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经邀请，Don DelBalzo 将前来我所作两场学术报告，现将学术报告相关信息通知如下：
1.报告题目：Passive Acoustic Inversion Technique (PGAIT)
2.报告时间：2018年6月5, 9:30-11:30 AM
4.报告摘要：Naval operations are often conducted in littoral, shallow-water areas, where seabed geophysical properties are complicated and unknown and where the water conditions change regularly. Existing data collection survey techniques for geo-acoustic bottom characteristics are expensive, time consuming, and they suffer from time latency between collection, processing, analysis, and tactical use. The research reported here describes an algorithm that applies coherent and incoherent matched-field processing to “unknown” signals from ships of opportunity. It is robust to a nominal amount of environmental model mismatch away from the receiver. It uses broadband and temporal averaging to reduce ambiguities and to produce an output with at least 10 dB SNR, which is sufficient to identify sediment types. New work reported here extends the previous simulation analysis to a horizontal array on the seabed, which is much more challenging than a vertical array, and considers several sediment conditions, ranging from very soft to very hard amidst an unknown water sound-speed profile. We show a capability to identify range-independent sediment types and water sound-speeds with the enhanced algorithms.
1.报告题目：Efficient Acoustic Gridder for Littoral Environments (EAGLE)
2.报告时间：2018年6月6, 9:30-11:00 AM
4.报告摘要：Many Navy operations, like searching for undersea targets, require extensive acoustic calculations. The standard computational approach is to predict sonar performance on a uniform grid of points and radial directions. Unfortunately, the time required to achieve the necessary accuracy often renders them tactically useless. The Efficient Acoustic Gridder for Littoral Environments (EAGLE) was developed to produce sparse, irregular grids that are "matched" to acoustic complexity. EAGLE produces an acoustic field with ever-increasing resolution and accuracy. The present work presents a new concept for point-selection based on local predictive errors defined as the difference between a known value at a location and an estimate of its value based on all other points gathered to date, so it resembles the statistical process known as “jack-knifing.” Tests show that a grid of the predictive error is at least as smooth as, and usually much smoother than, the underlying dataset, so it can be and probed for extreme values. Consistent removal of extremal uncertainty rapidly forces the irregular grid toward a narrow band of low uncertainties, a property referred to as “iso-deviance.”
5.报告人简介： Don DelBalzo worked at the US Naval Research Lab for 32 yr and QinetiQ for 10 yr. He is the founder and owner of DelBalzo Tech Solutions. He managed and conducted projects to assess environmental impacts on acoustic systems in littoral regions. He served as ASW advisor to Commander, US 7th Fleet (1985-86), US scientific representative to NATO’s ASW Search & Screening Group (1995-2000), Senior scientific advisor to Commander, U.S. 6th Fleet (1988-1990), Awarded “Science Advisor of the Year” 1989, Awarded US Meritorious Civilian Service Award 1990, Best technical paper of the year Military Operations Research Journal 2003.