Abstract: In this paper, a new index is proposed for detecting the frequency of unknown underwater signals based on the stochastic resonance theory. When the received weak signal is input into the stochastic resonance system, first, by frequency analysis, the frequency with the highest amplitude Aₘ of the output signal spectrum is considered as the pre-detection fre-quency. Then a cosine signal with the pre-detection frequency and unit amplitude is constructed. Define the pre-signal-to-noise-ratio as the logarithm of the squared amplitude Aₘ over the mean of signal amplitudes in all other frequencies. The new index is defined as the product of the pre-signal-to-noise-ratio and the correlation coefficient between the received unknown signal and the constructed cosine signal. The new index is featured by taking into account the signal characteristics in both time and frequency domain, and it will yield better signal frequency detection performance. In addition, to improve the time efficiency of the frequency detection, a method to bound the searching range, keyed to the genetic algorithm, of the stochastic resonance system parameters is proposed. The method can be used to detect the frequency of both single frequency and frequency-hopping unknown signals. With the designed new index and system parameter bounding method, the simulations and experiments for the weak underwater unknown signals are conducted. Compared to the piecewise mean value index and weighted power spectral kurtosis index, the new index yields a higher detection probability at varied input signal-to-noise ratios and signal frequencies. With bounding system parameter searching ranges, the time efficiency is improved. The main purpose of this paper is to detect the frequency of unknown underwater weak signals by stochastic resonance system with genetic algorithm. The main contributions are summarized as follows. First, the detection probability of weak signals is improved by stochastic resonance system with the proposed signal detection index than some other indexes. Second, to improve the time efficiency of the signal frequency detection, a method to bound the searching range of system parameters is proposed.

Weixiang Yu and Xiukui Li, “A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(2), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130290

@article{Yu2022,
title = {A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130290},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130290},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {2},
author = {Weixiang Yu and Xiukui Li}
}