美国声学学会Fellow、IEEE Senior Member
Daniel Rouseffreceived the Ph.D. degree in electrical engineering from the University of Washington, Seattle, WA USA, in 1989. Upon graduation, he joined the Senior Professional Staff at the Milton S. Eisenhower Research Center, Applied Physics Laboratory, Johns Hopkins University, Laurel, MD. In 1992, he returned to the University of Washington, where he is currently an Affiliate Scientist at that university’s Applied Physics Laboratory. Since 2006, he has also been an Adjunct Research Associate Professor at Portland State University, Portland, OR, in the Department of Electrical and Computer Engineering. He has held visiting positions at the Naval Research Laboratory, Washington DC, and the Department of Applied Mathematics and Theoretical Physics, Cambridge University, Cambridge, U.K.
Dr. Rouseff was Chief Scientist on the 2009 Cooperative Array Performance Experiment (CAPEx09), a joint China-USA underwater acoustics experiment. His primary technical interests are underwater communications, acoustical oceanography, and physics-based signal processing. Dr. Rouseff is a Fellow of the Acoustical Society of America and a Senior Member of IEEE.
报告题目1：Physical-Layer Modeling for Underwater Sensor Networks
Reliable underwater communications could be a key enabler for several commercial applications. The vision is to have the functional equivalent of a terrestrial wireless communications network, but operating underwater with acoustic rather than electromagnetic waves providing most of the links. Advances in unmanned underwater vehicle (UUV) and unmanned surface vehicle (USV) technology offer the possibility of mobile, ad hoc networks operating on a large scale.
Ideally, nodes in the network would be able to adjust operational parameters to optimize communications performance. For example, data from onboard environmental sensors might be incorporated into physical-layer models run on a UUV to determine the operational depth that would give the best communications performance. Physical-layer models might also inform choices for modem parameters like the data rate, data block size, and the interval between pilot sequences.
In the present talk, it is argued that physical-layer models need not always be complicated. Relatively simple models can sometimes capture the essential physics and distill the results into a form useful for adjusting the parameters of an equalizer. Particular emphasis is placed on modeling acoustic paths that interact with the dynamic sea surface. Predictions for communications performance as a function of adjustable equalizer parameters are shown to match favorably with experimental results for fixed-source, fixed-receiver scenarios.
报告题目2：Ocean Internal Waves and How They Affect Acoustic Array Processing
Internal waves arise in the ocean because the water density is not constant with depth. Often there is a near-constant density mixed layer between the sea surface and the denser water below. External forces like tides and currents cause internal waves to propagate along the interfaces between waters of different density. Internal waves can propagate long distances and “break” similar to the more familiar waves on the sea surface. Breaking internal waves create turbulence that affects ocean circulation.
Besides being interesting in the own right, internal waves are important for how they affect acoustic propagation. The internal waves perturb the speed of sound, causing acoustic focusing and defocusing. As the internal waves propagate, the temporal and spatial coherence of acoustic signals will change accordingly.
In the present talk, the affects of internal waves on acoustic array processing are surveyed. Both non-linear and nearly linear internal waves are considered. In developing acoustic models, it is shown how a deterministic approach is appropriate for non-linear waves while a stochastic approach is used for the background linear waves. Two specific acoustic array-processing problems are studied: the spatial coherence of a horizontal array, and the directivity of ambient noise as observed on a vertical array.