A Global System for Detecting Dangerous Seas Using GNSS Bi-static Radar Technology

Transcription

1 A Global System for Detecting Dangerous Seas Using GNSS Bi-static Radar Technology Scott Gleason, Ka Bian, Alex da Silva Curiel Stephen Mackin and Martin Sweeting 20 th AIAA/USU Smallsat Conference, Logan, UT

2 Presentation Overview Dangers presented by the World s oceans Sea sensing using GNSS bi-static radar Sea roughness sensing Wind sensing Flight experiment Results Sea surface sensing Ice sensing Land cover sensing Potential exploitation

3 Dangers Caused by the World s Oceans

4 Source: LMIU for Joint Hull Committee Total Losses By Cause, All Vessel Types Vessels > 500Grt Weather Grounding Fire/explosion Collision/contact Hull Machinery Other 0% 5% 10% 15% 20% 25% 30% Frequency (%age of overall total losses)

5 The Need for Sensing Dangerous Seas 2 Ships of over 500 tonnes sink every week More than 1,000 people die at sea each year Correlation Peak 1 Second Averaging Insurance companies pay out more than $2 Billion (US) in claims each year in weather related losses Sea conditions have been a significant contributing factor to numerous accidents October 2002 Joola capsizing, resulting in 1800 lives lost September 1998, The Princess of the Orient sinks due in large part to sea conditions. No Signal Capsized passenger Ferry Joola off the West Coast of Africa

6 Sensing Dangerous Seas

7 GNSS signals reflected off the ocean rich in information Wind speed, Wind direction, Wave height, Sea height Etc. GNSS REFLECTOMETRY / BISTATIC RADAR GNSS Signals Numerous studies conducted on the ground and air ESA, Colorado, NASA, JPL, SSTL Passive instrument (brings down cost significantly). Possible Tracking of multiple reflections (good coverage). GNSS Reflections Iso range and Doppler ellipses

8 UK-DMC Satellite and Bistatic GPS Experiment SSTL and BNSC funding UK-DMC satellite Launched Sept km altitude sunsynchronous orbit Reflectometry Antenna Imager During pre-launch thermal vacuum testing, with NigeriaSat-1 Earth facing facet, with LHCP, 12 dbic gain antenna

9 Payload SSTL SGR10/20 GPS receiver One high-gain antenna Left Hand Circular polarisation Specially built for the purpose dbi needed Compensates for reflected signal loss One regular antenna (minimum) To receive the direct GPS signal Placed to have GPS satellite in Fieldof-regard LNA LNA SGR RF Front- End 1,2,3 SGR RF Front-End 4 Sign Mag 5.71 MHz SGR Digital Real Time Processing Section Solid Solid State State Data Recorder Data Recorder & OBC and OBC CISCO Router Solid State Data Recorder and OBC PVT & Processed Data from RF2 CAN Bus High speed bus Sampled data Downlink Additional sampling On-Board computer stores data A few seconds of data sampled Currently about one minute maximum

10 Ocean Roughness Sensing

11 Transmit power & gain Received power Calculation of Normalised Radar Cross Section (sigma0) σ0 represents scattering gain towards the receiver in bistatic radar equation: Receive gain P R = P G 2 T Txλ σ 0GRx 4 Rx Tx ( π ) R R Paths before and after reflection Equation expanded by Zavorotney and Voronovich to include GPS code and Doppler terms. Re-arranging gives us our estimated value: ˆ σ ( 4π ) 3 Ti PR 0 = 2 P Tλ A 1 G G Tx Rx 2 RRx Λ R 2 S 2 Tx 2 da 1 Code and Doppler terms Surface of integration A, selected as first GPS code chip isorange ellipse on the oceans surface

12 Three Examples Under Different Ocean Conditions Correlation Peak 1 Second Averaging No Signal March 23 rd 2004 Winds ~ 2 m/s March 4 th 2005 Winds 7 m/s September 3rd 2004 Winds 10 m/s

13 November 16 th 2004 Model Waveform Signal in Raw Data

14 Ice and land sensing

15 Signals Detected Off Sea Ice Very strong coherent signals from sea ice Signal power variation with ice thickness UK-DMC Specular Reflection Point 9/10ths ice total concentration, first year thin ice 30-70cm thick

16 Height profile Signal

17 Height profile Signal

18 Height profile Signal

19 Discussion of Land Reflections Signals where easily detectable in all cases, often with only minimum averaging times. The terrain roughness significantly effected the signal magnitude. Flat terrain with sparse vegetation coverage resulted in the strongest signals. Irrespective of surface moisture. The crossing of the Missouri river is clearly identifiable. With proper calibration it may be possible to sense, Surface water or moisture. Terrain types, possibly including human developments. The achievable surface measurement resolution will be critical. Detailed validation work may start next year

20 Measurement Coverage

21 Medium/High Wave Conditions Instantaneous example of reflection opportunities No Signal

22 72 Hour Coverage for a Single Satellite Coverage after 72 hours Denser at higher latitudes Can also add SBAS signals: E.g. EGNOS, WAAS, MSAS Geostationary Adds more equatorial coverage Galileo Glonass

23 Coverage No Signal

24

25 Example of Tracks over One Orbit

26 Summary Earth reflected GPS signals can be easily detected in low Earth orbit from a wide range of surfaces with a medium gain antenna. When reflected from the ocean these signals have been linked to, The surface winds under well developed seas. The surface waves under all conditions. (BRCS and Doppler). GNSS Earth reflected signals are a very promising remote sensing tool for a range of applications. Sensing surface waves and roughness. Sensing surface wave direction. Sensing surface height, tsunamis. Need stronger signals and very accurate models. Sensing sea ice and surface soil moisture. A promising remote sensing technique for small satellite missions

27 Thank You!