Trajectory prediction via a feature vector approach
Abstract
Described herein are various technologies pertaining to extracting one or more features from trajectory data recorded during motion of a body, and further, generating a n-dimensional feature vector based upon the one or more extracted features. The n-dimensional feature vector enables expedited analysis of the trajectory data from which the feature vector was generated. For example, rather than having to analyze a trajectory curve comprising a large number of time-position data points, the n-dimensional feature vector can be compared with one or more search parameters to facilitate clustering of the trajectory data associated with the n-dimensional feature vector with other trajectory data which also satisfies the search request. The trajectory data can be plotted on a screen in combination with the n-dimensional feature vector, and other pertinent information. The trajectory data, etc., can be displayed using heat maps or other graphical representation.
- Inventors:
- Issue Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1771505
- Patent Number(s):
- 10801841
- Application Number:
- 15/454,812
- Assignee:
- National Technology & Engineering Solutions of Sandia, LLC (Albuquerque, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01C - MEASURING DISTANCES, LEVELS OR BEARINGS
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 03/09/2017
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Citation Formats
Rintoul, Mark Daniel, Wilson, Andrew T., and Valicka, Christopher G. Trajectory prediction via a feature vector approach. United States: N. p., 2020.
Web.
Rintoul, Mark Daniel, Wilson, Andrew T., & Valicka, Christopher G. Trajectory prediction via a feature vector approach. United States.
Rintoul, Mark Daniel, Wilson, Andrew T., and Valicka, Christopher G. Tue .
"Trajectory prediction via a feature vector approach". United States. https://www.osti.gov/servlets/purl/1771505.
@article{osti_1771505,
title = {Trajectory prediction via a feature vector approach},
author = {Rintoul, Mark Daniel and Wilson, Andrew T. and Valicka, Christopher G.},
abstractNote = {Described herein are various technologies pertaining to extracting one or more features from trajectory data recorded during motion of a body, and further, generating a n-dimensional feature vector based upon the one or more extracted features. The n-dimensional feature vector enables expedited analysis of the trajectory data from which the feature vector was generated. For example, rather than having to analyze a trajectory curve comprising a large number of time-position data points, the n-dimensional feature vector can be compared with one or more search parameters to facilitate clustering of the trajectory data associated with the n-dimensional feature vector with other trajectory data which also satisfies the search request. The trajectory data can be plotted on a screen in combination with the n-dimensional feature vector, and other pertinent information. The trajectory data, etc., can be displayed using heat maps or other graphical representation.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {10}
}
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