GPS/INS Computing System

1. GPS/INS Computing System Performed by: Alexander Pavlov David Domb Supervisor: Mony Orbach Project Characterization Spring 2008/9

2. Agenda 1. General overview 2. Our Project 3. Working environment 4. Design Solution 5. Timeline GPS/INS Computing System

3. General overview “Even Noah got no salary for the first six months partly on account of the weather and partly because he was learning navigation.” Mark Twain GPS/INS Computing System

4. INS Characteristics Self-contained Provides accurate position and velocity over short time periods but slowly drifts over time More expensive and heavier than GPS GPS Characteristics Relies on GPS satellites: susceptible to jamming, RF interference, multipath and integrity problems Provides accurate position and velocity over longer time periods but has high frequency noise GPS/INS navigation Both Global Positioning System (GPS) and Inertial Navigation System (INS) has its advantages and disadvantages. By combining the outputs of a GPS and an INS, the performance issues of both systems can be remedied. GPS/INS Computing System

5. Tightly Coupled INS/GPS PI PINS/GPS IMU Strap down Inertial Navigation Algorithm State Update ∆Vm VI VINS/GPS qI qINS/GPS ∆θm SDINS P V q PSAT  Filter GPS Innovation Calculation  meas  . b GPS/INS Computing System

6. Kalman & Particle filter • Standard filter used in navigation systems is extended Kalman filter (EKF) • Disadvantages of the extended Kalman filter: • EKF is not an optimal estimator for non-linear systems • Optimized for statistical noise only • Particle filter can be used as an alternative to EKF, these to improve estimation's accuracy. • Particle filters is a sophisticated model estimation technique based on simulation using sufficient number of samples GPS/INS Computing System

7. Theoretical Solution • Implementing the tightly coupled INS/GPS navigation unit with the particle filter, according to algorithm developed in Technion. • The theoretical algorithm stages: GPS/INS Computing System

8. Project Goals GPS/INS Computing System

9. Our Project GPS Computing System

10. General Project will be performed in 2 stages. First part in this semester. Project will be performed by several work groups Our group will implement Particle Propagation and State Estimation stages in this first part. Both stages need to be performed each 0.01 sec, regardless of other stages performance. GPS Computing System

11. Group Project Goals – PART 1 GPS/INS Computing System

12. WorkingEnvironment GPS/INS Computing System

13. GidelPROCStar II • Up to 4 ALTERA Stratix II • 60 to 180 FPGAs • Five level memory structure • (over 2.5GB) • Typical system frequencies: • 100-300 Mhz. • Flexible clocking system. • Up to 695 available I/Os. • Up to 5 PSDBs (ProcStar II Daughter Boards):  Camera Links, machine I/Os and other interfaces. • Expandable system: up to 96 DDR II I/Os between   PROCStar II boards. • Up to 660 Gbits per second connectivity between FPGAs. GPS/INS Computing System

14. AlteraStratix II • 15,600 to 179,400 equivalent Logic elements • Adaptive logic module (ALM), maximizes performance and resource usage efficiency • Up to 9,383,040 RAM bits • High-speed DSP blocks provide dedicated implementation of multiply-accumulate functions. • Up to 12 PLLs (four enhanced • PLLs and eight fast PLLs) • per device. • Support for high-speed • external memory • Megafunctions support GPS/INS Computing System

15. AlteraQuartus II Provides a multiplatform design environment for all phases of FPGA design. GPS/INS Computing System

16. Design solutions GPS/INS Computing System

17. State Vector - X[1..18] GPS/INS Computing System

18. Design guidelines Constrains: • large amount of calculations • Limited hardware • real-time results Possible solutions: • Pipelining • Large amount of parallel calculation units Selected solution: • Max. Parallel processing • What can’t be parallel – will be Pipelined. GPS/INS Computing System

19. Solution – Top design xN Controller Weight vector Particles propagation unit State estimation unit Estimated State Vector [1..18] Extended State Vector [1..18] Extended State Vector [1..18] Extended State Vector [1..18] GPS/INS Computing System

20. Parallel VS. Pipeline • Considerations: • Max. parallel processes will result in Min. calculation time. • Number of parallel processes is limited by the hardware. • Not all calculations have to be parallel in order to comply with 100 Hz. GPS/INS Computing System

21. Parallel VS. Pipeline • Conclusions: • The number of L.E.’s (available on the FPGA) will determine the number of parallel processes in the “Particle propagation unit” and the “State estimation unit”. • To complete this number to N, we will pipeline the processes in those units. GPS/INS Computing System

22. Particles Propagation block GPS/NS Computing System

23. 1 Particle Propagation block GPS/INS Computing System

24. State estimation block GPS/INS Computing System

25. q estimation block – N units GPS/INS Computing System

26. Not q components estimation block GPS/INS Computing System

27. Growth capability • Each design unit, deals with a number of particles. • The basic calculations in each unit, are designed for one particle and is then multiplied. • The same design can be implemented with “bigger” FPGAs, by increasing the number of multiplications and parallel processes. • This can result in lesser pipelines which means faster realization. • It can also implement bigger N. GPS/INS Computing System

28. Timeline GPS/INS Computing System

29. GANTT – PART A GPS/INS Computing System