Abstract: In the covert reconnaissance communication of reverse unmanned aerial vehicles, the complex and variable electromagnetic environment often interweaves interference signals with normal communication signals, and the interference source may be located on a non-direct view path, making it difficult for traditional positioning methods to accurately identify and locate the specific location of the interference source. Therefore, this study proposes an electromagnetic interference source radar positioning algorithm. This algorithm utilizes the characteristic of unmanned aerial vehicles only receiving carrier signals, combines radar technology to construct a mathematical representation of the carrier signal, and considers the Doppler effect to adjust the signal frequency. At each observation moment, a signal vector containing key information such as signal amplitude and phase is extracted using segment sampling techniques. The introduction of grid partitioning method divides the drone communication environment into multiple grids, and assigns unique numbers to each grid based on signal vector characteristics to preliminarily determine the approximate range of interference sources. Starting from the randomly set initial interference source position, recursively solve the position deviation using Taylor series expansion and iteratively optimize the position estimation. During the iteration process, calculate the deviation coefficient between the actual received signal vector and the predicted signal vector generated from the current position estimation, and convert it into matrix form to accelerate the calculation. At the same time, use the least squares method to solve for the optimal position solution. After multiple iterations and updates, when the calculated position meets the preset accuracy threshold, the specific location of the electromagnetic interference source can be accurately locked. The experimental results demonstrate that the algorithm can achieve efficient localization in both clear environments with high signal-to-noise ratio and complex environments with low signal-to-noise ratio, significantly improving the accuracy and efficiency of interference source localization, and providing strong technical support for covert reconnaissance communication of reverse unmanned aerial vehicles.