TIMING CONSIDERATIONS FOR ADVANCED SIMULATIONS

1. TIMING CONSIDERATIONS FOR ADVANCED SIMULATIONS Mark Holbrow

2. Presentation Purpose • This presentation aims to cover the following topics: • Trajectory simulation fundamentals with emphasis on remote motion • Learn how sampling remote motion data at different rates impacts simulated position accuracy • Performing hardware-in-the-loop (HWIL) testing with 250Hz data • Find out how late or missing motion data affects simulation results • How to choose appropriate update rates for simulations

3. Trajectory Basics • Vehicle position prediction • Translation to SV pseudorange profile • Delivery and realisation in the hardware • Presentation Notes: • From a hardware respect we are solely concentrating on the GSS8000 series • Only vehicle motion effects on pseudorange are considered • Aspects re. signal level and antenna pattern arrival angles are not covered

4. Vehicle Trajectory Prediction • Fundamental to Spirent’s methodology for simulating vehicle motion is prediction • SimGEN predicts a vehicles positional change over a finite time step through propagating the most recently sampled motion data record (MOT or MOTB). That time step is controlled by the Simulation Iteration Rate (SIR) • The GSS8000 supports the following SIRs • 4 msec • 10 msec • 100 msec

5. Vehicle Trajectory Prediction - 1 • As we will see Simulation Iteration Rate (SIR) does have an effect on performance but from the outset it is worth noting that faster is not always better • In fact when using internally generated motion models there is potentially a downside in that SimGEN’s processing burden is much higher than it need be, without realising any performance improvements

6. Vehicle Trajectory Prediction – 2 • Let’s consider an SIR of 10 msec • Also, all remote data is available ahead of time Sample Sample Sample Sample T=10ms T=20ms T=40ms T=0ms T=30ms Process data for T=30 data Process data for T=40 Process data for T=20 data T=40 data applied over this period T=30 data applied over this period T=20 data applied over this period T=10 data applied over this period Process data for T=10 Remote motion for T=10 in buffer Remote motion for T=30 in buffer Remote motion for T=40 in buffer Remote motion for T=20 in buffer

7. Vehicle Trajectory Prediction – 3 • One item to focus on straight away is that the ‘quality’ of the incoming motion records is fundamental to SimGEN smoothly and accurately synthesising the incoming trajectory, i.e. if the motion derivatives at each epoch do not correctly propagate the position specified at the next epochthen there will be a positional error introduced • SimGEN will work around this problem because it knows where it propagated too and now, where the incoming data says it wants to be, but in having to make that adjustment it will have to add an artefact into the trajectory over the next time step

8. Vehicle Trajectory Prediction – 4 • What happens if the data isn’t available ahead of time? Sample Sample Sample Sample T=10ms T=20ms T=40ms T=0ms T=30ms Process data for T=30 from T=20 data Process data for T=40 Extrapolate data for T=20 from T=10 data T=40 data applied over this period Extrapolated T=30 data applied over this period Extrapolated T=20 data applied over this period T=10 data applied over this period Process data for T=10 Remote motion for T=10 in buffer Remote motion for T=20 arrives late Remote motion for T=30 arrives late Remote motion for T=40 arrives on time

9. Vehicle Trajectory Prediction - 5 • It is worth noting that the predictive method helps to iron out synchronisation and timing issues between the remote source and SimGEN, in fact SimGEN will happily work with asynchronous data records as it will just continue to predict from its last sampled record and then correct (if necessary) over a number of time steps, once a more recent record is available • Recovery from missed or late records can be further improved by increasing the sample rate of the incoming datastream. 500Hz data can be accepted using TCPIP as the remote transfer mechanism

10. Vehicle Trajectory Prediction – 6 SimGEN Trajectory / Control DataSimREMOTE External System RF Generator Position Solution Data RF GNSS Receiver

11. Vehicle Trajectory Prediction - 7 • In the previous examples, data records were available to sample ahead of time. In real-time HWIL systems this will not be the case and data samples will effectively be sampled in SimGEN after their time of applicability has passed • Assuming the incoming data is self consistent and predictable from epoch to epoch, SimGEN makes an extremely good job of predicting forward, hence effectively eliminating the intuitive lag (latency) you might expect • Only under very extreme, unexpected dynamics, will this lag be seen, and even then, only for short periods. Later in the presentation we will graphically see that effect, together with how it is minimised through selection of SIR

