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AnNguyen_MSThesis

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AnNguyen_MSThesis

  1. 1. Master Thesis Star Tracker for Satellite – CubeSat platform Implemented by: Nguyen, Ngoc An Academic Supervisor: Prof. Dr-Ing. Frieder Keller Work place Supervisor: Prof. Dr. Hien Vo Work place: Universidad del Turabo – Puerto Rico Puerto Rico, May, 2016
  2. 2. Contents  Introduction  Existing Designs  Hardware Design  Algorithm Design  Implementing Algorithm on FPGA  Debug, Testing and Result
  3. 3. Introduction Cube Satellite (CubeSat) Dimension: 10 x 10 x 10 cm (1 litter) Mass: < 1.33kg Commercial off-the-shelf (COTS) components Electronics Devices CubeSat QB50P1 [http://www.amsat.org/?page_id=2944]
  4. 4. Introduction Star Tracker Image Sensor Star Detect & Centroid Calculation Star Identification Algorithm Attitude Determination Star Catalog
  5. 5. Existing Design Star Tracker Accuracy Update Rate Size Power Consumption ST-200 30” 1 Hz 50g 30x30x38 mm 220 mW NST 7” 5 Hz 500g 50x50x100 mm 500 mW STELLA ~ 2” 4 Hz 120g 60x46x58 mm 200 mW ST-16 7” 2 Hz 90g 59x56x32.5 mm 500 mW
  6. 6. Existing Design Star Tracker STELLA [University of Wuerzburg, Germany]
  7. 7. Existing Designs Star Tracker CubeStar [Stellenbosch University, South Africa]
  8. 8. System-level design Real time Star Detect Module FPGA Image Sensor External memory (Star Catalog) Integrated Processor core External Interfacing (e.g. SPI, I2C) Star Detection step is speed up by hardware module. Processor core is free from handling with large amount of image data.  Do not need to buffer image data before process. Image Sensor Star Detect & Centroid Calculation Star Identification Algorithm Attitude Determination Star Catalog Thesis works: Hardware Design  Design Real time Star Detect Module
  9. 9. Hardware Design Brightness of Stellar Object m1: apparent magnitude of star 1 m2: apparent magnitude of star 2 I1: illuminance of star 1 [W/cm2/s] I2: illuminance of star 2 [W/cm2/s] Star Vega is used as brightness reference  Magnitude of star Vega is 0
  10. 10. Hardware Design Effect of Magnitude cut-off Two Schemes to maintain Sky coverage: _ Small FOV, high magnitude cut-off  more accurate. _ Large FOV, low magnitude cut-off  smaller star catalogs / smaller lens size
  11. 11. Hardware Design Image Sensor MT9P031 (ST-16 star tracker) MT9M001 AR0130 VITA 1300 Python 0.5 Optical format 1/2.5" 1/2" 1/3" 1/2" 1/3.6" Resolution 2592 x 1944 = 5 MP 1280x1024 = 1.3 MP 1280x960 = 1.2 MP 1280 x 1024 = 1.3 MP 800 x 600 =0.5 MP Pixel size 2.2 um 5.2 um 3.75 um 4.8 um 4.8 um Sensitivity 1.4 V/lux.sec 4.8 V/lux.sec 6.5 V/lux.sec 4.6 V/lux.sec 7.7 V/lux.sec ADC Resolution 12-bit 10-bit 12-bit 10-bit 10-bit Dynamic Range 70.1 dB 68.2dB 82dB 60dB >60dB SNR 38.1 dB 45 dB 44 dB 41dB 40dB QE (@ 550nm) 55% 55% 77% 55% Power Consumption 381 mW 363 mW 270 mW 290mW 375 mW Operating temperature -30 to 70 0 to 70 -30 to 70 -40 to 85 -40 to 85 Candidate Image Sensors
  12. 12. Hardware Design Lens choosing f-number 2.0 1.4 2.8 Focal length 12mm 16mm 8mm Field-of-view Sensor AR0130 18.2 x 12.8 degree 12.78 x 9.0 degree 27 x 19.16 degree Magnitude cut-off (expected) 5.16 5.9 4.4 Number of Star in Catalog 1977 4531 821 Lens available on market Edmund Optics #66-893 Lensagon BHR16012S12 Edmund Optics #66-892
  13. 13. Hardware Design _ Processor: FPGA Cyclone IV EP4CE6 _ Image Sensor: AR0130 _ Lens: Edmund Optics #66-892 _ Resolution: 1024 x 720 _ Field-of-view: 27 x 19.16 degree Star Tracker Prototype (without baffle)
  14. 14. Hardware Design Baffle Design
  15. 15. Hardware Design Baffle Design Star Tracker <0.5U  Vane 0 Vane 1 Vane 2 Height [mm] 4.73 8.03 11.84 Distance from outermost vane [mm] 0 11.24 24.24 •Baffle radius r = 20 mm •Baffle length s = 35 mm
  16. 16. Star Detect and Centroid Calculation Star can be considered as a point-source light if the lens is right focus, star intensity occupy 1 pixel on image sensor.  Centroid Calculation cannot sub-pixel accuracy  Cannot separate star image and hot pixel. Solution: Slightly defocus the image  Star occupy 3x3 to 5x5 group of pixels. Star Centroid is calculated by intensity weighting 1 1 1 1 ( , )ROIend ROIend center x ROIstart y ROIstart x I x y x DN           1 1 1 1 ( , )ROIend ROIend center x ROIstart y ROIstart y I x y y DN           1 1 1 1 ( , ) ROIend ROIend x ROIstart y ROIstart DN I x y          ROI (Region of Interest)
  17. 17. Star Detect and Centroid Calculation Star valid conditions (method 2): _ Median value of 3x3 window > threshold value Star valid conditions (method 1): _ Center pixel > threshold value 1 _ Sum of 3x3 window > threshold value 2 Additional condition: Center pixel = max of the window  Avoid re-detect the same star spot
  18. 18. Noise in AR0130 sensor ( , ) ( , ) ( )read signal background colI x y I x y I x   Vertical strip noise is dominant
  19. 19. Noise Correction 720 1024 8 I(1,1) I(1,2) I(1,3) … I(1,1024 ) I(2,1) I(2,2) I(2,3) … I(2,1024 ) … … … … … I(8,1) I(8,2) I(8,3) … I(8,1024 ) Mean Column 1 Mean Column 2 Mean Column 3 … Mean Column 1024 Every pixel will be subtracted by the mean value of its column Noise-correction
  20. 20. Star Detect & Centroid Calculation Block
  21. 21. Row Buffer (Internal RAM FIFO) Row Buffer (Internal RAM FIFO) Row Buffer (Internal RAM FIFO) Row Buffer (Internal RAM FIFO) Data Pix_clk FV LV Pixel Counter x_pixel y_pixel Centroiding Block : D flip-flop Note: Condition Checking Block Window data en_tick x_centroid y_centroid
  22. 22. Centroiding Block
  23. 23. Condition Checking Block
  24. 24. Testing
  25. 25. Testing Accuracy of centroiding is evaluated by inter-star angular distance With distortion correction the averaged error of inter-star distance is 0.0536 degrees (~ 3’12.96”)  Dimmest star can be detected is σ Orionius (Magnitude 4.0) Testing scene
  26. 26. Conclusion Current achievement: • Build Real time Star Detect Module. • Build de-noise Pre-processing Module • Can detect star with magnitude up to 4.0 Future Works: • Integrated hardware modules with a processor core into one FPGA chip. • Implement Star Identification algorithm and Attitude Calculation on processor core. • Build Lab Test bench. • Testing whole system.

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