船舶动力定位系统中无线传感器网络数据融合技术研究
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摘要
随着人们对海洋开发和探索的范围逐渐扩大,对于海上作业的船舶而言,动力定位控制系统已成为船舶的核心装备之一。船舶动力定位控制系统具有多种位置参考系统和传感器以获得船舶状态及海况环境信息,这就必须对这些测量数据进行融合,从而提高数据的精确度和可靠性以及系统的容错能力。本文将新兴的无线传感器网络(WSN)分布式数据处理结构与目前船舶动力定位控制系统中常用的集中式总线数据处理结构相结合,基于无线传感器网络进行了船舶动力定位控制系统相关数据融合方法的研究。
首先针对船舶动力定位控制系统中面向监测功能的数据融合特点,采用基于传感器节点之间协作感知的方法,通过传感器节点数据信息的关联性分析,确定动态组簇方式；提出了基于移动代理和蚁群遍历相结合的簇内数据融合优化方法,并且研究了分簇结构中基于模糊自适应卡尔曼滤波的异类多传感器融合方法。
然后针对船舶动力定位控制系统中面向控制决策功能的数据融合特点,充分利用FPGA并行运算的硬件资源优势,构建了基于脉动阵列结构的融合模型,使得BP神经网络这样的复杂预测算法能够在WSN网络的汇聚节点得以实现；而且提出了基于不相交集合(DJC)的支持向量机(SVM)预测融合方法,这种模型有两个优点：一是利用强汇聚节点中基于脉动阵列的BP神经网络融合方法实时优化支持向量机模型,克服训练速度慢、易陷入局部最优、过学习问题,保证预测精度；二是将汇聚节点的支持向量机预测计算功能分散到各DJC汇聚节点中进行,各DJC汇聚节点可以选择性将数据上传,这样不仅减轻了汇聚节点的计算负担,而且可有效地实现复杂的优化控制算法。
最后研究了WSN数据融合方法在船舶动力定位系统中的具体应用,建立无线传感器网络分布式融合功能模型和结构模型,研究了模糊自适应卡尔曼滤波算法在船舶状态估计融合中的应用,利用基于DJC模型的支持向量机预测算法解决船舶动力定位MPC控制中二次型最优性能指标的求解问题,得到最优控制推力。针对船舶动力定位系统中面向网络传输功能的数据融合特点,基于经典的分簇路由协议,提出了基于剩余能量和休眠调度机制的数据融合结构,无论是普通传感器节点还是汇聚节点的数据融合方法都是建立在合理可靠的数据融合结构和数据传输方式基础上的,这样不仅增强了整个网络避免拥塞的能力,而且提高了数据传输的实时性。
总的来说,本文主要围绕船舶动力定位系统中无线传感器网络的分簇拓扑结构,从数据级、特征级、决策级各个层面进行传感器节点的数据融合技术研究。其研究成果将很大程度地提高船舶动力定位控制系统中数据采集的实时性和可靠性以及预测控制的准确性。
As the range of marine for people to develop and explora is gradually expanding, for the ships of Marine operation, the Dynamic Positioning Control System has become one of a core component for the ship. The Dynamic Positioning Control System of the Marine has a variety of position reference systems and sensors to monitor the environmental factors, the status of ship and the conditions of ocean, for which we must adopt Data Fusion for the measurements in order to improve the accuracy and reliability of the data and System Fault Tolerance. In this article the distributed data processing structure of the emerging Wireless Sensor Network (WSN) and the centralized-bus data processing structure are combined, the Data Fusion method based on Wireless Sensor Network for the Dynamic Positioning Control System on ship is researched.
Firstly, for the characteristics of monitoring function on data fusion technology in Dynamic Positioning Control System on ship.The fusion method based on the collaboration between the nodes is adopted, the dynamic group clusters can be determined by the qualitative analysis of sensor nodes; the optimization method based on Mobile Agent and Ant Colony Traverse for cluster data fusion is proposed and the fuzzy adaptive Kalman filter method based on the clustering structure for heterogeneous multi-sensor fusion is researched.
Secondly, for the characteristics of decision-making and control-oriented functions on the data integration in Dynamic Positioning Control System on ship, the hard integration model of the FPGA-BP is built by making full use of the advantage of the hardware resources of the parallel computing, which make such complex prediction algorithm of BP neural network model based on the nodes can be achieved in WSN network; The Support Vector Machine prediction algorithm based on disjoint collection is proposed.This model has two advantages:firstly, the use of strong systolic array-based BP neural network fusion method in aggregation node can real-time optimiz Support Vector Machine model to overcome the slow training speed, falling into local optimum easily and over learning problems, it can ensure the prediction accuracy; On the other hand, Support Vector Machine prediction calculation function can be dispersed to the DJC aggregation nodes by setting DJC model in WSN network, and each DJC aggregation node can selectively upload data, so that not only the computational burden of the aggregation node is reduced,but also can effectively implement complex optimal control algorithms.
Finally, how to use the WSN Data Fusion method in ship Dynamic Position System is researched, the distributed control structures is built, the Fuzzy adaptive Kalman filter algorithm used in estimating the status of ship is researched, The Quadratic Optimal Control algorithm performance in the Ship Dynamic Positioning MPC can be solved by Support Vector Machine prediction algorithm based on the DJC model in Wireless Wensore Network, getting the optimal thrust and results. For the characteristics of network and transmission functions in data fusion technology, the data fusion routing algorithm based on sleep scheduling mechanism is studied, data fusion method whether in ordinary sensor nodes or sink node are based on reasonable and reliable data fusion structure, enhancing the ability to avoide entire network congestion, to reduce the delay of the data transmission and to improve the real-time nature.
