A design methodology for the implementation of embedded vehicle navigation systems

This paper presents a design methodology for the implementation of GPS/INS navigation system on Field Programmable Gate Arrays (FPGA). The method proposed in this research is examined using data from three-axis accelerometers and gyroscopes integrated with GPS for a road test experiment in a land vehicle. The designs are described in software which is executed on an embedded microprocessor. Results show that the decentralized closed loop Kalman filter algorithm running on a single precision floating point embedded processor can produce acceptable results relative to those obtained from a desktop PC platform. A first version of the Kalman filter code (used for the optimal integration of INS and GPS) is executed from a 1 MB external SRAM supported by 8 KB of data cache and 4 KB of instruction cache. A second version is run from high speed 64 KB on-chip Block RAM. In the two memory configurations, the maximum sampling frequencies at which the code can be executed are 80 Hz and 119 Hz, respectively, while accelerometer and gyroscope sensors provide data at 75 Hz. Additionally, from the post synthesis timing analyses, the critical frequencies for the two hardware configurations were 63.3 MHz and 83.2 MHz, an enhancement of 26% and 66% respectively over the reference clock of 50 MHz.