research-article

Gaussian Reciprocal Sequences from the Viewpoint of Conditionally Markov Sequences

Authors:

X. Rong LiAuthors Info & Claims

ICVISP 2018: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing

Article No.: 33, Pages 1 - 6

https://doi.org/10.1145/3271553.3271587

Published: 27 August 2018 Publication History

Abstract

The conditionally Markov (CM) sequence contains several classes, including the reciprocal sequence. Reciprocal sequences have been widely used in many areas of engineering, including image processing, acausal systems, intelligent systems, and intent inference. In this paper, the reciprocal sequence is studied from the CM sequence point of view, which is different from the viewpoint of the literature and leads to more insight into the reciprocal sequence. Based on this viewpoint, new results, properties, and easily applicable tools are obtained for the reciprocal sequence. The nonsingular Gaussian (NG) reciprocal sequence is modeled and characterized from the CM viewpoint. It is shown that a NG sequence is reciprocal if and only if it is both CML and CMF (two special classes of CM sequences). New dynamic models are presented for the NG reciprocal sequence. These models (unlike the existing one, which is driven by colored noise) are driven by white noise and are easily applicable. As a special reciprocal sequence, the Markov sequence is also discussed. Finally, it can be seen how all CM sequences, including Markov and reciprocal, are unified.

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Cited By

Rezaie RLi XJilkov V(2021)Conditionally Markov Modeling and Optimal Estimation for Trajectory With Waypoints and DestinationIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2021.307553357:4(2006-2020)Online publication date: Aug-2021
https://doi.org/10.1109/TAES.2021.3075533
Rezaie RLi X(2021)Destination-Directed Trajectory Modeling, Filtering, and Prediction Using Conditionally Markov SequencesIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2020.303183657:2(820-833)Online publication date: Apr-2021
https://doi.org/10.1109/TAES.2020.3031836
Rezaie RLi X(2021)Gaussian conditionally Markov sequencesAutomatica (Journal of IFAC)10.1016/j.automatica.2021.109780131:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.automatica.2021.109780
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Index Terms

Gaussian Reciprocal Sequences from the Viewpoint of Conditionally Markov Sequences
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic representations
      1. Markov networks
      2. Stochastic differential equations
    2. Stochastic processes
      1. Markov processes

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ICVISP 2018: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing

August 2018

402 pages

ISBN:9781450365291

DOI:10.1145/3271553

Copyright © 2018 ACM.

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Published: 27 August 2018

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ICVISP 2018: The 2nd International Conference on Vision, Image and Signal Processing

August 27 - 29, 2018

NV, Las Vegas, USA

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Cited By

Rezaie RLi XJilkov V(2021)Conditionally Markov Modeling and Optimal Estimation for Trajectory With Waypoints and DestinationIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2021.307553357:4(2006-2020)Online publication date: Aug-2021
https://doi.org/10.1109/TAES.2021.3075533
Rezaie RLi X(2021)Destination-Directed Trajectory Modeling, Filtering, and Prediction Using Conditionally Markov SequencesIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2020.303183657:2(820-833)Online publication date: Apr-2021
https://doi.org/10.1109/TAES.2020.3031836
Rezaie RLi X(2021)Gaussian conditionally Markov sequencesAutomatica (Journal of IFAC)10.1016/j.automatica.2021.109780131:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.automatica.2021.109780
Rezaie RLi X(2020)Gaussian Conditionally Markov Sequences: Algebraically Equivalent Dynamic ModelsIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2019.295118856:3(2390-2405)Online publication date: Jun-2020
https://doi.org/10.1109/TAES.2019.2951188
Rezaie RLi X(2020)Gaussian Conditionally Markov Sequences: Singular/NonsingularIEEE Transactions on Automatic Control10.1109/TAC.2019.294436365:5(2286-2293)Online publication date: May-2020
https://doi.org/10.1109/TAC.2019.2944363
Rezaie RLi X(2020)Mathematical Modeling and Optimal Inference of Guided Markov-Like Trajectory2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)10.1109/MFI49285.2020.9235241(26-31)Online publication date: 14-Sep-2020
https://doi.org/10.1109/MFI49285.2020.9235241
Tang MRong YLi X(2019)Markov and Conditionally Markov Processes: from Gaussian to Elliptical2019 22th International Conference on Information Fusion (FUSION)10.23919/FUSION43075.2019.9011274(1-8)Online publication date: Jul-2019
https://doi.org/10.23919/FUSION43075.2019.9011274
Rezaie RLi X(2018)Models and Representations of Gaussian Reciprocal and Conditionally Markov SequencesProceedings of the 2nd International Conference on Vision, Image and Signal Processing10.1145/3271553.3271598(1-6)Online publication date: 27-Aug-2018
https://dl.acm.org/doi/10.1145/3271553.3271598

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