Vehicles & Navigation Sensors
OHS (Outboard
Horizontal Stabilizer)
Navigation Sensors (Piccolo from Cloudcap Tech)
• GPS Motorola M12
• Inertial
• 3 Tokin CG-16D rate gyros
• 3 ADXL202 accelerometers
Navigation Sensors
•Air Data
• GPS Receiver (Marconi, Allstar)
• Dynamic & absolute pressure sensor
• Inertial Sensors
• Air temperature sensor
- Crossbow 3-axis Accelerometer,
• MHX 910/2400 radio modem
Tokin Ceramic Gyro (MINI) or
• MPC555 CPU
Crossbow IMU (OHS)
• Pitot Static Probe: measures
• Crista Inertial Measurement Unit
airspeed
• 3 Analog Devices ADXL accelerometers
• Altitude Pressure Sensor
• 3 ADXRS MEMs rate sensors
Complementary Filter (CF)
Often, there are cases where you have two different measurement sources for
estimating one variable and the noise properties of the two measurements
are such that one source gives good information only in low frequency region
while the other is good only in high frequency region.
Æ You can use a complementary filter !
Example : Tilt angle estimation using accelerometer and rate gyro
≈
∫
rate)(angular dt
- not good in long term
due to integration
outputaccel.
⎞
⎟
⎠
+
τ
τ
⎛
⎜
⎝
s
1
examplefor,
s
=
est
θ
accelerometer rate gyro
High Pass Filter
⎞
⎛
θ
θ
1
g
- not proper during fast motion
⎞
⎟
⎠
τ
=
⎛
⎜
⎝
1
s +
−
sin
1
- only good in long term
Low Pass Filter
⎟
⎟
⎠
⎜
⎜
⎝
≈
θ
Complementary Filter(CF) Examples
• CF1. Roll Angle Estimation
• CF2. Pitch Angle Estimation
• CF3. Altitude Estimation
• CF4. Altitude Rate Estimation
CF1. Roll Angle Estimation
• High freq. : integrating roll rate (p) gyro output
• Low freq. : using aircraft kinematics
-
Assuming steady state turn dynamics,
roll angle is related with turning rate, which is close to yaw rate (r)
L
sin
φ
=
mV
Ω
L ≈ mg
V
φ
≈ r
g
≈ Ω
r
sin
φ
≈
φ
Roll
CF setup
Rate
Gyro
Yaw
Rate
Gyro
1
s
HPF
LPF
V
g
+
+
Roll
angle
estimat
e
p
r
φ