互补滤波器和卡尔曼滤波器.pdf

发布时间：2022-04-19 发布人：admin 分类：互联网资料大小：0.32M 资料格式：pdf 举报版权申诉

第1页 / 共15页

第2页 / 共15页

第3页 / 共15页

第4页 / 共15页

第5页 / 共15页

第6页 / 共15页

第7页 / 共15页

第8页 / 共15页

Examples of Estimation Filtersfrom Recent Aircraft Projects at MIT

Vehicles & Navigation Sensors

Complementary filtering

Complementary Filter(CF) Examples

CF1. Roll Angle Estimation

CF2. Pitch Angle Estimation

CF3. Altitude Estimation

CF4. Altitude Rate Estimation

Kalman Filter(KF) Examples

KF 1. Manipulation of GPS Outputs

KF 1. Kalman Filter Setup

KF 2. Removing Rate Gyro Bias Effect

KF 2. Kalman Filter Setup

KF 2. Simulation Result

References

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 Motorola M12 • Inertial • 3 Tokin CG-16D rate gyros • 3 ADXL202 accelerometers • Air Data • Dynamic & absolute pressure sensor • Air temperature sensor • MHX 910/2400 radio modem • MPC555 CPU • Crista Inertial Measurement Unit • 3 Analog Devices ADXL accelerometers • 3 ADXRS MEMs rate sensors Navigation Sensors • GPS Receiver (Marconi, Allstar) • Inertial Sensors - Crossbow 3-axis Accelerometer, Tokin Ceramic Gyro (MINI) or Crossbow IMU (OHS) • Pitot Static Probe: measures airspeed • Altitude Pressure Sensor

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 accelerometer rate gyro θ rate) (angular ≈ ∫ dt θ - not good in long term due to integration High Pass Filter ⎛ = ⎜ ⎝ s τ for , s 1 + τ example ⎞ ⎟ ⎠ estθ ≈ θ accel. ⎛ sin 1 − ⎜⎜ ⎝ output g ⎞ ⎟⎟ ⎠ - only good in long term - not proper during fast motion Low Pass Filter = ⎛ ⎜ ⎝ 1 ⎞ ⎟ s + 1 ⎠τ

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= mVΩ L ≈ mg ≈ Ω r sinφ≈φ φ≈ V g r HPF LPF + + φ Roll angle estimate CF setup p r Roll Rate Gyro Yaw Rate Gyro 1 s V g

CF2. Pitch Angle Estimation • High freq. : integrating pitch rate (q) gyro output • Low freq. : using the sensitivity of accelerometers to gravity direction - “gravity aiding” In steady state AX = g sin θ AZ − = g cosθ AX , AZ − A ⎞ θ = tan 1 ⎛ ⎟ ⎜⎜− x ⎟ ⎝ Az ⎠ − accelerome ter outputs • Roll angle compensation is needed CF setup qmeas φ est A x A z φ est ≈ qmeas cosφ θ est 1 s θ = tan 1 ⎜⎜ − ss − x ⎛ ⎝ Az A cosφ est ⎟⎟ ⎞ ⎠ HPF LPF + + θ est

CF3. Altitude Estimation • Motivation : GPS receiver gives altitude output, but it has ~0.4 seconds of delay. In order of overcome this, pressure sensor was added. • Low freq. : from GPS receiver • High freq. : from pressure sensor CF setup & flight data h from Pressure Sensor h from GPS KF HPF LPF + + h est

CF4. Altitude Rate Estimation • Motivation : GPS receiver gives altitude rate, but it has ~0.4 seconds of delay. In order of overcome this, inertial sensor outputs were added. • Low freq. : from GPS receiver • High freq. : integrating acceleration estimate in altitude direction from inertial sensors CF setup az a y ax Angular Transform est , estθ φ ah 1 s h from GPS KF note : ax⎪ ⎧ ay ⎨ ⎪ a z ⎩ ⎫ ⎪ ⎬ ⎪ ⎭ ⎧ ⎪ ⎨= ⎪ ⎩ Ax Ay Az ⎫ ⎪ ⎬ − φ[ est θ[ ] ⎪ ⎭ est ⎧ ⎪ ⎨] ⎪ ⎩ 0 ⎫ ⎪ 0 ⎬ ⎪ ⎭− g , A Ax z − ]φ[ est , θ[ est ] accelerome angular : ter outputs transforma tion HPF LPF + + hest matrices

分享到：

赞收藏

资料库

互补滤波器和卡尔曼滤波器.pdf

相关推荐

最新资料