← Back to list of papers of the 2022 EuroGNC conference

CEAS EuroGNC 2022

Destination and Time-Series Inference of Moving Objects Using Conditionally Markov Sequences
Seokwon Lee Research Fellow, School of Aerospace, Transport, and Manufacturing, Cranfield University, MK43 0AL, Cranfield, United Kingdom.
Hyo-Sang Shin Professor, School of Aerospace, Transport, and Manufacturing, Cranfield University, MK43 0AL, Cranfield, United Kingdom.
Abstract:
This study aims to design a destination and time-series inference algorithm for tracking moving targets. The destination of the vehicle is considered as the drone's intent, and the inference is accommodated in the Bayesian framework. The destination-driven dynamic model is described by a Conditionally Markov (CM) model. The CM model is utilised in the intent-driven trajectory estimation based on a multiple model adaptive estimator (MMAE), where the bank of Kalman filters produce state estimates and the inferred destination. The proposed inference algorithm is applied to a moving target tracking scenario, and performance is evaluated via the numerical simulation.
Keywords: Intent Inference; Target Tracking; Multiple Model Adaptive Estimation; Bayesian Inference
 View PDF CEAS-GNC-2022-048


Seokwon Lee, Hyo-Sang Shin: Destination and Time-Series Inference of Moving Objects Using Conditionally Markov Sequences. Proceedings of the 2022 CEAS EuroGNC conference. Berlin, Germany. May 2022. CEAS-GNC-2022-048.
BibTeX entry:

@Incollection{CEAS-GNC-2022-048,
    authors = {Lee, Seokwon and Shin, Hyo-Sang},
    title = {Destination and Time-Series Inference of Moving Objects Using Conditionally Markov Sequences},
    booktitle = {Proceedings of the 2022 {CEAS EuroGNC} conference},
    address = {Berlin, Germany},
    month = may,
    year = {2022},
    note = {CEAS-GNC-2022-048}
}