Abstract
As a noise analysis of MEMS IMU, the traditional Allanvariance methods have large computational burden because of requiring tostore a large amount of data. Moreover, the procedure of drawing slope linesfor estimation is also painful. In order to overcome these drawbacks, aonline method is proposed to estimate the Allan variance parameters, whichdirectly model sensors random errors including quantization noise, angularrandom walk, bias instability, rate random walk and rate ramp into anonlinear state space model and then implemented by sage-husa adaptiveKalman filter algorithm. The comparison of results of real ADIS16405 IMUstatic gyro noise analyzed by Allan variance method and the proposedapproach shows that the results from the proposed method are well within theerror limits of Allan variance method. Moreover, the technique proposed hereestimates the Allan variance coefficients in real time, effectively avoidsstorage of history data and manual analysis for an Allan variance graph
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
