Abstract:

The design and implementation of a real-time high-precision frequency estimation algorithm for Starlink downlink signals is a critical technology for the engineering application of LEO satellite dynamic opportunistic navigation. Traditional algorithms such as maxi-mum likelihood estimation， frequency-domain sliding window estimation， and Kalman filtering suffer from poor robustness and insufficient real-time performance in capturing low Signal-to-Noise Ratio （SNR） Starlink signals. To address these issues， this paper proposes a Multi-Carrier Joint Frequency Estimation （MC-JFE） algorithm， which enhances frequency estimation accuracy and real-time performance by deeply exploiting the multi-subcarrier structural characteristics of signals and jointly optimizing carrier frequency and frequency interval parameters. To overcome the intensive computational bottleneck in the engineering application of the MC-JFE algorithm， an innovative CPU+GPU heterogeneous parallel acceleration architecture is constructed， achieving over an order of magnitude improvement in execution efficiency through coordinated scheduling of CPU logic control and GPU large-scale parallel computing capabilities. To validate the theoretical and technical effectiveness of the proposed algorithm， real-time frequency estimation experiments were conducted on 5 978 Starlink satellite downlink beacon signals generated by a hardware-in-the-loop simulation platform， along with a comparative Doppler estimation studies using measured signals from China’s border regions. Results show that the MC-JFE algorithm maintains the lowest estimation error boundary across the full SNR range （-10 dB to 10 dB）， with over 50% improvement in estimation accuracy at 0 dB. Moreover， stable out-put is maintained during partial subcarrier interruptions through a phase information fusion mechanism. The CUDA-optimized CPU+GPU heterogeneous architecture achieves 0.1 Hz-level high-precision frequency estimation， with a peak speedup ratio of 47× （2.8× faster than traditional CPU solutions） and a positive correlation between accuracy and acceleration， providing highly reliable and real-time frequency estimation technical support for LEO satellite dynamic opportunistic navigation， demonstrating significant engineering application value.

Key words: Starlink downlink signal, high-precision frequency estimation, CPU+GPU heterogeneous computing, parallel acceleration, multithread processing