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Publications
Ghassemzadeh, A., Xu, H.T. and Guedes Soares, C. (2024), Dynamic evolution of ship state prediction using adaptive unscented Kalman filtering in zigzag manoeuvring tests, Advances in Maritime Technology and Engineering, Guedes Soares, C. & Santos T.A. (Eds.), Taylor and Francis, London, UK, pp. 327-335.
This paper explores ship state prediction during zigzag manoeuvres using the Unscented Kalman Filter. To address uncertainties, it introduces the Adaptive Unscented Kalman Filter, emphasizing covariance matching. The evaluation compares conventional Unscented Kalman Filter and Adaptive Unscented Kalman Filter, highlighting Adaptive Unscented Kalman Filterfs superior accuracy in state estimation.The analysis extends to the adaptive algorithmfs dynamic behavior, revealing a gradual evolution of error covariance over time.The covariance converges to a constant value after around 200 iterations, revealing a critical nuance in the systemfs uncertainty dynamic. Moreover, the limitations of relying solely on prior estimations based on sensor specifications are underscored. The study suggests adapting filtering algorithms for evolving uncertainty in ship dynamic, emphasizing continuous refinement for accurate state estimations in dynamic maritime environments, especially through the use of the Unscented Kalman Filter.
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