Abstract
In this paper, a Kalman filter-based sensor fusion filter that measures posture by calibrating and combining information obtained from acceleration and gyro sensors was proposed. Recent advancements in sensor network technology have required sensor fusion technology. In the proposed approach, the nonlinear system model of the filter is converted to a linear system model through a Jacobian matrix operation, and the measurement value predicted via Euler integration. The proposed filter was implemented at an operating frequency of 74 MHz using a Virtex-6 FPGA Board from Xilinx Inc. Further, the accuracy and reliability of the measured posture were validated by comparing the values obtained using the implemented filters with those from existing filters.
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