Simulink based prototype for real-time intelligent GNSS-based localisation system

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 enable 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.