Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

Abstract

Passive sonar is widely used for target detection, identification and classification based on the target radiated acoustic signal. Under the influence of Doppler, generated by relative motion between the moving target and the sonar array, the received ship-radiated acoustic signals are non-stationary and time-varying, which has a negative effect on target detection and other fields. In order to reduce the influence of Doppler and improve the performance of target detection, a coherent integration method based on cross-power spectrum is proposed in this paper. It can be concluded that the frequency shift and phase change in the cross-power spectrum obtained by each pair of data segments can be corrected with the compensations of time scale (Doppler) factor and time delay. Moreover, the time scale factor and time delay can be estimated from the amplitude and phase of the original cross-power spectrum, respectively. Therefore, coherent integration can be implemented with the compensated cross-power spectra. Simulation and experimental data processing results show that the proposed method can provide sufficient processing gains and effectively extract the discrete spectra for the detection of moving targets.

Boat Detection in Marina Using Time-Delay Analysis and Deep Learning

“…Passive acoustic systems used to track an underwater sound are frequently based on several hydrophones connected to a central unit (Fillinger, 2010;Guo, 2020). In order to detect a boat without computing the spectrum, the time delay between the signals received by the hydrophones can be measured to estimate a boat's position.…”

“…Several approaches were developed to detect the harmonic frequencies in the signals and to extract the acoustic signature of the ships. In many cases, these methods are based on the spectrum (Guo et al, 2020), DEMON spectrum (Chung et al, 2011) and Cepstrum (Das, 2013;Santos-Domínguez, 2016) and the automatic detection is usually performed by detecting the peaks with a threshold (Reis et al, 2019).…”

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

“…The Delay-Doppler features can represent the difference of targets within a specific range. The coherence integration method of the power spectrum is used to compensate for the Delay-Doppler factor of passive sonar signals, which may significantly enhance the detection efficacy of mobile targets [10]. The underwater acoustic communication receiver has a high bit error rate (BER) since it was constructed using the signal characteristics found in the Delay-Doppler feature [11].…”

A Prediction Method of Ship Motion Based on LSTM Neural Network with Variable Step-Variable Sampling Frequency Characteristics

“…Aiming at the problem of the low accuracy of ship roll angle prediction by traditional prediction algorithms and the single neural network model, Wang Y proposed a ship roll angle prediction method based on a bidirectional long short-term memory network (Bi-LSTM) and a temporal pattern attention mechanism (TPA) combined deep learning model; temporal pattern attention mechanism extracts the time patterns from the deep features of a bidirectional long shortterm memory network output state that are beneficial to ship roll angle prediction and ignores other features that contribute less to the prediction [26]. For the prediction of ship heave motion, the prediction time step is particularly important, so calculating the delay in the ship motion acquisition system is also a top priority [27][28][29]. Hu T developed an adaptive time delay compensation algorithm based on thorough analysis of the integrated information transmission platform and the composition and characteristics of the time delay in channel, during which forward and reverse transmission channels have been developed properly [30].…”