Underwater Small Target Detection Method Based on the Short-Time Fourier Transform and the Improved Permutation Entropy

In the realm of underwater active target detection, the presence of reverberation is an important factor that significantly impacts the efficacy of detection. This article introduces the improved permutation entropy algorithm into the analysis of active underwater acoustic signals. Based on the significant difference between the improved permutation entropy in the frequency domain and the time domain, a frequency-domain-improved permutation entropy detection algorithm is proposed. The performance of this algorithm and the energy detection algorithm are compared and analyzed under the same conditions. The results show that the spectral entropy detector is about 2.7 dB better than the energy detector, realized via active small target signal detection under a reverberation background. At the same time, based on the characteristics of improved permutation entropy changing with the length of processed data, the short-time Fourier transform is integrated into frequency domain entropy detection to obtain distance and velocity information of the target. To validate the proposed methods, comparative analysis experiments were executed utilizing actual experiment data. The outcomes of both simulation and actual experiment data processing demonstrated that the sliding entropy feature detection method for signal spectrum has a small computational complexity and can quickly determine whether there is a target echo in the receive data. The two-dimensional entropy feature detection method for short-time signal spectra was found to effectively mitigate the impact of reverberation intensity and while enhancing the prominence of the target signal, thereby yielding a more robust detection outcome.