为实时准确获得船舶升沉信息，提出一种基于简化Sage-Husa自适应滤波的船舶升沉估计方法。对船舶短时间内测量的升沉信息进行频谱分析，建立近似升沉运动模型，以升沉位移和升沉速度为状态量，升沉加速度为观测量构建简化Sage-Husa自适应滤波器。通过3组仿真验证所提出算法的准确性，结果表明，和卡尔曼滤波相比简化Sage-Husa自适应滤波对随机系统的适应性更好，符合船舶随机运动状态的测量要求，具有更高的实用价值。
In order to obtain ship heave information accurately in real time, a method for ship heave estimation based on simplified Sage-Husa adaptive filtering is proposed. The heave information measured in a short period of time is analyzed by frequency spectrum, and an approximate heave motion model is established. The heave displacement and heave velocity are used as the state variables, and the heave acceleration is used for the observation to construct a simplified Sage-Husa adaptive filter. The accuracy of the proposed algorithm is verified by three sets of simulations. The results show that compared with Kalman filter, the simplified Sage-Husa adaptive filter has better adaptability to the stochastic system, meets the requirements of ship random motion state measurement, and has higher practical value.
2023,45(1): 45-49 收稿日期：2021-11-17
DOI：10.3404/j.issn.1672-7649.2023.01.009
分类号：U666.1
基金项目：浙江省市场监督管理局质量技术基础建设项目(20200132)
作者简介：温小飞(1977-),男,博士,副教授,研究方向为海上无人艇智能感知与测控技术
