Fuse refractivity inferred from electromagnetic (EM) propagation observations with background fields from numerical weather prediction (NWP) models. OBJECTIVES Develop data fusion method for atmospheric refractivity scheme based on objective analysis. Develop means to map observations of refractivity based on RF propagation measurements into the space utilized for the analysis. Exercise the data fusion scheme on a combination of synthetic and real data to assess performance. Acheive reasonable processing time (on the order of 1-minute) with a representative domain size using a high-end laptop computer. APPROACH An initial approach to estimation of atmospheric surface layer parameters by fusing radar clutter data with ensemble predictions from NWP is described in [1]. That work was completed in the beginning of the 2013 Fiscal Year. We now describe a fusing EM observations with NWP background for the region above the surface layer (that includes surface based ducts and elevated ducts). In fusing EM observations with background from NWP, some considerations include: 1. The mapping from the space of EM signal enhancement (typically dBs) into the space of modified refractivity is non-linear, sometimes highly so.
The Navy is actively developing diverse optical application areas, including high-energy laser weapons and freespace optical communications, which depend on an accurate and timely knowledge of the state of the atmospheric channel. The Optical Channel Characterization in Maritime Atmospheres (OCCIMA) project is a comprehensive program to coalesce and extend the current capability to characterize the maritime atmosphere for all optical and infrared wavelengths. The program goal is the development of a unified and validated analysis toolbox. The foundational design for this program coordinates the development of sensors, measurement protocols, analytical models, and basic physics necessary to fulfill this goal.
This report describes experiments designed to evaluate the usefulness of a specific algorithm for classifying images of commercial ships by class. This algorithm uses a technique known as sparse coding to represent images for classification. The sparse coding algorithm is compared with another algorithm evaluated in previous publications. RESULTS The sparse coding algorithm is shown to perform approximately as well as the algorithm it is compared with and does not appear to offer any improvement. RECOMMENDATIONS Additional research is required to identify algorithms best suited for the ship classification task. iii v 16. SECURITY CLASSIFICATION OF: a. REPORT b. ABSTRACT c. THIS PAGE 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON 19B. TELEPHONE NUMBER (Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18
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