协同导航系统仿真工具的开发

N. Fernandez, S. Schön
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引用次数: 5

摘要

协同定位(CP)描述了一种技术,其中一组动态节点(行人,车辆等)配备了不同的传感器,可以通过交换导航信息以及在节点之间或对环境元素进行测量来提高其定位导航和定时(PNT)信息的精度。此外,在网络中,环境要素(地标、建筑物等)也被认为是一组坐标已知或仅部分已知的固定节点。因此,导航系统可以被认为是一个大地测量网,其中一些节点正在改变它们的位置。在本文中,我们将讨论一个仿真工具的实现,以评估协同定位的好处。由于导航场景中包含多个多传感器系统,因此需要一种传感器测量融合算法来对不同节点进行参数估计。在这里,我们实现了一种批处理算法,可以轻松研究不同参数之间的相关性和依赖性,以及识别传感器网络中对整体性能的关键观测值。通过一个典型的2D汽车导航场景,讨论并说明了当前的实现状况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a simulation tool for collaborative navigation systems
Collaborative Positioning (CP) describes a technique in which a group of dynamic nodes (pedestrians, vehicles, etc.) equipped with different sensors can increase their precision of the Positioning Navigation and Timing (PNT) information by exchanging navigation information as well as performing measurements between nodes or to elements of the environment. In addition, the elements of the environment (landmarks, buildings, etc.) are also considered in the network as a set of fixed nodes with known or only partially known coordinates. Hence, the navigation system can be considered as a geodetic network in which some of the nodes are changing their position. In this paper, we will discuss the implementation of a simulation tool to evaluate the benefits of collaborative positioning. The fact that several multi-sensor systems are included in the navigation situation, brings the necessity of a sensor measurement fusion algorithm for the parameter estimation of the different nodes. Here, we implement a batch algorithm that enables an easy study of correlations and dependencies between different parameters as well as the identification of critical observations in the sensor network for the overall performance. The current status of implementation is discussed and illustrated with a typical 2D car navigation scenario.
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