kalman filter 1 early planar imu 14x28 mm. kalman filter 2 3dof imu - measures two states
TRANSCRIPT
Kalman Filter 1
Early Planar IMU
14x28 mm
Kalman Filter 2
3DOF IMU - Measures Two States
Kalman Filter 3
Tractor Overturn
hr
s
qOP
qOP
qP
rP
rP qOP+ qP
m, JP
2O
Kalman Filter 4
Prevent Rear Overturn
Kalman Filter 5
s = measured signal b = zero drift or bias (function of
temp) f = scale factor (function of temp) w = Gaussian white noise s2 = variance
Sensor Uncertainty
wsfb GYRO
Kalman Filter 6
f = 300 degps/V, 0.05 %/C° b = 1.23 V, 0.05 degps/C° Nonlinearity ±1% s = 0.035 degps/sqrt(Hz) pink noise
LSY530 gyro ±300 degps
wbfVOUT
Kalman Filter 7
Uses state space model Position Velocity
Adaptive time domain filter Combines states Tracks variance-covariance Helps reject zero drift
Kalman Filter
Kalman Filter 8
Kalman Filter - 2D IMU
Kalman Filter 9
Kalman Filter - Simplified
t*
ttt*
t*
ttt*
tt
measuredtoCompare
tCompute
measuredtoCompare
t/Compute
ttimeatandMeasure
Kalman Filter 10
Kalman Filter – Prediction
qlatitude
probability Novice navigator
Kalman Filter 11
Kalman Filter - Measurement
qlatitude
probability Novice navigator
Experienced navigator
Kalman Filter 12
Kalman Filter - Correction
qlatitude
probability Novice navigator
Experienced navigator
Combination
Kalman Filter 13
Kalman Filter - Prediction
qlatitude
probability
constant speed
fixed time
Kalman Filter 14
Kalman Filter – 2D IMU
q angle
probability
elmoddynamic
ttCORRPRED
Kalman Filter 15
Kalman Filter
kk
kkk
1k1kk
xofcovvarPtrack
vxHztsmeasuremenonbased
wxAxstateestimate
noisesensorv
matrixdynamicsensorH
noiseprocessw
matrixdynamicstateA
estimatepriorx
k
1k
1k
matrixgainadaptiveK
vofcovvarR
wofcovvarQ
k
Kalman Filter 16
Kalman Filter
k
kk
k
PHKIP
xHzKxx
RHPHHPK
correction
APAP
xAx
prediction
kk
kkkkk
1TTk
Tk
1kk