| 基于改进强跟踪无迹卡尔曼滤波的饵料动态称重算法 |
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| 引用本文： | 张丽珍,李旗明,吴迪,保冶君,何睿杰,李志坚.基于改进强跟踪无迹卡尔曼滤波的饵料动态称重算法[J].上海海洋大学学报,2023,32(5):967-977. |
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| 作者姓名： | 张丽珍 李旗明 吴迪 保冶君 何睿杰 李志坚 |
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| 作者单位： | 上海海洋大学 工程学院,上海海洋大学 工程学院,上海海洋大学 工程学院,上海海洋大学 工程学院,上海海洋大学 工程学院,上海海洋大学 工程学院 |
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| 基金项目： | 国家重点研发计划蓝色粮仓科技创新专项项目“海水池塘和盐碱水域生态工程化养殖技术与模式”课题“基于物联网与大数据的池塘养殖智能化投喂与自动化管控技术”(2019YFD0900401)、上海市水产动物良种创制与绿色养殖协同创新中心项目(2021科技02-12)、上海市工程技术研究中心建设计划“上海海洋可再生能源工程技术研究中心”(19DZ2254800)、国家自然科学基金国家重大科研仪器研制项目(62027810)。 |
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| 摘 要： | 投饵是虾塘养殖中的重要环节，获取剩余饵料重量是实现精准投饵的关键。为解决在投饵过程中由环境、系统自身等因素引起的剩余饵料称重不准确问题，提出了一种适用于投饵船饵料动态称重的自适应强跟踪无迹卡尔曼滤波算法(SHFL-ASTUKF)。首先，建立称重系统的二阶模型，并对量测结果进行滤波。然后，在渐消因子快速引入的基础上，使用奇异值分解替换Cholesky分解处理误差协方差问题。同时，结合模糊控制算法和Sage-Husa自适应滤波，自适应更新量测噪声协方差和系统噪声协方差，从而抑制滤波过程中的发散情况。对SHFL-ASTUKF进行实例数据仿真和实验验证，结果表明，与强跟踪无迹卡尔曼滤波相比，实例数据仿真中，RMSE提高了14.9%，投饵船剩余饵料称重实验中RMSE平均提高了15.15%，MAE提高17.27%，所提出的算法具有更高的动态测量精度和更好的降噪效果。
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| 关 键 词： | 精准投饵 动态称重 数据降噪 无迹卡尔曼滤波 模糊控制 |
| 收稿时间： | 2023/6/15 0:00:00 |
| 修稿时间： | 2023/7/31 0:00:00 |
Dynamic weighing algorithm of bait based on improved strong tracking unscented Kalman filtering |
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| ZHANG Lizhen,LI Qiming,WU Di,BAO Yejun,HE Ruijie,LI Zhijian.Dynamic weighing algorithm of bait based on improved strong tracking unscented Kalman filtering[J].Journal of Shanghai Ocean University,2023,32(5):967-977. |
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| Authors: | ZHANG Lizhen LI Qiming WU Di BAO Yejun HE Ruijie LI Zhijian |
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| Institution: | College of Engineering Science and Technology,Shanghai Ocean University,College of Engineering Science and Technology,Shanghai Ocean University,College of Engineering Science and Technology,Shanghai Ocean University,College of Engineering Science and Technology,Shanghai Ocean University,College of Engineering Science and Technology,Shanghai Ocean University,College of Engineering Science and Technology,Shanghai Ocean University |
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| Abstract: | Baiting is an important part of shrimp pond culture, and obtaining the weight of the remaining bait is the key to achieving accurate baiting. In order to solve the problem of inaccurate weighing of the remaining bait caused by the environment, the system itself and other factors., an adaptive strong-tracking untraceable Kalman filtering algorithm (SHFL-ASTUKF) for the dynamic weighing of the bait on baiting boats is proposed. Firstly, a second-order model of the weighing system is established and the measurement results are filtered. Then, based on the fast introduction of the asymptotic cancellation factor, the singular value decomposition is used to replace the Cholesky decomposition to deal with the error covariance problem. Meanwhile, the fuzzy control algorithm and Sage-Husa adaptive filtering are combined to adaptively update the measurement noise covariance and the system noise covariance, so as to suppress the divergence in the filtering process. Example data simulation and experimental validation of SHFL-ASTUKF show that, compared with the strong tracking traceless Kalman filter, the RMSE is improved by 14.9% in the example data simulation, and the RMSE is improved by an average of 15.15% in the experiments of the bait weighing of the remaining bait in the baiting boat and the MAE is improved by 17.27%, which means that the proposed algorithm has a higher dynamic measurement accuracy and a better noise reduction The proposed algorithm has higher dynamic measurement accuracy and better noise reduction effect. |
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| Keywords: | Precision baiting Dynamic weighing Data noise reduction unscented Kalman filter Fuzzy controls |
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