Acoustic Ranging Issues Based on Time-of-Arrival for Underwater Localization

Abstract

Localization techniques for underwater acoustic sensor networks (UASNs) have been widely studied to support diverse location-based applications, such as oceanographic monitoring systems, offshore exploration, tactical surveillance, and disaster prevention. In these areas, identifying the locations of sensor nodes plays an important role in the spatio-temporal tagging of the collected oceanographic data.

For underwater localization, the methods to estimate the location of sensor nodes typically can be divided into range-based and range-free. The range-free method can localize sensor nodes by estimating distance without expensive devices (\ie only signal reception decision) for measuring distance. Meanwhile, range-based methods such as Received Signal Strength Indicator, Angle of Arrival, Time-Difference-of-Arrival, and Time-of-Arrival (ToA) typically measure the distance between nodes by receiving signal information. Although the range-free method do not require any additional hardware for measurement (\ie low complexity and low cost), the range-based methods are generally used due to its high accuracy. Among them, "ToA" method is a superior approach to localize sensor nodes with respect to accuracy than other range-based methods. Although the advantage of ToA method, achieving high accuracy is a challenging problem because of the harsh environmental characteristics of the acoustic underwater medium.

Our focuses mainly are ToA-based acoustic ranging for underwater localization and its actual performance in real underwater environments. Further we consider impacts of beacon deployment types (static and mobile) on localization accuracy. Even though there are many considerable issues on underwater ranging, we investigated the significant improvement of the ranging accuracy and complexity, and localization accuracy.

As the first contribution, we propose a novel ToA-based ranging algorithm called real-time acoustic ranging (RAR). The RAR algorithm harnesses ray pattern locality to provide accurate distance estimation, and evaluate performance with respect to accuracy and computational overhead matrices. We also present the results of simulations and experiments conducted near Jeju Island in S. Korea. The results verified that RAR attained an accuracy very close to that of RRFR and was at least six times faster.

As the second contribution, we propose a novel ToA-based acoustic ranging with a ray selection called (TAR) for shallow-water localization. To avoid the misperception problem, the proposed TAR algorithm selects an appropriate ray path under ray pattern similarity assumption, instead of selecting the shortest path, and searches for one possible point based on the given arrival time. To do that, the measured sound speed profile (SSP) and bathymetry are necessary as inputs. Simulations and experiments (\ie VITYAZ11) show that the proposed TAR scheme has better ranging accuracy than the existing ranging algorithms in a shallow water environment.

As the third contribution, we propose underwater localization schemes to confirm the localization performance and to consider the actual problems in the process. we first propose a static beacon-based localization scheme using the proposed TAR algorithm, namely ray tracing-assisted localization (RTL). Herein, we compare the proposed scheme with ray bending-based localization (RBL). Further, we propose a mobile beacon-assisted localization via motion compensation called LMB-MC. The LMB-MC scheme compensates the projected beacon points and generalizes the conventional circle-based model into an ellipse-based model on geometric constraints. To evaluate LMB-MC, we analyze real-world measured ship motion data and perform modeling according to oceanic conditions. Here, we deliver the verified results in harsh oceanic conditions.

In this dissertation, for establishment of marine environmental monitoring systems, it is meaningful to propose practical ranging algorithms and localization schemes by measuring and using marine environmental data, underwater acoustic data, and ship motion data.