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Title: 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

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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