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一种用于船舶自动识别系统中两个基站的改进定位方法。

An Improved Positioning Method for Two Base Stations in AIS.

作者信息

Jiang Yi, Wu Jiani, Zhang Shufang

机构信息

Key Laboratory of Intelligent Waterway Transport Ministry of Transport, Information Science and Technology College, Dalian Maritime University, Dalian 116026, China.

出版信息

Sensors (Basel). 2018 Mar 27;18(4):991. doi: 10.3390/s18040991.

Abstract

Resilient position, navigation, and timing (PNT) data is indispensable information in the field of e-navigation. An automatic identification system (AIS) based ranging mode (R-Mode) is put forward to develop a terrestrial backup system in order to overcome the vulnerability of the global navigation satellite system (GNSS). In general, at least three base stations are required in the traditional R-Mode positioning method. However, the geometric distribution of existing base stations is not considered for positioning, as AIS is a communication system. In some cases, a vessel can only receive signals from two base stations. In this paper, an improved position estimation method based on displacement correction is therefore proposed to solve this problem. Compared with the prior displacement correction position estimation (DCPE) method, the proposed method can improve positioning accuracy effectively by adopting a more precise motion model for the vessel, including an accelerated motion and a turning motion model. Moreover, the motion model is employed adaptively to correct the displacement of the vessel. Finally, the proposed method is verified and the performance is analyzed and compared by simulation. This study can extend the application region of AIS R-Mode.

摘要

弹性定位、导航与授时(PNT)数据是电子导航领域不可或缺的信息。为克服全球导航卫星系统(GNSS)的脆弱性,提出了一种基于自动识别系统(AIS)的测距模式(R-Mode)来开发地面备份系统。一般来说,传统的R-Mode定位方法至少需要三个基站。然而,由于AIS是一个通信系统,现有基站的几何分布在定位时并未被考虑。在某些情况下,船舶可能只能接收到来自两个基站的信号。因此,本文提出一种基于位移校正的改进位置估计方法来解决这一问题。与现有的位移校正位置估计(DCPE)方法相比,该方法通过采用更精确的船舶运动模型,包括加速运动和转向运动模型,能有效提高定位精度。此外,该运动模型被自适应地用于校正船舶的位移。最后,通过仿真对所提方法进行了验证,并对其性能进行了分析和比较。本研究可扩展AIS R-Mode的应用范围。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d104/5948767/48def6b2a09f/sensors-18-00991-g001.jpg

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