Logarithm-Based Methods for Interpolating Quaternion Time Series

被引:3
|
作者
Parker, Joshua [1 ]
Ibarra, Dionne [2 ]
Ober, David [1 ,3 ]
机构
[1] US Army Corps Engineers, Geospatial Res Lab, 7701 Telegraph Rd, Alexandria, VA 22307 USA
[2] Monash Univ, Sch Math, Clayton Campus, Melbourne, Vic 3800, Australia
[3] Purdue Univ, Dept Civil Engn, 610 Purdue Mall, W Lafayette, IN 47907 USA
关键词
quaternions; interpolation; rotations;
D O I
10.3390/math11051131
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this paper, we discuss a modified quaternion interpolation method based on interpolations performed on the logarithmic form. This builds on prior work that demonstrated this approach maintains C-2 continuity for prescriptive rotation. However, we develop and extend this method to descriptive interpolation, i.e., interpolating an arbitrary quaternion time series. To accomplish this, we provide a robust method of taking the logarithm of a quaternion time series such that the variables theta and (n) over cap<^> have a consistent and continuous axis-angle representation. We then demonstrate how logarithmic quaternion interpolation out-performs Renormalized Quaternion Bezier interpolation by orders of magnitude.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Testing for monotonic trend in time series based on resampling methods
    Zhu, Xiaojie
    Ng, Hon Keung Tony
    Woodward, Wayne A.
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2019, 89 (10) : 1899 - 1913
  • [42] INTERPOLATING TIME-SERIES WITH APPLICATION TO ESTIMATION OF HOLIDAY EFFECTS ON ELECTRICITY DEMAND
    BRUBACHER, SR
    WILSON, GT
    THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 1976, 25 (02): : 107 - 116
  • [43] SAR time series despeckling via nonlocal matrix decomposition in logarithm domain
    Kang, Jian
    Ji, Tengyu
    Zhang, Zhe
    Fernandez-Beltran, Ruben
    SIGNAL PROCESSING, 2023, 209
  • [45] Intractability of Learning the Discrete Logarithm with Gradient-Based Methods
    Takhanov, Rustem
    Tezekbayev, Maxat
    Pak, Artur
    Bolatov, Arman
    Kadyrsizova, Zhibek
    Assylbekov, Zhenisbek
    ASIAN CONFERENCE ON MACHINE LEARNING, VOL 222, 2023, 222
  • [46] Bootstrap methods for time series
    Härdle, W
    Horowitz, J
    Kreiss, JP
    INTERNATIONAL STATISTICAL REVIEW, 2003, 71 (02) : 435 - 459
  • [47] Granger causality based on vector time series and quaternion algebra with possible applications to molecular dynamics data analysis
    Sobieraj, Marcin
    Kalinowski, Marek W.
    Lesyng, Bogdan
    PHYSICAL REVIEW E, 2023, 108 (05)
  • [48] Proactive Fiber Break Detection Based on Quaternion Time Series and Automatic Variable Selection from Relational Data
    Lemaire, Vincent
    Boitier, Fabien
    Pesic, Jelena
    Bondu, Alexis
    Ragot, Stephane
    Clerot, Fabrice
    ADVANCED ANALYTICS AND LEARNING ON TEMPORAL DATA, AALTD 2019, 2020, 11986 : 26 - 42
  • [49] Fault diagnosis methods based on a time-series convolution and the comparison of multiple methods
    Lin K.
    Zhou Z.
    Pan D.
    Zhang Y.
    Insight: Non-Destructive Testing and Condition Monitoring, 2022, 64 (09): : 520 - 527
  • [50] A novel optimal transport-based approach for interpolating spectral time series: Paving the way for photometric classification of supernovae
    Ramirez, Mauricio
    Pignata, Giuliano
    Förster, Francisco
    González-Gaitán, Santiago
    Gutiérrez, Claudia P.
    Ayala, Bastian
    Cabrera-Vives, Guillermo
    Catelan, Márcio
    Arancibia, Alejandra M. Muñoz
    Pineda-García, Jonathan
    Astronomy and Astrophysics, 2024, 691