Abstract
The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL 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: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST 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.
- Developers:
- Release Date:
- 2017-05-01
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Version:
- 0.1
- Licenses:
-
Other (Commercial or Open-Source): https://github.com/sandialabs/Adaptive-Self-Tuning?tab=readme-ov-file#readme
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:NA0003525
- Code ID:
- 73092
- Site Accession Number:
- 7674; SCR# 2210.0
- Research Org.:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Country of Origin:
- United States
Citation Formats
Peterson, Matthew, Draelos, Timothy, and Knox, Hunter.
Adaptive Self Tuning.
Computer Software.
https://github.com/sandialabs/Adaptive-Self-Tuning.
USDOE.
01 May. 2017.
Web.
doi:10.11578/dc.20220414.54.
Peterson, Matthew, Draelos, Timothy, & Knox, Hunter.
(2017, May 01).
Adaptive Self Tuning.
[Computer software].
https://github.com/sandialabs/Adaptive-Self-Tuning.
https://doi.org/10.11578/dc.20220414.54.
Peterson, Matthew, Draelos, Timothy, and Knox, Hunter.
"Adaptive Self Tuning." Computer software.
May 01, 2017.
https://github.com/sandialabs/Adaptive-Self-Tuning.
https://doi.org/10.11578/dc.20220414.54.
@misc{
doecode_73092,
title = {Adaptive Self Tuning},
author = {Peterson, Matthew and Draelos, Timothy and Knox, Hunter},
abstractNote = {The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL 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: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST 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.},
doi = {10.11578/dc.20220414.54},
url = {https://doi.org/10.11578/dc.20220414.54},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20220414.54}},
year = {2017},
month = {may}
}