Optical Microfiber All−Optical Phase Modulator for Fiber Optic Hydrophone

In order to meet the needs of phase generated carrier (PGC) demodulation technology for interferometric fiber optic hydrophones, we proposed an optical microfiber all−optical phase modulator (OMAOPM) based on the photo−induced thermal phase shift effect, which can be used as a phase carrier generation component, so as to make the modulation efficiency and working bandwidth of this type of modulator satisfy the requirements of underwater acoustic signal demodulation applications. We analyzed the modulation principle of this modulator and optimized the structural parameters of the optical microfiber (OM) when the waist length and waist diameter of OM are 15 mm and 1.4 μm, respectively. The modulation amplitude of the modulator can reach 1 rad, which can meet the requirements of sensing applications. On this basis, the optical fiber hydrophone PGC−Atan demodulation system was constructed, and the simulated underwater acoustic signal test demodulation research was carried out. Experimental results showed that the system can demodulate underwater acoustic signals below 1 kHz.

Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500~700 m less than the KFAT method.

Underwater acoustic target classification based on u-shaped network

Target recognition is a key task and a difficult technique in underwater acoustic signal processing. One of the most challenging problem is that the label information of the underwater acoustic samples is scarce or missing. To solve the problem, this paper presents a local skip connection u-shaped architecture network(U-Net)based on the convolutional neural network(CNN).To this end, the network architecture is designed cleverly to generate a contracting path and an expansive path to achieve the extraction of different scale features. More importantly, a local skip connection mechanism is proposed to optimize classification rates by reusing former feature maps in contracting path. The experimental results of the measured dataset demonstrate the recognition accuracy of the model is better than that of deep belief network(DBN) and generative adversarial network(GAN) networks.Further research on three kinds of network by visualization method shows that the proposed network can learn more effective feature information with limited samples.

Single Ping Filtering of Multi-Beam Sounding Data Based on Alpha Shapes

From the standpoint of complex marine environments relative to underwater acoustic signal propagation, the application of multi-beam systems for the filtering and processing of multi-beam sounding data is critical. However, currently existing automatic filtering methods estimate data, which involve several calculations and require the respective computer to possess significant processing capabilities. In this study, raw data are measured using an interferometer multi-beam echo sounder, and the characteristics of the noise data are analyzed. The noise data are discontinuous; however, accurate data can be approximated as a continuous distribution. Under the assumption of continuous underwater terrain, in consideration of a circle of the appropriate radius rolling on the terrain profile, the continuous underwater terrain data can be extracted from the raw data by means of the alpha-shapes algorithm. Finally, on the basis of the measured data in a sail trial, the effectiveness of the proposed algorithm is verified.