Real-Time Embedded System Design and Realization for Integrated Navigation Systems
Abdelfatah, Walid Farid
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Navigation algorithms integrating measurements from multi-sensor systems overcome the problems that arise from using GPS navigation systems in standalone mode. Algorithms which integrate the data from 2D low-cost reduced inertial sensor system, consisting of a gyroscope and an odometer, along with a GPS via a Kalman filter has proved to be worthy in providing a consistent and more reliable navigation solution compared to the standalone GPS. It has been also shown to be beneficial, especially in GPS-denied environments such as urban canyons and tunnels. The main objective of this research is to narrow the idea-to-implementation gap that follows the algorithm development by realizing a low-cost real-time embedded navigation system that is capable of computing the data-fused positioning solution instantly. The role of the developed system is to synchronize the measurements from the three sensors, GPS, gyroscope and odometer, relative to the pulse per second signal generated from the GPS, after which the navigation algorithm is applied to the synchronized measurements to compute the navigation solution in real-time. Xilinx’s MicroBlaze soft-core processor on a Virtex-4 FPGA is utilized and customized for developing the real-time navigation system. The soft-core processor offers the flexibility to choose or implement a set of features and peripherals that are tailored to the specific application to be developed. An embedded system design model is chosen to act as a framework for the work flow to be carried through the system life cycle starting from the system specification phase and ending with the system release. The developed navigation system is tested first on a mobile robot to reveal system bugs and integration problems, and then on a land vehicle testing platform for further testing. The real-time solution from the implemented system when compared to the solution of a high-end navigation system, proved to be successful in providing a comparable consistent real-time navigation solution. Employing a soft-core processor in the kernel of the navigation system, provided the flexibility for communicating with the various sensors and the computation capability required by the Kalman filter integration algorithm.