GNSS Multipath Signals: Mitigation or Constructive Use for Robust Navigation in Urban Canyons?

Transcription

1 Space Reflecto 2013, Brest, 04 November 2013 GNSS Multipath Signals: Mitigation or Constructive Use for Robust Navigation in Urban Canyons? Mohamed SAHMOUDI 1,2 1 ISAE, University of Toulouse, France 2 TeSA, University of Toulouse, France 1

2 Outline GNSS & Problematics in Urban Context Concept of Cognitive Navigation Context-Awarness : user contexte & environment Adaptive processing and fusion Case Study: Predicting GNSS NLOS with a 3D Model [PhD Thesis Aude Bourdeau] GNSS navigation assisted by a 3D model Comparison 3D Simulations v.s. Real Measurements Discussion & Conclusion 2

3 Positioning & Navigation in Urban Environments Shadowing : Reduced visibility RAIM, SV selection with LOS and best GDOP Multipaths: Bias mitigation (specific antenna, signal processing, estimation and cancellation, multiple antennas) Multipath + Shadowing Posintioning with NLOS! Or rely on other sensors Shadowing effect Urban canyons Multipaths / NLOS: ρ measured >ρ real 3

4 Challenges of GNSS Positioning Goal: accurate and trusted positioning in harsh environments such as urban canyons and indoor environments. Satellite i Pseudorange ρ i ρ i = Receiver i ( ) 2 i R S + ( R S ) 2 + ( R S i ) 2 x x y y z z %ρ The receiver determines ρ based on code time delay = Pseudodistance(time delay) + Bias( context ) Detect the context and related parameters? 4

5 Need of Context-Awareness The navigation system must detect its operating context and configurate its processing and fusion algorithms accordingly. New metrics & techniques of context recognition: Environment : outdoor, urban, indoor, interference 3D model, Camera, decision indicators. User s state: moving, static, space constraints, etc IMU Context characterization (on-line): statistical modeling and learning of context parameters. Challenge: Robust navigation needs to consider a variety of different possible contexts in real world.

6 Concept of Cognitive Navigation Innovative Approach: Robust, context-adaptive and deep integration of navigation informations. Sensors -GNSS -IMU, Odometer Signals Measurements Cognitive Detection - GNSS: C/N0, Elevation, Multipath, NLOS - Velocity, angular rate etc.. - Motion and measurments models Camera Context and dynamic state Mode decisions NAV solution Signals Measurements Adaptive Processing Preprocessed Input data Navigation Module Accurate, Robust and continuous localization Pose, Features

7 Cognitive Navigation Fusion Filter Deep Integration of Prior Knowledge of user s Contexte and/or Environment at the level of GNSS Signal Tracking Camera Pose or points of Interest EKF IMU Acceleration, gyration GNSS Correlator Discreminator Space Segmentation SE-NAV Signal Generator

8 Case Study: GNSS Navigation Assisted by a 3D Model Classical approach: Multipath is undesirable Mitigation & suppression However, when we receive only or mostly reflected signals, localization is unreliable or impossible. Our Idea: Exploiting all received GNSS signals including multipath. Knowing the multipath signal path and power is essential. Our methodology: Simulating GNSS signals with a realistic 3D model. A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 8

9 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Problem: How to model paths of the received signals? Geometrical problem? The environment has to be known Received information Reality A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 9

10 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion GNSS principles Challenges in Urban Context GNSS navigation assisted by a 3D model Trajectory and power modeling of the received signals Use of a 3D City Model Validation of the 3D simulation realism necessary before using it in a navigation algorithm. SE-NAV simulation in Toulouse downtown. OKTAL-SE A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 10

11 SE-NAV 3D City Model SE-NAV simulates the reception of GNSS Signal in a 3D virtual environment. SE-NAV assesses the interaction between the signal and the environment (building, cars, user) by taking into account: Shadowing effect (availability of the system) Multipaths (performance) SE-NAV uses the deterministic method of Ray Tracing to compute multipaths. SE-NAV defines 4 computation modes:simulation along a trajectory, Coverage, Replay and Remote control (TCP/IP)

