为弥补单一数据源不足,并在不同航行环境下,确保导航信号的稳定性与可靠性,研究基于海上观测值的舰船导航信号滤波方法。在舰船导航中,基于纬度、经度、海流分量、航速、航向和角速度变化量等观测值建立了导航信号模型。利用改进量子粒子群优化算法,优化卡尔曼滤波算法的观测噪声协方差矩阵,以及舰船导航信号模型噪声协方差矩阵,得到改进卡尔曼滤波算法,结合舰船导航信号模型,获取舰船导航信号滤波估计结果。实验结果证明,该方法可有效滤波估计舰船导航信号,提升舰船导航信号质量;该方法改进后的舰船导航轨迹与期望轨迹非常接近,即改进后该方法的舰船导航信号滤波效果较优。
To compensate for the shortcomings of a single data source and ensure the stability and reliability of navigation signals in different navigation environments, a ship navigation signal filtering method based on sea observation values is studied. In ship navigation, a navigation signal model is established based on observations such as latitude, longitude, ocean current components, speed, heading, and angular velocity changes. Using the improved quantum particle swarm optimization algorithm, the observation noise covariance matrix of the Kalman filtering algorithm and the noise covariance matrix of the ship navigation signal model are optimized to obtain the improved Kalman filtering algorithm. Combined with the ship navigation signal model, the filtering estimation results of the ship navigation signal are obtained. The experimental results demonstrate that this method can effectively filter and estimate ship navigation signals, improving the quality of ship navigation signals; The improved ship navigation trajectory of this method is very close to the expected trajectory, indicating that the improved ship navigation signal filtering effect of this method is better.
2024,46(19): 113-117 收稿日期:2024-6-11
DOI:10.3404/j.issn.1672-7649.2024.19.019
分类号:TP666.11
作者简介:叶祖超(1981-),男,高级工程师,研究方向为海洋资源开发与利用
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