead associated with traditional localization methods. The method is validated through simulated data as well as measurements at 2.7 GHz, with an average error of 7.23 m and a standard deviation of 3.32 m. This work addresses the need for scalable and low-cost transmitter localization methods, particularly in environments where conventional approaches are hindered by obstacles or require extensive data collection.