Particle filters for estimating average grain diameter of material excavated by hopper dredger

Stano, Paweł Mirosław; Lendek, Zsófia; Babuška, Robert; Braaksma, J.; Keizer, C de

doi:10.1109/cca.2010.5611339

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In general, the accurate calculation of these parameters is not a straightforward task. Fortunately, k e can be calculated using the empirical formula as (8,9) 28.06 6.35…”

Section: Soil-type-dependent Parametersmentioning

confidence: 99%

“…Owing to the time-varying characteristic and high uncertainty of d m , no mathematical model exists currently to describe the real changes in d m . As a consequence, we model it as a random walk, which is commonly applied in the stochastic problem (8)(9)(10)…”

Section: Estimation Problemmentioning

confidence: 99%

“…The observer of the BPF was designed using the measured values of Q o and ρ o for the no-overflow and constant-volume phases. (9) Moreover, the feedback particle filter (FPF) and BPF used for estimating immeasurable parameters for the no-overflow phase were compared. (8,10) The results suggested that the FPF could become a benchmark filter for onboard estimators.…”

Section: Introductionmentioning

confidence: 99%

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Online Estimation of Soil Grain Diameter during Dredging of Hopper Dredger by Continuous-discrete Feedback Particle Filter

Su¹,

Zhou²,

Yu³

et al. 2019

Sensors and Materials

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The trailing suction hopper dredger (TSHD) is a ship that excavates sediments from the sea bottom while sailing. Soil properties have a strong effect on the dredging process. The parameter with the greatest importance is the real-time soil grain diameter d m. This, however, cannot be directly measured by available sensors. In this paper, an alternative method is proposed to solve this problem. A new estimation method is developed on the basis of an existing sedimentation model and measuring system data. The loading process with several grain diameters is simulated to perform a sensitivity analysis of soil type. Simulation results show that the dredging efficiency is strongly affected by fine soil. This soil-related estimation problem is solved with a continuous-discrete feedback particle filter (CD-FPF), which is a recently developed filter for a CD time system. For comparison, a bootstrap particle filter (BPF) is also used to simulate the steplike changes in d m in both the no-overflow and constantvolume loading phases. The results show that the CD-FPF outperforms the BPF in terms of accuracy and applicability. Thus, it is recommended to be applied in the estimator of artificial intelligence (AI) dredging systems.

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“…In general, the accurate calculation of these parameters is not a straightforward task. Fortunately, k e can be calculated using the empirical formula as (8,9) 28.06 6.35…”

Section: Soil-type-dependent Parametersmentioning

confidence: 99%

Section: Estimation Problemmentioning

confidence: 99%