Multistatic acoustic characterization of seabed targets

Abstract

One application for autonomous underwater vehicles (AUVs) is detecting and classifying hazardous objects on the seabed. An acoustic approach to this problem has been studied in which an acoustic source insonifies seabed target while receiving AUVs with passive sensing payloads discriminate targets based on features of the three dimensional scattered fields. The OASES-SCATT simulator was used to study how scattering data collected by mobile receivers around targets insonified by mobile sources might be used for sphere and cylinder target characterization in terms of shape, composition, and size. The impact of target geometry on these multistatic scattering fields is explored, and a discrimination approach developed in which the source and receiver circle the target with the same radial speed. The frequency components of the multistatic scattering data at different bistatic angles are used to form models for target characteristics. Data are then classified using these models. Classification accuracies were greater than 98% for shape and composition. Regression for target volume showed potential, with 90% chance of errors less than 15%. The significance of this approach is to make classification using low-cost vehicles plausible from scattering amplitudes and the relative angles between the target, source, and receiver vehicles.

Fault-Tolerant Prescribed Performance Control Algorithm for Underwater Acoustic Sensor Network Nodes With Thruster Saturation

“…5is a performance function where the setting of corresponding parameters is given in Eq.(6). The step 2 demonstrates that the selection of parameters t f and k affects the convergence speed of the performance function (5), and they can be chosen without constraints. The new performance function (5) has the following important properties:…”

“…2. When the steady state convergence time is given, we can adjust parameter k to change the convergent speed of the performance function (5).…”

“…Remark 1: On account of traditional performance function (3) which adopts exponential convergence form, the convergent speed depends on the parameter k. The traditional performance function is difficult to satisfy requirements when we hope to reduce the initial convergent speed to avoid overlarge control demand. The proposed new performance function (5) can choose appropriate parameter t f to ensure the system convergence within desired time, and adjust parameter k to control initial convergent speed of the error system.…”

“…According to the definition of the prescribed performance method and the proof of theorem 1, we propose the parameter selection guidelines about performance function (5), observer (24), and control strategy (25).…”

“…The ocean contains rich undiscovered natural resources, which include marine lives, mineral resources, and energy. In order to acquire the resources, people must master the key technologies, which contain the deep-sea navigation, exploration, and exploitation [1]- [5]. Using underwater acoustic sensor networks (UASNs) can obtain the information about changes in the marine environment more effectively, which is significantly important to marine resource exploration and scientific research [6].…”