Overall, this paper mainly around the clustering topology of wireless sensor networks for the Dynamic Positioning System on ship, Data Fusion technology in sensor nodes is researched from the data level, feature-level and decision-making level. The results of research will greatly increase reliability and real-time of data acquisition and the accuracy of the predictive control in the Ship Dynamic Positioning Control System.
首先针对船舶动力定位控制系统中面向监测功能的数据融合特点,采用基于传感器节点之间协作感知的方法,通过传感器节点数据信息的关联性分析,确定动态组簇方式；提出了基于移动代理和蚁群遍历相结合的簇内数据融合优化方法,并且研究了分簇结构中基于模糊自适应卡尔曼滤波的异类多传感器融合方法。
然后针对船舶动力定位控制系统中面向控制决策功能的数据融合特点,充分利用FPGA并行运算的硬件资源优势,构建了基于脉动阵列结构的融合模型,使得BP神经网络这样的复杂预测算法能够在WSN网络的汇聚节点得以实现；而且提出了基于不相交集合(DJC)的支持向量机(SVM)预测融合方法,这种模型有两个优点：一是利用强汇聚节点中基于脉动阵列的BP神经网络融合方法实时优化支持向量机模型,克服训练速度慢、易陷入局部最优、过学习问题,保证预测精度；二是将汇聚节点的支持向量机预测计算功能分散到各DJC汇聚节点中进行,各DJC汇聚节点可以选择性将数据上传,这样不仅减轻了汇聚节点的计算负担,而且可有效地实现复杂的优化控制算法。
最后研究了WSN数据融合方法在船舶动力定位系统中的具体应用,建立无线传感器网络分布式融合功能模型和结构模型,研究了模糊自适应卡尔曼滤波算法在船舶状态估计融合中的应用,利用基于DJC模型的支持向量机预测算法解决船舶动力定位MPC控制中二次型最优性能指标的求解问题,得到最优控制推力。针对船舶动力定位系统中面向网络传输功能的数据融合特点,基于经典的分簇路由协议,提出了基于剩余能量和休眠调度机制的数据融合结构,无论是普通传感器节点还是汇聚节点的数据融合方法都是建立在合理可靠的数据融合结构和数据传输方式基础上的,这样不仅增强了整个网络避免拥塞的能力,而且提高了数据传输的实时性。
总的来说,本文主要围绕船舶动力定位系统中无线传感器网络的分簇拓扑结构,从数据级、特征级、决策级各个层面进行传感器节点的数据融合技术研究。其研究成果将很大程度地提高船舶动力定位控制系统中数据采集的实时性和可靠性以及预测控制的准确性。
As the range of marine for people to develop and explora is gradually expanding, for the ships of Marine operation, the Dynamic Positioning Control System has become one of a core component for the ship. The Dynamic Positioning Control System of the Marine has a variety of position reference systems and sensors to monitor the environmental factors, the status of ship and the conditions of ocean, for which we must adopt Data Fusion for the measurements in order to improve the accuracy and reliability of the data and System Fault Tolerance. In this article the distributed data processing structure of the emerging Wireless Sensor Network (WSN) and the centralized-bus data processing structure are combined, the Data Fusion method based on Wireless Sensor Network for the Dynamic Positioning Control System on ship is researched.
Firstly, for the characteristics of monitoring function on data fusion technology in Dynamic Positioning Control System on ship.The fusion method based on the collaboration between the nodes is adopted, the dynamic group clusters can be determined by the qualitative analysis of sensor nodes; the optimization method based on Mobile Agent and Ant Colony Traverse for cluster data fusion is proposed and the fuzzy adaptive Kalman filter method based on the clustering structure for heterogeneous multi-sensor fusion is researched.
Secondly, for the characteristics of decision-making and control-oriented functions on the data integration in Dynamic Positioning Control System on ship, the hard integration model of the FPGA-BP is built by making full use of the advantage of the hardware resources of the parallel computing, which make such complex prediction algorithm of BP neural network model based on the nodes can be achieved in WSN network; The Support Vector Machine prediction algorithm based on disjoint collection is proposed.This model has two advantages:firstly, the use of strong systolic array-based BP neural network fusion method in aggregation node can real-time optimiz Support Vector Machine model to overcome the slow training speed, falling into local optimum easily and over learning problems, it can ensure the prediction accuracy; On the other hand, Support Vector Machine prediction calculation function can be dispersed to the DJC aggregation nodes by setting DJC model in WSN network, and each DJC aggregation node can selectively upload data, so that not only the computational burden of the aggregation node is reduced,but also can effectively implement complex optimal control algorithms.
Finally, how to use the WSN Data Fusion method in ship Dynamic Position System is researched, the distributed control structures is built, the Fuzzy adaptive Kalman filter algorithm used in estimating the status of ship is researched, The Quadratic Optimal Control algorithm performance in the Ship Dynamic Positioning MPC can be solved by Support Vector Machine prediction algorithm based on the DJC model in Wireless Wensore Network, getting the optimal thrust and results. For the characteristics of network and transmission functions in data fusion technology, the data fusion routing algorithm based on sleep scheduling mechanism is studied, data fusion method whether in ordinary sensor nodes or sink node are based on reasonable and reliable data fusion structure, enhancing the ability to avoide entire network congestion, to reduce the delay of the data transmission and to improve the real-time nature.
Overall, this paper mainly around the clustering topology of wireless sensor networks for the Dynamic Positioning System on ship, Data Fusion technology in sensor nodes is researched from the data level, feature-level and decision-making level. The results of research will greatly increase reliability and real-time of data acquisition and the accuracy of the predictive control in the Ship Dynamic Positioning Control System.
引文
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