Attitude and heading reference system based on recursive robust Kalman filter implemented in FPGA

• Grant number: 16/01899-5
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): June 01, 2016
• Effective date (End): December 31, 2016
• Field of knowledge: Engineering - Electrical Engineering
• Principal Investigator: Marco Henrique Terra
• Grantee: Mundla Narasimhappa
• Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 14/08432-0 - Attitude and heading reference system based on recursive robust Kalman filter implemented in FPGA, AP.ESCIENCE.R

Abstract

This proposal aims to develop a fault tolerant attitude, orientation and position reference system based on a robust Markovian Kalman filter (RMKF) we presented in Cerri et al. (2012). This RMKF performs better than the standard Markovian Kalman filter to the data fusion from low cost inertial measurement units (IMUs). These types of IMU lacks the quality of signals measured in costly IMUs. The main contribution of this postdoctoral research is the development of an RMKF array algorithm for the implementation of the fixed point proposed system in an FPGA (Field Programmable Gate Array) to deal with a set of IMUs. We are proposing a collective modeling of a set of inertial sensors, combining them in a Markovian estimation model, in order to get the best information from each sensor. This project will be implemented by the Intelligent Systems Laboratory (LASI) in partnership with the Mobile Robotics Laboratory (LRM) both of the University of São Paulo at São Carlos. These laboratories develop research in the areas of robotics, robust control and filtering, and estimation of attitude and position of autonomous vehicles. The results of this proposal will be applied in an autonomous truck. (AU)