L. Marata, J. Chuma, I. Ngebani, A. Yahya, O.L.A. López, Monte Carlo mean for non-Gaussian autonomous object tracking, Computers & Electrical Engineering, Volume 76, 2019, Pages 389-397, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2019.04.004
Monte Carlo mean for non-Gaussian autonomous object tracking
|Author:||Marata, L.1; Chuma, J.1; Ngebani, I.2;|
1Botswana International University of Science and Technology, Botswana
2University of Botswana, Botswana
3University of Oulu, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042019226
|Publish Date:|| 2021-04-24
Object tracking is highly applicable in emerging technologies and is normally done using measurements from sensors. Unfortunately, due to the presence of deleterious noise, measurements are inaccurate and different estimation methods have been developed. Most of them are mainly for Gaussian noise, leaving non-Gaussian noise scenarios unresolved. Also, while particle filters were introduced to address a more general noise scenario, they are mathematically complex especially when used in high dimensional systems. To circumvent these problems, we propose the Separate Monte Carlo Mean (SMC-MEAN) which is formulated on the Bayesian particle filtering framework. The proposed method is applied to an autonomous object tracking problem in both Gaussian and non-Gaussian scenarios. Results are compared to the Kalman filter and Maximum A Posteriori (MAP) in Exponential and Logistic distributed noise. The proposed method outperforms the other methods by an average of 17% yet maintaining low mathematical complexity.
Computers & electrical engineering
|Pages:||389 - 397|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
© 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.