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Locality-Sensitive Non-Linear Kalman Filter for Target Tracking

Locality-Sensitive Non-Linear Kalman Filter for Target Tracking
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Author(s): Ben-Bright Benuwa (University of Education, Winneba, Ghana) and Benjamin Ghansah (University of Education, Winneba, Ghana)
Copyright: 2021
Volume: 13
Issue: 1
Pages: 22
Source title: International Journal of Distributed Artificial Intelligence (IJDAI)
Editor(s)-in-Chief: Firas Abdulrazzaq Raheem (University of Technology - Iraq, Iraq) and Israa AbdulAmeer AbdulJabbar (University of Technology - Iraq, Iraq)
DOI: 10.4018/IJDAI.2021010102

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Abstract

Target tracking (TT) with non-linear kalman filtering (NLKF) has recently become a very popular research area, particularly in the field of marine engineering and air traffic control. Contemporary NLKF algorithms have been very effective, in particular, with extensions and merging with a reduced root mean square error (RMSE) value. However, there are a number of issues that confront NLKF approaches, notably weakness in robustness, convergence speed, and tracking accuracy due to large initial error and weak observability. Furthermore, NLKF algorithms significantly results in error for high non-linear systems (NLS) because of the propagation of uncertainty. Again, there is a problem of estimating future states as a result of white noise. To handle these issues, the authors propose a novel non-linear filtering algorithm, called locality-sensitive NLKF (LSNLKF) that incorporates locality-sensitive adaptors into the structure of an integrated NLKF. They are the extended kalman filter (EKF) and the unscented kalman filter (UKF) for TT.

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