A calibration procedure and testing of MEMS inertial sensors for an FPGA-based GPS/INS system

Abstract

Given the burgeoning demand for a small, lightweight, consume very little power, low-cost high performance inertial Measurement Units(IMU), test methods are necessary to evaluate the current state of the available of Micro-Electro-Mechanical Systems(MEMS) technology. A reliable and efficient calibration and testing method for evaluating the performance of MEMS IMU is developed, by virtue of MEMS sensor suffering from various errors that have to be calibrated and accomplished to get acceptable navigation results. A prototype development board based on FPGA, dual core processor's configuration for IMU/INS/GPS integrated navigation system is designed for experimental tests. According to the MEMS IMU developed, static tests, rate tests, thermal tests are implemented to estimate the deterministic error such as bias, scale factor, and non-orthogonality. Allan variance method is used to characterize the stochastic noise in inertial sensor. Experimental results are afforded and evaluated.

Development of a system for monitoring performance in the strength and conditioning domain

“…This was as expected as the major acceleration occurs in these two axes. RMS errors in X-, Y-and Z-axes were similar with values of 0.47, 0.36 and 0.45 ms 22 , respectively. Coefficient of determination for velocity was high for the Y-and Z-axes (R 2 = 0.955 and 0.951, respectively, with p = 0.05).…”

“…Each sensing axis can be subjected to + 9.81, 0 and 29.81 ms 22 by a sequence of orientations with respect to the gravity vector, while the six-position and angular rate tests were used to calibrate the accelerometers and gyroscopes used in the study. 22,23 An example output for Z-axis calibration and the corresponding linear regression output model are shown in Figure 14.…”

A Review on Key Issues and Challenges in Devices Level MEMS Testing

“…The major dilemma was to discover the faulty origins in multiple devices that were put together on single test bench for mass production. Recently existing tools [89] have limitations to test specific MEMS devices electrically and mechanically. The emerging challenge is to analyze and identify unique source of failure during assessment of multiple die on single test stage.…”

MEMS IMU stochastic error modelling

“…The deterministic model encapsulates the biases, scale factors and misalignments errors. The parameters associated to the deterministic error model can be obtained by two different approaches: (1) static procedure in a laboratory environment, such as in Wang, Hao, Wei, and Wang (2010) and (2) identification based procedure, with a cost function which incorporates the unknown parameters. See Skog and Händel (2006) for details.…”