Dual-sensor coherence-driven adaptive denoising (WF-VMD-DDCDO) for underwater target detection

Underwater vehicle will generate a shaft-rate magnetic field at the propeller rotation frequency as the fundamental frequency during navigation, hence offering a approach for underwater target detection. Conventional shaft-rate magnetic field detection algorithms presume that the signal noise is Gaussian white noise. Moreover, the interference line spectrum frequently manifests, significantly elevating the false alarm rate in shaft-rate magnetic field detection. This paper presents a detection method, CSEM-WF-VMD-DDCDO, which integrates Coherent Signal Enhancement Method, whitening filter, Variational Mode Decomposition, and Differential Double-Coupled Duffing Oscillator. This method effectively detects magnetic fields at low signal-to-noise ratios (SNR) and in the presence of interference line spectra. The approach identifies target from colored noise samples with a very low SNR and interference line spectrum. The method considers the target signal as spatially coherent and employs data from two magnetometers to amplify the target signal while attenuating the interference line spectrum and colored noise in the background magnetic field. Finally, we employ an enhanced differential double-coupled Duffing oscillator to detect the shaft-rate magnetic field at SNR of - 54.39 dB. The findings indicate that the suggested method attains 95.83% accuracy on the assessed signals during a field test, demonstrating its efficacy in both simulated and empirical data.