目前国内外许多船舶采用了液位监控系统，极大地提高了船舶的自动化程度。为了保证液位监测的准确性与可靠性，针对环境干扰易导致液位测量结果出现偏差的问题，构建新的液位监控系统，对液位测量的精度进行分析和改进。考虑液位监控系统中存在一步随机时滞的情况，对传感器测量液位的过程进行数学建模，基于卡尔曼滤波和矩阵加权融合算法，提出一种新的多传感器融合算法。通过仿真实验，表明多传感器滤波融合算法能够使液位测量更精确、更稳定，且易于故障监测和分离。
At present, some domestic ships have adopted liquid level monitoring systems, which greatly improves the degree of automation of ships. In order to ensure the accuracy and reliability of the liquid level monitoring system, the accuracy of the liquid level measurement is analyzed and improved in view of the deviations of the measurement result caused by the interference of the environment. Considering the existence of one-step random delay in the liquid level monitoring system, mathematical modeling is carried out for the process of measuring the liquid level with the sensor, based on Kalman filtering and matrix weighted fusion algorithm, a new multi-sensor fusion algorithm is proposed. The simulation results show that the multi-sensor filter fusion algorithm can make the liquid level measurement more accurate and stable, and is easy to monitor and separate faults.
2021,43(6): 157-162 收稿日期：2020-06-20
DOI：10.3404/j.issn.1672-7649.2021.06.030
分类号：U664
基金项目：江苏省产学研合作项目(BY2018208)；江苏省科研与实践创新计划项目(KYCX20_2824)；国家重点研发计划项目(2019YFB2005304)
作者简介：刘江莉(1996-)，女，硕士研究生，研究方向为机电一体化装备及其测控技术。
参考文献：
[1] 周少华, 周根明, 郭霆, 等. 浮船坞沉浮自动控制系统的设计和实现[J]. 船舶工程, 2013, 35(5): 55–58
[2] 施海涛. 船舶液位遥测与阀门遥控系统的设计与实现[D]. 镇江: 江苏大学, 2016.
[3] 王莉娜, 赵先龙, 阮锐, 等. 压力传感器的自动标定技术[J]. 海洋测绘, 2007(4): 78–80
[4] 姚健. 液位遥测在船舶配载中的应用研究[D]. 大连: 大连海事大学, 2019.
[5] 陶峰. 船用高精度液位遥测及控制系统的设计[D]. 镇江: 江苏科技大学, 2016.
[6] TIAN TIAN, SHULI SUN, HONGLEI LIN. Distributed fusion filter for multi-sensor systems with finite-step correlated noises[J]. Information Fusion, 2019, 46: 128–140
[7] 孙甲冰. 多传感器离散随机系统的分布式融合估计研究[D]. 济南: 山东大学, 2011.
[8] 郭颜萍, 胡桐, 李志乾, 等. 船舶相对风的融合算法研究[J]. 舰船科学技术, 2020, 42(5): 34–39
[9] 宋琳, 蔡云泽, 高建喜, 等. 多传感器时滞系统数据丢失时滤波融合算法[J]. 控制工程, 2010, 17(S2): 104–107+123
[10] Z. LI, M. LIU AND B. LI, Research on fusion filtering algorithm for lost observation system[C]//2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2019: 257−261.
[11] 吕楠. 时滞系统的分布式融合滤波[D]. 哈尔滨: 黑龙江大学, 2008.
[12] YI Chun-lei, ZHANG Kun-fan, PENG Neng-ling. A multi-sensor fusion and object tracking algorithm for self-driving vehicles[J]. Proceedings of the Institution of Mechanical Engineers, 2019, 233(9): 2293–2300
[13] KAPALE S, BHARAKAD A. Liquid level estimation in dynamic condition using Kalman filter[J]. International Journal of Engineering Research & Applications, 2016, 6(8): 12–16
[14] 刘庆华, 仲海啸, 陶峰, 等. 卡尔曼滤波融合新算法及其船舶高精度液位测量的应用[J]. 中国造船, 2016, 57(2): 192–200
