2. OUTLINE Motivation Modeling Simulation Experiments Conclusions

3. OUTLINE Motivation Modeling Simulation Experiments Conclusions

4. GNSS Constellations GNSS are all weather L-band satellite sytems dedicated to navigation purposes:

5. GNSS Constellations GNSS are all weather L-band satellite sytems dedicated to navigation purposes: GPS: 24 satellites Glonass: 24 satellites Beidou: 35 satellites (completed 2020) Galileo: 27 satellites (operative 2020)

6. GNSS Constellations The GNSS are designed to provide Positioning, Velocity and Time (PVT) to an user with a receiver

7. GNSS Constellations The distance satellite-user measuring the Time of Arrival (ToA) of the direct signal, i.e. Line of Sight (LoS). 4 satellites are needed to compute x,y,z and time The GNSS are designed to provide Positioning, Velocity and Time (PVT) to an user with a receiver.

8. GNSS Signal

9. Pseudo-Random-Noise (PRN) codes: zero mean: constant envelope t t+τ c t-τ c 1 PRN is a sequence of random rectangluar pulses called chips: Autocorrelation = 1 Cross-correlation = 0

10. GNSS-REFLECTOMETRY

11. GNSS Reflectometry (GNSS- R) is an innovative technique that exploits GNSS signals reflected off surfaces as signals of opportunity to infer geophysical information of the reflecting scene.

12. GNSS-R vs Remote Sensing Missions

13. Excellent temporal sampling and global coverage; Long-term GNSS mission life; Cost effectiveness, i.e. only a receiver is needed.

14. GNSS-R vs Remote Sensing Missions Excellent temporal sampling and global coverage; Long-term GNSS mission life; Cost effectiveness, i.e. only a receiver is needed. GNSS satellites coverage Snapshot

15. GNSS-R Applications

16. Soil moistureIce observation Altimetry Sea surface observation

17. Sea Surface Observation

18. Off shore wind farmCoastal erosion Weather forecasting Maritime control in harbor areas

19. OUTLINE Motivation Modeling Simulation Experiments Conclusions

20. GNSS-R for Sea Surface Observation Model

21. Specular reflection dominates this scattering scenario, Geometric Optic (GO) approximation has been used. For smooth surface, e.g. calm see Tx Rx Specular Point

22. GNSS-R for Sea Surface Observation Model When the sea roughness increases, the transmitted signal is spreaded over the sea surface and different points within the so called Glistening Zone (GZ) contribute to the scattered power Tx Rx glistening zone Tx Rx glistening zone

23. GNSS-R Geometry Modeling

25. Nominal Specular Point (SP) is in the origin of axes; Transmitter and receiver lie in the zy plane; Points whose scattered wave experiences the same delay lie in an ellippse with Tx and Rx as its foci (iso-range ellipse) Points whose scattered wave experiences the same frequncy shift lie in an hyperbola (iso- Doppler hyperbola) The received power is mapped in Delay Doppler Map (DDM)

26. OUTLINE Motivation Modeling Simulation Experiments Conclusions

27. GNSS-R Model Simulation Simulated data are different from real data but are very Important:

28. GNSS-R Model Simulation Simulated data are different from real data but are very Important: To better understand the scattering scenario To simulate a complex scenario in a controlled environment

29. GNSS-R Simulation

30. The received average scattered power is given by:

31. GNSS-R Simulation The received average scattered power is given by: Where T i is the coherent integration time D is the radiation antenna pattern R t and R r are the distances between Tx-scatterer and Rx-scatterer, respectively Λ()S() represents the Woodward Ambiguity Function (WAF) is the Fresnel coefficient accounting polarization from RHCP to LHCP σ o is the Normalized Radar Cross Section (NRCS) – Gaussian slopes PDF

32. Woodward Ambiguity Function

33. The WAF represents the cross-correlation performed at the receiver between the scattered signal and the generated replica, where

34. Woodward Ambiguity Function The WAF represents the cross-correlation performed at the receiver between the scattered signal and the generated replica, where Along the the delay axes, the overlapping of rectangular chips generated a trianglura shape function:

35. Woodward Ambiguity Function The WAF represents the cross-correlation performed at the receiver between the scattered signal and the generated replica, where Along the the delay axes, the overlapping of rectangular chips generated a trianglura shape function: Along the Doppler axes a sinc function is generated: For low speed receiver, i.e. airborne or fixed platform, the Doppler effect can be neglected and S(δf) = 1 and 1-D Delay Map is generated.

36. OUTLINE Motivation Modeling Simulation Experiments Conclusions

37. Experiments In this study the potentialities of GPS L1 C/A signal for sea surface wind speed estimation have been investigated and the system sensitivity has been evaluated against:

38. Experiments In this study the potentialities of GPS L1 C/A signal for sea surface wind speed estimation have been investigated and the system sensitivity has been evaluated against: receiver altitude ; Transmitter elevation angle; Wind speed.

39. Experiments GNSS-R SIMULATOR Received waveform Wind speed Receiver altitude Elevation angle

40. Experiments GNSS-R SIMULATOR Received waveform Wind speed Receiver altitude Elevation angle

41. Experiments GNSS-R SIMULATOR Received waveform Wind speed Receiver altitude Elevation angle

42. Experiments GNSS-R SIMULATOR Received waveform Wind speed Receiver altitude Elevation angle

43. Experiments Signal-to-Noise-Ratio has been evaluated as: Where: Received power – bistatic link budget Thermal noise

44. Experiments The received triangular-shape waveform is wind dependent

45. Experiments H = 10 Km elevation angle = 45°

46. Experiments H = 10 Km elevation angle = 30°

47. Experiments H = 10 Km elevation angle = 60°

48. Experiments H = 1 Km elevation angle = 45°

49. Experiments H = 1 Km elevation angle = 30°

50. Experiments H = 1 Km elevation angle = 60°

51. Experiments H = 500 m elevation angle = 45°

52. Experiments H = 500 m elevation angle = 30°

53. Experiments H = 500 m elevation angle = 60°

54. OUTLINE Motivation Modeling Simulation Experiments Conclusions

55. In this study a different approach to deal with GNSS signals is proposed. GNSS-R can be seen as a bistatic radar system. Results show that GNSS signals can be succesfully exploited for remote sensing purposes. The SNR shows that different system configuration can be exploited but different receivers with different accuracy, i.e. cost, need to be employed.