Helicopter Seismic Signal Detection Algorithm Based on Cyclic Stationarity Analysis

Low-altitude helicopters play a pivotal role in aviation safety, military surveillance, and disaster rescue. However, traditional detection techniques, such as radar and visual recognition, exhibit limitations in low-altitude blind spots or complex environments. This paper proposes a novel helicopter seismic signal detection algorithm based on cyclic stationarity analysis. The algorithm exploits the periodic seismic wave signals generated by the rotating rotor blades of helicopters, leveraging their cyclic stationary characteristics for target identification. Specifically, the periodic motion of the rotor generates airflow disturbances that propagate through the ground at a specific frequency, forming seismic wave signals with distinct periodic statistical features. Through autocorrelation analysis and cyclic frequency extraction, the proposed algorithm effectively differentiates helicopter signals from environmental noise. Extensive experimental results demonstrate that the proposed algorithm outperforms the traditional STA/LTA method in terms of detection accuracy and robustness to interference. The algorithm achieves near-zero false alarms when exposed to common interference sources, such as pedestrians and vehicles. These findings establish the proposed method as an innovative and reliable solution for low-altitude helicopter detection in challenging environments.