Underwater Source Localization based on Modal Propagation and Acoustic Signal Processing

Acoustic localization plays a pivotal role in underwater vehicle systems and marine mammal detection. Previous efforts adopt synchronized arrays of sensors to extract some features like direction of arrival (DOA) or time of flight (TOF) from the received signal. However, installing and synchronizing several hydrophones over a large area is costly and challenging. To tackle this problem, we use a single-hydrophone localization system which relies on acoustic signal processing methods rather than multiple hydrophones. This system takes modal dispersion into consideration and estimates the distance between sound source and receiver (range) based on dispersion curves. It is shown that the larger the range is, the more separable the modes are. To make the modes more distinguishable, a non-linear signal processing technique, called warping, is utilized. Propagation model of low-frequency signals, such as dolphin sound, is well-studied in shallow water environment (depth D\u3c200 m), and it was demonstrated that at large ranges (range r\u3e1 km), modal dispersion is utterly visible at time frequency (TF) domain. We used Peker is model for the aforementioned situation to localize both synthetic and real underwater acoustic signals. The accuracy of the localization system is examined with various sounds, including impulsive signal, sounds with known Fourier transform, and signals with estimated source phase. Experimental results show that the warping technique can considerably lessen the localization error, especially when prior knowledge about the source signal and waveguide are available

Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images †

This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status—i.e., the existence and identity (or name)—of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods—particle filtering and Bayesian feature estimation—are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented