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Title: Systems, methods and computer program products for self-tuning sensor data processing

Abstract

Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.

Inventors:
; ; ;
Issue Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1771638
Patent Number(s):
10837811
Application Number:
15/828,188
Assignee:
National Technology & Engineering Solutions of Sandia, LLC (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G01 - MEASURING G01D - MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE
DOE Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Patent
Resource Relation:
Patent File Date: 11/30/2017
Country of Publication:
United States
Language:
English

Citation Formats

Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., and Peterson, Matthew Gregor. Systems, methods and computer program products for self-tuning sensor data processing. United States: N. p., 2020. Web.
Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., & Peterson, Matthew Gregor. Systems, methods and computer program products for self-tuning sensor data processing. United States.
Draelos, Timothy J., Faust, Aleksandra, Knox, Hunter A., and Peterson, Matthew Gregor. Tue . "Systems, methods and computer program products for self-tuning sensor data processing". United States. https://www.osti.gov/servlets/purl/1771638.
@article{osti_1771638,
title = {Systems, methods and computer program products for self-tuning sensor data processing},
author = {Draelos, Timothy J. and Faust, Aleksandra and Knox, Hunter A. and Peterson, Matthew Gregor},
abstractNote = {Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {11}
}

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