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首页> 外文会议>International Association of Institutes of Navigation 15th World Congress 2015 >Simulink based prototype for real-time intelligent GNSS-based localisation system
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Simulink based prototype for real-time intelligent GNSS-based localisation system

机译：基于Simulink的原型，用于基于GNSS的实时智能本地化系统

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In the frame of the development of artificial intelligent (AI) based validation tool for Global Navigation Satellite Systems (GNSS) localisation systems MATLAB Simulink models for all the developed AI-based methodologies have been created. The combination of Artificial Neural Network (ANN) based validation tools with the Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation for quantitative and qualitative analysis of trueness and precision, as well as Particle Filter (PF) techniques for position estimation to aid the reference system result in significant improvements for GNSS-aided localisation system, allowing the construction of a GNSS-dependent reference system for when no independent reference is available. These additional elements are the bases for future intelligent GNSS-based localisation systems with both quantitative and qualitative validation tools. The proposed prototype constructed within System functions (S-Functions) for extension of MATLAB Simulink capabilities presents total interconnectivity in real-time, implemented using commodity microcomputers and providing an interface between the MATLAB Simulink implemented algorithms and the real world. In the presented prototype a Raspberry Pi Model B microcomputer running a modified Debian Linux distribution provides General Purpose and UART ports that enable the implementation of the low-level communication driver for the GNSS receiver. The prototype enables real-time application of the algorithms. The prototype includes a GNSS receiver, with a sampling position rate and constellation data at 1Hz. NMEA format provides the necessary data to be processed with the MATLAB Simulink model of the ANN-based validation tools and the PF-based estimator. All algorithms are executed in a Windows PC and the communication between the microcomputer and the MATLAB Simulink model is achieved via TCP Sockets, with interface S-Functions to handle the communication. These methodologies will enab- e future safety-relevant applications, such as on-board uncertainty evaluation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective.
机译：在开发用于全球导航卫星系统（GNSS）定位系统的基于人工智能（AI）的验证工具的框架中，已为所有已开发的基于AI的方法论创建了MATLAB Simulink模型。基于人工神经网络（ANN）的验证工具与用于精确性定量和定性分析的基于准确性的数据评估的Mahalanobis椭圆滤波器（MEF）方法以及用于位置估计的粒子滤波（PF）技术相结合有助于参考系统大大改善了GNSS辅助的定位系统，允许在没有独立参考可用的情况下构建GNSS依赖的参考系统。这些附加元素是未来基于GNSS的基于定位和定性验证工具的智能本地化系统的基础。在系统函数（S函数）中构造的拟议原型，用于扩展MATLAB Simulink功能，可实时显示全部互连性，可使用商用微型计算机来实现，并提供MATLAB Simulink实现的算法与现实世界之间的接口。在提出的原型中，运行修改过的Debian Linux发行版的Raspberry Pi B型微型计算机提供了通用端口和UART端口，这些端口可实现GNSS接收器的低级通信驱动程序的实现。该原型实现了算法的实时应用。该原型机包括一个GNSS接收器，具有1Hz的采样位置速率和星座数据。 NMEA格式提供了使用基于ANN的验证工具的MATLAB Simulink模型和基于PF的估计器进行处理所需的数据。所有算法都在Windows PC上执行，并且微型计算机与MATLAB Simulink模型之间的通信是通过TCP套接字（带有接口S-Functions处理通信）实现的。这些方法将促进未来与安全相关的应用，例如车载不确定性评估；先进的驾驶员辅助系统；以及基于GNSS的车辆定位以及智能地图，以进行轨道选择。

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