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Adaptive Signal Analysis System for Non-periodic Signals

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0075
Agency Tracking Number: N192-086-0895
Amount: $140,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N192-086
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-10-08
Award End Date (Contract End Date): 2020-04-16
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Genshe Chen
 (240) 481-5397
Business Contact
 Yingli Wu
Phone: (949) 596-0057
Research Institution

This proposal aims to develop advanced signal analysis tools for utilization on non-periodic radio frequency signal sources that have the capability to detect, process, generate and classify non-periodic RF signals that do not exhibit sinusoidal characteristics. The adaptive signal analysis methods including empirical mode decomposition (EMD) and variational mode decomposition (VMD) are utilized for the construction of the adaptive features by the adaptive feature construction framework for the non-periodic signals. A deep learning based classification module will be built for the signal classification and characterizing tasks. The signal processing and generating tasks can also be performed by operating on the adaptive components derived from the above adaptive signal analysis methods. A preliminary evaluation is performed for the effectiveness of the adaptive components. The results of classification accuracy of 0.93 demonstrates the effectiveness of the adaptive components. With the further construction of the adaptive features, better performance and more complicated situations can be handled for the non-periodic signals. The ultimate goal of the proposed integrated adaptive signal analysis system is to handle ubiquitous kinds of non-periodic signals.

* Information listed above is at the time of submission. *

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