A Review of Non-Linear Kalman Filtering for Target Tracking

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Author(s): Benjamin Ghansah (University of Education, Winneba, Ghana), Ben-Bright Benuwa (University of Education, Winneba, Ghana), Daniel Danso Essel (University of Education, Winneba, Ghana), Andriana Pokuaa Sarkodie (University of Education, Winneba, Ghana)and Mathias Agbeko (University of Education, Winneba, Ghana)
Copyright: 2022
Volume: 3
Issue: 1
Pages: 25
Source title: International Journal of Data Analytics (IJDA)
Editor(s)-in-Chief: Bruce Qiang Swan (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.294864

Keywords: Data Analysis & Statistics / Data Analysis and Statistics / Information Science Reference / Library & Information Science

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Abstract

Target Tracking (TT) with Non-Linear (NL) Kalman Filtering (NLKF) has recently become a hot research hotspot, particularly in the field of Marine Engineering and air traffic control. This paper presents a comprehensive investigation of NLKF algorithms, with emphases on a proposed theoretical framework to significantly improve its implementation results with regards to accuracy and efficiency. Further, the proposed framework demonstrates potential superior performance in terms of robustness, convergence speed, effective computation and tracking accuracy, comparatively with prior state-of-the-art NLKF techniques. It is anticipated that this study will be beneficial to researchers studying Kalman Filtering (KF) algorithms and also serve as the bedrock for future research, especially for those pursuing their career in Electronics and Information Engineering. Some conclusions and possible research directions of NLKF are proposed in the end.

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A Review of Non-Linear Kalman Filtering for Target Tracking

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