In some problems, there is information about the destination of a moving object. An example is a flight from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. We call such trajectories ending up at the destination destination-directed trajectories (DDTs). Described by an evolution law and an initial probability density, a Markov sequence is not flexible enough to model DDT well. The future (including destination) of a Markov sequence is completely determined probabilistically by its initial density and evolution law. One class of conditionally Markov (CM) sequences, called the CM$_\text{L}$ sequence (it includes the Markov sequence as a special case), has the following main components: a joint density of two endpoints and a Markov-like evolution law. This article proposes modeling DDT as CM$_\text{L}$ sequences. We study the CM$_\text{L}$ sequence, its dynamic model, and its realizations, all for DDT modeling. We demonstrate that CM$_\text{L}$ sequences naturally model DDT, enjoy several desirable properties for DDT modeling, and can be easily and systematically generalized if necessary. In addition, we study DDT filtering and trajectory prediction based on a CM$_\text{L}$ model and compare them with those based on a Markov model. Several simulation examples are presented to illustrate DDT modeling and inference.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Destination-Directed Trajectory Modeling, Filtering, and Prediction Using Conditionally Markov Sequences


    Contributors:
    Rezaie, Reza (author) / Li, X. Rong (author)


    Publication date :

    2021-04-01


    Size :

    3496421 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Destination and time-series inference of moving objects via conditionally Markov process

    Lee, Seokwon / Shin, Hyo-Sang | Springer Verlag | 2024

    Free access

    Destination and time-series inference of moving objects via conditionally Markov process

    Lee, Seokwon / Shin, Hyo-Sang | Springer Verlag | 2024

    Free access

    Conditionally-Linear Filtering Using Conditionally-Orthogonal Projection

    Choukroun, D. / Speyer, J. | British Library Conference Proceedings | 2007