Underwater Low-Frequency Magnetic Field Detection Based on Rao’s Sliding Threshold Method

This paper proposes a joint time-frequency analysis method that combines <i>Rao</i> detector with dynamic sliding thresholds to enhance the detection performance of electric source axial frequency magnetic field signals. For each signal-to-noise ratio (SNR) point, 1000 Monte Carlo simulations were independently conducted, with SNR ranging from 15 dB to -30 dB. The results show that the proposed method maintains high detection rates even at extremely low SNRs, achieving about 90% detection probability at -13 dB, significantly outperforming traditional energy detectors (with a threshold of 2 dB). Under conditions where the detection probability is ≥90% and the false alarm probability is 10^-3, the SNR threshold for the <i>Rao</i> detector is reduced by 15 dB compared to energy detectors, greatly improving detection performance. Even at lower SNRs (-30 dB), the <i>Rao</i> detector still maintains a certain detection rate, while the detection rate of energy detectors rapidly drops to zero. Further analysis of the impact of different frequencies (1-5 Hz) and CPA distances (45-80 cm) on performance verifies the algorithm's robustness and practicality in complex non-Gaussian noise environments. This method provides an effective technical solution for low SNR detection of ship axial frequency magnetic fields and has good potential for practical application.