Part. A: A review of the real-time sea-state estimation, using wave buoy analogy

Majidian, Hamed; Wang, Lei; Enshaei, Hossein

doi:10.1016/j.oceaneng.2022.111684

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Predictor-corrector filters and observers are widely used for state and parameter estimation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. These filters can be roughly divided into two categories: those aiming for optimal solutions and those focused on maintaining stability [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. One of the most recent filters in the latter category is the Sliding Innovation Filter (SIF) .…”

Section: Introductionmentioning

confidence: 99%

The application of sliding innovation filter as estimation strategy applied to a UAV

AlShabi,

Gadsden,

Khuwaileh

et al. 2024

Unmanned Systems Technology XXVI

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The unmanned aerial vehicle (UAV) and unmanned aerial system (UAS) are popular in nowadays applications including military, industry, weather casting, monitoring, and many other applications. According to several research, the system must be controlled in precise way to make sure that the UAV and UAS are moving in the desired trajectories. However, this task is not an easy task in real life due to the presence of disturbances and noise in feedback measurements. To overcome this issue, researchers either developed more stable controllers, i.e. active disturbance rejection control (ADRC), or they improved the measured signals using filters with more accurate/stable performance. This work belongs to the second category, where a newly developed filter, which is referred to as sliding innovation filter (SIF), is used to estimate the states of a UAV system while it is tracking a target at the same height to improve the quality of the controller.

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Section: Introductionmentioning

confidence: 99%

The application of sliding innovation filter as estimation strategy applied to a UAV

AlShabi,

Gadsden,

Khuwaileh

et al. 2024

Unmanned Systems Technology XXVI

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show abstract

“…Despite simplicity and amenability, several downsides are associated with this approach. First, these models are basically constructed based on stationarity or low-order non-stationarity, absolute convergence of Autocovariance Function (ACVF), specific conditions for spectral density, and Gaussian distribution for joint probability distribution density, where the optimal prediction must meet all conditions [9] that cannot be met always in real-world data, i.e., one sea condition to another [13]. For instance, in [12], the predictor failed to capture amplitude precisely above 8s due to non-stationarities.…”

Section: Introductionmentioning

confidence: 99%

Online Short-term Ship Response Prediction with Dynamic Buffer Window Using Transient Free Switching Filter

Majidiyan,

Enshaei

et al. 2023

Preprint

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Ship response prediction is one of the emerging interests in seakeeping, given the extensive range of applications for autonomous control of marine vehicles. In particular, the shortterm prediction and online updates of the ship response have been in the practical attention span. Despite a body of studies on different predictors, the asymptotic properties of estimators concerning the time series sample size have not been addressed specifically. The predictors only have been analysed for a fixed observation window regardless of the intrinsic statistical characteristics obtainable in the time series. To this end, the current research has considered the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy highly depends on the observation window length with respect to the statistical properties of the responses in various wave conditions. Therefore, an innovative filter using the weighted average of the proposed functions has been introduced to dynamically adjust the buffer window based on the signal statistical characteristics. The promising results showed that the filter not only could contribute to promoting any predictors used in autonomous onboard decision-making, guidance, and stabilization systems for present and future intelligent onboard control systems, but the proposed mechanism can be employed in dynamic systems in other disciplines too.

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Learning Dynamic Graph Structures for Sea State Estimation with Deep Neural Networks

Wang,

Cheng,

Shi

2023

2023 6th International Conference on Intelligent Autonomous Systems (ICoIAS)

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Part. A: A review of the real-time sea-state estimation, using wave buoy analogy

Cited by 6 publications

References 35 publications

The application of sliding innovation filter as estimation strategy applied to a UAV

The application of sliding innovation filter as estimation strategy applied to a UAV

Online Short-term Ship Response Prediction with Dynamic Buffer Window Using Transient Free Switching Filter

Learning Dynamic Graph Structures for Sea State Estimation with Deep Neural Networks

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