Application of Neural Networks to Acoustic Localization

The intent of the work conducted was to build a neural network for the purposes of acoustic localization. The target of this localization is a sound source underwater. For our purposes, it is an acoustic pinger, as it produces consistent sound at a fixed rate making it ideal for testing. The network was intended to ingest raw data streams and output location information based on the arrangement of sensors employed. To achieve an accurate network, a simulation factoring in the environment was to be created to produce a data set large and diverse enough to describe the unique parameters of the signals, including: frequency, environmental reflections, and range. This problem will be approached in multiple steps. Initial models will consider simplified problem spaces, such as individual frequencies and less descriptive training sets. Through development, this will be refined and extended. Where required, simplifications will be kept managing the scope of the problem to allow for a demonstration of the technology to be made at all. Discussion of what is the root cause of the issue navigated will be presented when this occurs. Results will then be shown to demonstrate the performance of the network created as compared to the classical approach to this problem, time difference of arrival. This paper will demonstrate the performance of a neural network as applied to the problem of acoustic localization. The network developed can accurately localize an acoustic sound source to the same order of magnitude of accuracy and execution time as the current approaches to the problem. However, the network also showed a lacking in some areas of robustness due to training factors not considered, hampering the full potential

Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network