12. Translation Into SV pseudo-range • At this point only half the job is done, we now need to translate that change in vehicle position to the corresponding change in pseudorange - on an SV by SV basis – over that time step • In doing this SimGEN must also describe the pseudorange change profile, i.e. how the range changes over that period. This is achieved through pseudorange derivatives. These derivatives are then sent to the hardware, where the pseudorange profile is reconstructed • Dependent upon the selected iteration rate SimGEN delivers the data as either: • 100msecs SIR - coefficients of cubic-spline expression • 10msecs SIR - PR acceleration change (every 10msecs), together with absolute PR change every 100msecs • 4msecs SIR - PR acceleration change (every 4msecs), together with absolute PR change every 100msecs

13. Hardware Realisation • The hardware realises this pseudorange change profile as a series of finite pseudorange rate (doppler) steps. These steps are applied at the hardware update rate (HUR) • Two different HURs are supported, 4msecs and 1msecs • Whether the pseudorange change profile is described as a cubic spline expression, or as series of PR accelerations, ultimately the hardware has to fit to it a ‘stair case’ of doppler steps

14. Rate of change of PR rate profile Increasing rate of change of pseudorange (Increasing Doppler) Doppler stair case, finite values of doppler applied at the hardware update rate Time Hardware update intervals Hardware Realisation-1 • Graphically this is what is happening in the hardware

15. Pseudorange Time Hardware Realisation - 2 • Which ultimately translates to the required pseudorange profile.

16. Trajectory capture comparisons at different SIRs

17. Pseudorange Error Estimates - Smooth motion dynamics

18. Pseudorange Error Estimates - Random jerky motion dynamics

19. Pseudorange Error Estimates - Random jerky motion dynamics

20. Pseudorange Error Estimates - Random jerky motion dynamics

21. Pseudorange Error Estimates - Random jerky motion dynamics

22. Effects of missing data • The impact of missing data is dependant upon several parameters: • How much is missing? • What is the trajectory like? • What SIR is being used? • Depending upon the answer to the above, it is possible that missing data will have a zero or at worst a very small effect

23. Visualising the performance of predictive technique • Aim of this section is to demonstrate how the effect of SIR, unexpected extreme motion and data arriving late can be seen on the RF signal. • A scenario is constructed with a single Geo SV at the zenith of the simulated position. • The PRN code and navigation message are switched off . • The measurement is carried out with the L1 carrier alone on a spectrum analyser centred at L1 with zero span. • A MOT motion file with the following characteristics is used: • At 6 seconds elapsed time, from rest, perform step change with maximum dynamics for 2 seconds directly toward SV, hold for 2 seconds then perform inverse manoeuvre • The experiment repeats with different SIRs with remote data on time and data late

24. Data ahead of time GSS8000 SimGEN PC Ethernet Timer Card 1msec tick Motion file Range (m) 12 6 8 10 Time (s) Spectrum Analyser 10MHz

25. L1 Carrier Carrier Signal Power Res BW Pass Band Use of zero span on a spectrum analyser Range (m) 6 8 10 12 Time (s) 6 8 10 12 Time (s) Velocity Increases Velocity Decreases Range rate (m/s) Time (s) Noise Floor

26. Green 100ms Red 10ms Blue 4ms 100ms 10ms 4ms Latency measurement

27. Data sent from remote PC GSS8000 Ethernet SimGEN PC Timer Card 1msec tick Ethernet Spectrum Analyser SimREMOTE PC Timer Card Range (m) Motion file 12 6 8 10 Time (s) 10MHz

28. 100ms 10ms 4ms Latency Measurement: 1KHz Motion File in SimREMOTE PC, Jerk, Acc, Vel & Position Data on time

29. Data Arrived Late Reference Latency Measurement: 1KHz Motion File in SimREMOTE PC, At 100ms SIR, Data arrive 100ms Late

30. Data Arrived Late Reference Latency Measurement: 1KHz Motion File in SimREMOTE PC, At 10ms SIR, Data arrive 10ms Late

31. Data Arrived Late Reference Latency Measurement: 1KHz Motion File in SimREMOTE PC, At 4ms SIR, Data arrive 4ms Late

32. Latency Measurement: 1KHz Motion File in SimREMOTE PC, Vel & Position Data on time Motion Started 6s

33. The Check Motion Utility • To help alleviate some of the issues with noisy, missing and consistent data, Spirent have been developing a Motion Check utility • This utility allows the user to enter either NMEA or umt data and effectively smoothes out, interpolates and generally improves the consistency of the data records • Individual trajectory parameter thresholds are controlled by the user

34. The Check Motion Utility

35. Any Questions? Thank you for your attention and time

36. Background • Step change at 5seconds 4msec SIR

37. Background • Step change at 5seconds 100msec SIR

38. Background • Handling missing data between 4.9 and 5.1 secs

39. Background • Handling missing data between 4.9 and 5.1 secs

40. Background • Missing data between 1 and 4 secs