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Sea state identification based on vessel motion response learning via multi-layer classifiers

机译：基于多层分类器的基于船舶运动响应学习的海况识别

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摘要

In order to extend the operational weather window for marine vessels under Dynamic Positioning (DP) control, a novel sea state identification method with multi-layer classifiers is proposed in this paper. Due to the distinction of system responses for various sea states, four motion signals including surge, sway, roll and yaw are adopted for classification purpose. Firstly, preprocessing techniques, like filtration and k-means clustering are performed to the raw data to filter out the "corrupted" low frequency (LF) information and generate the band-pass filter bank. Then, the processed data is decomposed into 20 categories via Hilbert-Huang transform (HHT), filter bank method and wavelet transform and 11 statistical features are extracted for each category. Subsequently, Max relevance Min-redundancy (mRMR) method helps to select salient features with best trade-off between relevance and redundancy. With these selected features, a newly developed three-layer classification structure with Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF) and Particle Swarm Optimization (PSO) based combination classifiers is proposed to derive the current sea state. The simulation results demonstrate that the proposed identification system can achieve satisfactory classification accuracy. Moreover, the multi-layer classifier outperforms single layer classifier and can rapidly classify the sea state in real-time implementation.
机译：为了扩展动态定位（DP）控制下的船舶运行天气窗口，提出了一种具有多层分类器的海况识别方法。由于不同海况下系统响应的区别，因此采用了浪涌，摇摆，侧倾和偏航四个运动信号进行分类。首先，对原始数据执行预处理技术，例如滤波和k均值聚类，以过滤掉“损坏的”低频（LF）信息并生成带通滤波器组。然后，通过Hilbert-Huang变换（HHT），滤波器组方法和小波变换将处理后的数据分解为20个类别，并为每个类别提取11个统计特征。随后，最大相关性最小冗余（mRMR）方法有助于选择在相关性和冗余之间取得最佳折衷的显着特征。利用这些选定的特征，提出了一种新的基于自适应神经模糊推理系统（ANFIS），随机森林（RF）和基于粒子群优化（PSO）的组合分类器的三层分类结构，以得出当前的海洋状态。仿真结果表明，所提出的识别系统可以达到满意的分类精度。此外，多层分类器优于单层分类器，并且可以在实时实现中快速对海状态进行分类。

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