Gaussian sum state estimators for three dimensional angles-only underwater target tracking problems

Gaussian sum filters are considered to be more accurate in terms of estimation accuracy when compared to the conventional algorithms. In this work, Gaussian sum state estimation algorithms are implemented for three dimensional angles-only target tracking problem. Shifted Rayleigh filter (SRF) has been considered as the most accurate estimation algorithms for bearings-only tracking, with moderate computational load. Therefore, SRF formulated in the Gaussian sum framework is developed for solving three dimensional angles-only target tracking problems. The estimation accuracy of the developed algorithm, and other Gaussian as well as Gaussian sum algorithms is validated in terms of percentage track-loss and root mean square error (RMSE). Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Automated summary

In this study, Gaussian sum state estimation algorithms are applied to the problem of three-dimensional angles-only target tracking, and compared with conventional algorithms. The results show that Gaussian sum filters achieve higher estimation accuracy.