A Far-Field Helicopter Acoustic Detection Method Based on FRESH Adaptive Filtering

Helicopter detection plays a vital role in obtaining critical aerial information promptly and ensuring the safety of lives and property. Since a helicopter’s aerodynamic noise primarily consists of main rotor noise, the cyclostationarity of this noise becomes our detection target. This paper proposes a filter based on the Frequency-Shift (FRESH) principle, which is updated using the Adam optimization algorithm. A smoothed global detector is presented to detect the cyclic frequency of rotor noise. The effectiveness of the proposed helicopter detection approach, comprising both the filter and the detector, has been validated through simulations and confirmed by far-field experiments with a ROBINSON R22 helicopter. In these tests, the proposed method was compared against a cyclostationarity adaptive filter based on the Normalized Least Mean Squares (NLMS) algorithm, as well as the traditional Detection of Envelope Modulation on Noise (DEMON) and Cyclic Modulation Coherence (CMC) algorithms. Experimental results demonstrate the superior robustness of the proposed method over these benchmarks. Even at extended ranges between 11 and 13 km, the system retains a consistent detection rate of 77.8%.