Monte Carlo mean for non-Gaussian autonomous object tracking. (June 2019)

• Record Type: Journal Article
• Authors: Marata, L.; Chuma, J.; Ngebani, I.; Yahya, A.; López, O.L.A.
• Is Part Of: Computers & electrical engineering. Volume 76(2019)
• Page Start: 389
• Page End: 397
• Publication Date: 2019-06
• Subjects: Autonomous tracking -- Kalman filter -- Maximum a posteriori -- Particle filtering -- Cramér–Rao lower bound; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.04.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.