Energy Accumulation in 5G PRS-Based Weak Target Sensing

The widespread use of low-altitude devices, such as unmanned aerial vehicles (UAVs), presents significant challenges for effective monitoring and accurate localization in modern airspace management. To address this issue, we developed an integrated sensing and communication (ISAC) system based on 5G positioning reference signals (PRS). For maneuvering targets, we proposed a radar technique based on the multi-frame data accumulation. Using Two-Dimensional Fast Fourier Transform (2D-FFT), we clarified the resolution of range and velocity. To correct nonlinear range migration caused by the target motion, an improved Keystone transformation has been introduced, incorporating an innovative frame accumulation threshold method. This approach enhances the algorithm adaptability to various motion patterns. Additionally, we employ a nonlinear least squares (NLS) method for precise three-dimensional localization. Simulation results show that the method achieves meter-level positioning accuracy in different motion patterns. Notably, increasing the frame accumulation beyond the recommended threshold, particularly during the accelerated spiral motion, may reduce localization accuracy.