SIMA Raw Data Simulation Software for the Development and Validation of Algorithms. Platforms

Transcription

1 FIG working week 2012 Rome SIMA Raw Data Simulation Software for the Development and Validation of Algorithms for GNSS and MEMS based Multi Sensor Navigation Platforms Andreas Hoscislawski HS Karlsruhe, Germany Seite 1 NAVIGATION STATE & FRAMES Navigation state vector: position (B,L,h) + velocity (,, ) + orientation (r,p,y) x b t B, L, h v,v,v r, p, y x n x b y y n Body y b z n z b y b z b Seite 2 1

2 SENSORS FOR ROBUST AND GLOBAL APPLICATIONS 1.) GNSS References: Inertial Space or e frame 4.) Magnetic field sensors References: Earth MagneticField 2.) Accelerometers References: Inertial Space and Gravity Field 3.) Gyroscopes References: Inertial Space 5.) Inclinometers References: Gravity Field Seite 3 SENSOR CONCEPT FOR STATE ESTIMATION General concept for robust algorithms & sensor simulation: Multiplatform (several platforms (p) navigate one body (b)) and Multisensor Leverarm Concept (several coordinated sensors on each platform) Seite 4 2

3 SIMA: SIMULATION OF MULTISENSOR ARRAYS Numerical comparison of optimized sensor platforms Numerical proof of functionality of new platforms with redundant sensors with sensors in motion Further system tests: Can additional parameters be estimated? Filter reaction on gross errors? Filter reactions on different trajectories? Simplified implementation because true numerical values are known Reference state knownfrom trajectorymodel Seite 5 SIMA LEVER ARM CONCEPT & PARAMETRIZATION t t t, Z e X e x(t) e body t e plat x(t) e sensor Y e body Z p Z s X p Y p 6 LA parameter platform i Y s t(t) e sensor X s platform i 5 LA parameter sensor j sensitive axis of sensor j,α,δ, ,, Seite 6, ε,ε,ε 3

4 SIMA LEVER ARM CONCEPT & M FRAME t _ t t t, body Z s Y s x(t) m body Z e X m x e origin m frame Z m t p plat x(t) e sensor Y e Z p t(t) s sensor X p Y p X s platform i sensitive axis of sensor j X e Seite 7 t t LEVER ARM CONCEPT SENSOR VELOCITY & ACCELERATION t t t,, t t,,,, t.,, Necessary parameters for observation modeling:,,,,,, constant constant, Seite 8 4

5 Trajectory parameters: TRAJECTORY GENERATION t t t t t t Standard models: straight line, circle, helix, in rest, rotating, 2D trajectory Example: Body orientation in a circle v sin v t/r v v =cos(v t / R) yt atan v v Seite 9 GNSS OBSERVATIONS GNSS position t t t,,, GNSS velocity constant t t t,, constanti, Seite 10 5

6 Raw data observation equations: pseudorange GNSS OBSERVATIONS,,,, c Δt, Δt,, ΔIonΔTrop phase l,,,,, c Δt, Δt, λ N λ D, ΔIon ΔTrop Doppler l, f 1, c f, Seite 11 ACCELEROMETER OBSERVATIONS Navigation equation in the inertial frame: Navigation equation in the earth frame: t t 2. Rotation to the s frame: s X a Adding sensor errors:, s Y s Z l, a, κ b n Seite 12 6

7 Gyro observation model: GYROSCOPE OBSERVATIONS. ω s is, x. for one sensor j on platform i: ω, Adding sensor errors: l ω,, κ b n. ω s is, y MEMS 3D Gyroskop 22 x 22 mm ω s is, z Seite 13 MAGNETIC FIELD OBSERVATIONS Magnetic field observation:,t World Magnetic Model 2010 from NOAA (National Oceanic and Atmospheric Administration) & BGS (British Geological Survey): for one sensor j on platform i: m Error model: l 1 0 0, m, n, Seite 14 7

8 INCLINOMETER OBSERVATIONS Observation equation inclinometer: θ, cos direction of gravity: Rotation of sensitive axis in s frame to LAV: 1 0 0, Adding sensor errors: l θc n, Seite 15 SIMA GUI Seite 16 8

9 EXAMPLE ATTITUDE HEADING REFERENCE SYSTEM Navigation state vector t,, Trajectory: Body rotates in rest ,0 Accelerometer biases: Seite 17 EXAMPLE ATTITUDE HEADING REFERENCE SYSTEM Kalmal filtered pitch angle: Kalmal filtered accelerometer biases Seite 18 9

10 CONCLUSION SIMAs features: arbitrary number of different types of sensors freely open platform design consideration of the lever arm effects modeling of sensorerrors different trajectories known reference data for filter validation Perspective: enhanced error modeling adding additional trajectories Seite 19 Thank you for your attention! SIMA available at: Seite 20 10

11 ENHANCED NAVIGATION ALGORITHMS Platform optimization in a similar manner as in the conventional classification in the optimization of geodetical nets: design of 0th order: choise of the appropriate sensor type design of 1th order: choice of optimal sensor position and orientation on platform at given variance for the observations and system state design of 2nd order: choice of optimal observation accuracy at given platform design andvariance ofthesystem state design of 3rd order: choice of additional sensors to optimize given platform design Sensor raw data simulation tool required Seite 21 MULTI SENSOR ALGORITHMS DEVELOPMENT Sensor design differs in sensor type sensor quantity sensor quality location Different sensor designs for different applications dependson: navigation parameters body tractory required accuracy Sensor raw data simulation tool required Seite 22 11