Abstract
The localization of radio emitters is a fundamental task in reconnaissance systems, and it has become increasingly important with the evolution of mobile networks. The signal Doppler frequency (SDF) method, developed for dual-use applications, leverages Doppler frequency shifts (DFSs) in received signals to estimate the positions of radio transmitters. This paper proposes enhancements to the SDF method through advanced signal processing techniques, including dedicated filtering and a novel two-level overlapping approach, which significantly improve localization accuracy. The overlapping technique increases the number of DFS estimations per time unit by analyzing overlapping segments at both the signal sample level and within the DFS vector. Simulation studies using various filter types and overlapping parameters were conducted to evaluate the effectiveness of these enhancements in a dynamic scenario involving multiple stationary transmitters and a single moving receiver. The results demonstrate that the proposed approach minimizes localization errors. The application of low-pass filtering at the DFS vector level improves localization accuracy. In the study, three types of filters for different cutoff frequencies are considered. Each of the analyzed filters with an appropriately selected cutoff frequency provides a comparable reduction in localization error at the level of about 30%. The use of overlapping at the signal sample level with a factor of 10% allows for more than a twofold decrease in localization errors, while overlapping at the DFS vector provides an increase in the refresh rate of the position of localized objects. Comparative analysis with direct position determination techniques additionally showed high effectiveness of the SDF method, especially using data filtration and overlapping. The simulation studies carried out are of significant importance for the selection of the operating parameters of real localization sensors in unmanned aerial vehicle (UAV) equipment.