12 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Outline Mathematical Modelling SE-NAV software for 3D modelling A. GNSS & Problematics in Urban Context GNSS principles Challenges in urban context GNSS navigation assisted by a 3D model B. Modelling GNSS Signals with a 3D Model Mathematical modelling of GNSS measurements SE-NAV software for 3D modelling C. Comparison 3D Simulations/Real Measurements D. Conclusion A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 12

13 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Mathematical Modelling SE-NAV software for 3D modelling Modelling of the multipath bias in the pseudorange measurement ρ m = ρ strong + b MP + n m + = α ( τ C ( τ ) = R ( τ ) R i 1 i δτ i ) 1 D( τ ) = 2 C( τ dτ ) C( τ dτ ) + C( τ + dτ ) C( τ + dτ ) b MP ( τ ) = D( τ ) τ Correlation function of GPS L1 C/A signal without noise (red) and its version distorted by multipath (blue). A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 13

14 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Mathematical Modelling SE-NAV software for 3D modelling Modelling of the multipath bias in the pseudorange measurement ρ m = ρ strong + b MP + n m + = α ( τ C ( τ ) = R ( τ ) R 1 i δτ i ) i 1 D( τ ) = 2 C( τ dτ ) C( τ dτ ) + C( τ + dτ ) C( τ + dτ ) b MP ( τ ) = D( τ ) τ Output of the discriminator: Delay estimation error due to multipath A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 14

15 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Mathematical Modelling SE-NAV software for 3D modelling Specifications of the 3D City Model Environment modeling by plans Ray-tracing method to characterize paths of received signals Geometric optics is used for path characterization Assessing main source of attenuation to estimate signal power Information given by the model: geometrical and physical information τ δτ ) α ) ( i ( i A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 15

16 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Mathematical Modelling SE-NAV software for 3D modelling m + = α ( τ C( τ ) = R( τ ) ir δτ i ) i 1 SE-NAV simulations ( i τ δτ ) ( i α ) Mathematical modelling D(τ ) Bias computation Estimated bias for comparison : b = mean( ( τ )) MP b MP b MP (τ ) A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 16

17 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Comparison 3D Simulations/Real Measurements (no noise on the receiver positions) Aerial 3D view of the scene and trajectory Comparison between real pseudorange errors (red dots) and pseudorange errors simulated by SE-NAV (blue dots) A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 17

18 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Comparison 3D Simulations/Real Measurements (no noise on the receiver positions) 3.1m -2.3m Boxplots of the differences between simulatedand real pseudorange errors. Boxplot for all the differences between simulated and real pseudorange errors A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 18

19 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Comparison 3D Simulations/Real Measurements (receiver positions corrupted by a noise uniform in [-8m ; +8m]) 4.6m Boxplots of the differences between simulatedand real pseudorange errors. Boxplot for all the differences between simulated and real pseudorange errors -2.3m A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 19

20 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion SE-NAV Prediction Accuracy SE-NAV predictions improving real measurement error [%] Final error divided at least by two [%] No noise on receiver position Receiver position with a noise on [-4m ; +4m] Receiver position with a noise on [-8m ; +8m] A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 20

21 Mesures : pseudodistances Using 3D Model information in Signal Domain Filtre de Kalman Etendu Modèle d évolution Modèle de mesure Identification de l état de réception par SE-NAV Cas LOS : distance géométrique satellite/récepteur. Cas NLOS : prédiction par SE-NAV. Robustificatio n Pondération des innovations et de leur matrice de covariance du bruit. Test d innovation LOS/NLOS pour garder la configuration la plus probable.

22 Err_sat26_Ublox.eps 22

23 Err_sat26_Ublox.eps 23

24 Using 3D Model information in Signal Domain 24

25 Using 3D Model information in Measurements Domain 25

26 GNSS & Problematics in Urban Context Modelling GNSS Signals with a 3D Model Comparison 3D Simulations/Real Measurements Conclusion Summary Predicting GNSS signal bias with a 3D model Prediction of path and power of the received multipath signals thanks to a realistic 3D model. Mathematical modelling of the multipath effects at the tracking step. Simulation validation by comparison with real data. 3D predictions is useful and can be used in a navigation algorithm. A. Bourdeau, M. Sahmoudi, J.-Y. Tourneret Predicting GNSS signals with a 3D Model 26

27 Thank you for your attention! Questions? 27