EXamples of Estimation Filters from Recent Aircraft Projects at MIT November 2004 Sanghyuk Park and Jonathan How
Examples of Estimation Filters from Recent Aircraft Projects at MIT November 2004 Sanghyuk Park and Jonathan How
Vehicles Navigation Sensors OHS(Outboard Horizontal stabilizer) Navigation Sensors(Piccolo from Cloudcap Tech) · GPS Motoro|aM12 Inertial 3 Tokin CG-16D rate gyros 3 ADXL202 accelerometers Navigation Sensors · Air data GPS Receiver(Marconi, Allstar) Dynamic& absolute pressure sensor ·| neria| Sensors Air temperature sensor Crossbow 3-axis Accelerometer MHX 910/2400 radio modem MPC555 CPU Tokin Ceramic Gyro MINior 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
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 You can use a complementary filter Example: Tilt angle estimation using accelerometer and rate gyro accelerometer rate gyro High Pass Filter for example 0≈| (angular rate) dt not good in long term due to integration 0≈sn- accel. output g only good in long term Low Pass Filter not proper during fast motion
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 ≈ ∫(angular rate) dt - not good in long term due to integration outputaccel. ⎞ ⎟ + ⎠ τ τ ⎛ ⎜ ⎝ s 1 examplefor, s = θ est 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
Complementary Filter(CF) Examples • CF1. Roll Angle Estimation • CF2. Pitch Angle Estimation • CF3. Altitude Estimation • CF4. Altitude Rate Estimation
CFI. 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) Lsing=mvQ2 L≈mg g np≈p mg CF setup Roll Rate p HPF Gyro Roll angle Yaw r estimate Rate LPF yro
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 = mVΩ L ≈ mg V φ ≈ r g Ω ≈ r sin φ ≈ φ CF setup Roll Rate Gyro Yaw Rate Gyro 1 s HPF LPF V g + + Roll angle estimate p r φ