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A scheme on indoor tracking of ship dynamic positioning based on distributed multi-sensor data fusion

机译:基于分布式多传感器数据融合的船舶动态定位室内跟踪方案

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摘要

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference global positioning system (DGPS) and ultrasonic sensors. Other important factors, including the indoor temperature, position and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications.
机译:通过模拟实验室实际海况来研究模型船动态定位系统,不仅可以避免直接在真实船上进行实验所带来的风险,而且可以降低成本。为了实现动态定位的高精度控制,除了船舶的高精度数学模型外,重要的条件是位置检测系统提供的位置信息必须准确,可靠和连续。当模型船动态定位系统设置在室内时,全球定位系统(GPS)信号受到限制。本文介绍了一种基于多传感器数据融合技术的舰船目标跟踪新方案。为了提高室内定位和船舶目标跟踪的准确性,系统分析了许多传感器的特性,例如雷达,差分全球定位系统(DGPS)和超声传感器。还考虑了其​​他重要因素,包括室内温度,位置和环境,以进一步优化性能。我们框架的核心算法结合了卡尔曼滤波,时间对准,坐标变换和最优融合准则方法,将轨迹相关性作为最优融合的性能指标。实验结果表明,我们的方法优于基于单个超声传感器的方法。估计位置与实际位置之间的最大误差仅为1.32厘米,符合工程应用标准。

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