Intelligent calibration method of low cost MEMS inertial measurement unit for an FPGA-based navigation system

Abstract

Based on a small, lightweight, low-cost high performance inertial Measurement Units(IMU), an effective calibration method is implemented to evaluate the performance of Micro-Electro-Mechanical Systems(MEMS) sensors suffering from various errors to get acceptable navigation results. A prototype development board based on FPGA, dual core processor's configuration for INS/GPS integrated navigation system is designed for experimental testing. The significant error sources of IMU such as bias, scale factor, and misalignment are estimated in virtue of static tests, rate tests, and thermal tests. Moreover, an effective intelligent calibration method combining with Kalman filter is proposed to estimate parameters and reduce the effect of IMU dynamic errors that can degrade the system performance. The efficiency of proposed approach is demonstrated by various experimental scenarios.