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Publication Abstracts

Submerged Target Detection and Localization at Ports of Entry Using Indirect Passive Sonar

M. Montanari, and J.R. Edwards
Undersea Defense Technology UDT Europe 2006
Hamburg, Germany, 27-29 June 2006

Maritime security has recently taken more prominence in international port of call, particularly due to increased willingness and capability of terrorists to interrupt commercial interests. Busy ports are flanked by busy shipping lanes, providing cover for both submerged vessels and small surface craft to enter undetected. A well-instrumented port of entry designed to remove the possibility for any sizeable vessel to enter or leave undetected. Remote sensing methods may be readily employed to monitor all large transports entering or exiting the harbor by land, sea, or air, but small submersibles and surface craft remain a problem. The most challenging means of entry to detect are undersea vehicles, including undersea drivers, mini-subs and unmanned underwater vehicles (UUVs).

Passive detection of such targets is challenging due to the very low emitted signal levels in comparison with the many very loud surface ships transiting the same area, while active detection has strategic and environmental drawbacks. Considering the strong existing sound field in and near a busy shipping lane, it would be preferable to exploit these existing signals as a surrogate for a separate source, in a method we refer to as indirect passive sonar (InPaS).

In this work we propose and investigate the possibility of fusing the radar remote sensing modalities with the underwater sensors in order to aid in passive detection of underwater intruders. In particular, the capability of pre-deploying fixed sonar arrays such that a specified maritime domain is protected within a given level of certainty is developed with respect to a segment near the Singapore straits. Theoretical limits are derived, and high fidelity acoustic simulations are employed to capture the bathymetric and propagation characteristics of the target area. A big challenge to the InPaS method is the fact that the surface ship direct signal is stronger than and well correlated to the reflected signal, and has nearly the same angle of arrival at the array. The novelty of this work is the fusion of the results obtained by separately exploiting the uncorrelated part of the signal as well as the correlated part of it, as well as the inclusion of independent radar sensors to enhance the sonar performance. To this purpose, high resolution spectral estimation techniques are employed in conjunction with forward-backward processing, as well as estimation techniques that correlate the signal received steering in the direction of the source with the signal impinging on the array from a nearby direction.

Submerged Object Detection Using Passive Sonar
J.R. Edwards, M. Montanari, J.A. Nave, and K.V. Rao
International Maritime Protection Symposium 2005
Honolulu, HI, December 2005

This paper presents an analysis of the possibility of deploying fixed passive arrays in the Keppel Harbor to protect the port of entry from submerged intruders. The many surface ships traversing the shipping channel are exploited as sources of opportunity to illuminate the intruding vessels, allowing detection through an indirect passive sonar (InPaS) technique. The positions of surface ships are known to the arrays through existing radar systems.

Incorporating Environmental Sensor Measurements to Improve Sonar Performance via Neural Networks
J.R. Edwards and J.A. Nave.
Sixth International Conference on Theoretical & Computational Acoustics.
Honolulu, HI, August 2003

Adaptive filtering is widely used in the sonar community to enhance sonar performance in the face of uncertain environmental properties. In effect, the filter adaptation serves to estimate the Green's function of the medium, thereby improving the target detection capability of the sonar. A more direct way to adapt to the environment would be to simply measure the environmental parameters during the sonar operation and use this information to adjust the steering vectors. Such an approach would be ideal for a sonar that remains in a temporally varying environment or for a sonar that moves through a range-dependent (or 2-D varying) environment. The drawbacks to this approach stem from the fact that a perturbation in the environmental parameters does not necessarily lead to a perturbation in the steering vectors. Therefore, a new environmental parameter measurement requires that the steering vectors be recomputed, which in most cases is a time- and computational resource-consuming process. In addition to the computational cost, the inherent uncertainty of the environmental sensor measurement is not captured by the direct computation of steering vectors. Both of these problems are alleviated with the application of Artificial Neural Networks (ANN) in the steering vector computation. In this work, the ANN approach is illustrated using a small sonar moving through a range-dependent environment, i.e. an autonomous underwater vehicle (AUV). High fidelity acoustic propagation models are applied in a comprehensive acoustic simulation package for AUV mission simulation and planning. The data generation is achieved with deterministic parameter fluctuations, while the AUV-borne environmental sensors are simulated as measuring the environmental properties with a known error distribution. The sonar performance for probability of target detection is compared between the fixed steering vector system, a system that incorporates the environmental measurements directly, and the neural network method that takes into account the sensor error distributions.

Fast Field Prediction via Neural Networks
J.A. Nave and J.R. Edwards
Sixth International Conference on Theoretical & Computational Acoustics
Honolulu, HI, August 2003

Despite decades of algorithm refinement and efficiency studies, the computational requirements of high fidelity acoustic field prediction models remain beyond the real-time or near real-time capabilities of modern computers. In many applications, however, there is ample time for pre-processing, so wise use of this time can reduce the on-line load of the field prediction. Artificial Neural Networks (ANNs), specifically Multilayer Perceptions (MLPs), are capable of approximating these acoustic models to any desired level of accuracy on a bounded domain. Most importantly, this highly accurate approximation of the field prediction can be produced almost instantaneously, after proper training of the MLP in the pre-processing phase. The speed of the MLP computation is easily sufficient to be included in real-time sonar systems, as well as at-sea trials. Futhermore, a MLP is not only capable of deterministic model approximation, but it can also be trained to produce a minimum-variance estimate in the presence of statistically predictable noise in model parameters such as sound speed. This inclusion of known statistics allows the MLP, in some cases, to be more accurate than deterministic modeling. In this paper, the MLP approach is demonstrated to be highly effective in several common ocean acoustic tasks, including fast field prediction and matched field inversion. Supporting numerical examples are included for both deterministic and stochastic models.

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