Real-Time Attitude Estimation of Sigma-Point Kalman Filter via Matrix Operation Accelerator

Abstract

Attitude estimation is an important part for navigation of mobile robotics and unmanned aerial vehicle (UAV) control. Although the Extended Kalman Filter (EKF) can be done typically, the trend is to use Sigma-Point Kalman Filter (SPKF) instead due to its higher accuracy and robustness in harsh environment. The only drawback of such system is the higher computation cost. In order to accelerate the system, most approaches based on Field Programmable Gate Arrays (FPGA) are proposed in the past but too specific, which is not reusable and the high price for design complexity. With looking for re-usability, we present an IP core called matrix operation accelerator in this paper. Moreover, we do the verification on Zynq-7020, the experimental result shows that the proposed scheme can reduce about 50% computing time and save silicon as well.