1. INS : State of the art Yves PATUREL

2. 2 INS : noise on the sensors For inertial sensors, one typical way of measuring noise is the draw the Allan variance :  Log sensor data at high frequency for a long period of time  Average data over 2 samples, compute standard deviation of all averages  Average data over 10 samples, compute standard deviation of all averages  Average data over 50 samples, compute standard deviation of all averages  …  Draw on a log-log scale, the standard deviation versus averaging time

3. 3 Allan variance

4. 4 Examples of Allan variance measured on different gyro technologies

5. 5

6. 6 Allan variance  Quantification  Angular random walk :  Averaging or integrating time aslong as it is smaller than the time at which flicker noise is reached will improve bias measurement  The smallest the noise, theshortest is the time to reach some bias measurement level  Flicker noise  Provide measurement of bias instability and time over which instability occurs

7. 7 Position accuracy along trajectory  Position aiding with GNSS for airborne applications  Case of large bank angles  Case of satellite masking

8. 8 Position aiding over trajectory  For applications that require the best positioning performance, PPK GPS is today the most accurate way for INS aiding :  Accuracy is unrivalled (a few centimeters)  But GNSNS require that at least satellites are visible  For airborne applications, this is the case most of the time except when turning with large bank angles or when horizon masking occurs :

9. 9  High grade IMU, so that accuracy is maintained during this occasions, but the accuracy cannot be maintained for long period of time  Tight coupling between INS and GNSS :  GNSS accuracy is lost « later », because PPK accuracy can be maintained longer while IMU provides aiding data to GNSS : fixed ambiguity solution is maintained longer with less visible satellites,  Better GNSS solution monitoring when less satellite redundancy  This is even improved when this tight coupling is done in forward and backward (post processing)  Obviously, the best is when you can combine both high grade IMU and tight coupling

10. 10 Point-of-interests: “La grille royale”: confidence intervals Higher fix availability

11. 11 Point-of-interests: “Rue Montardat” Higher precision

12. 12 Optimal use of flight time : alignment time  Alignment time includes attitude and heading alignment :  Attitude is quite fast to align, providing that accelerometer biases are stable enough over time  Heading alignment is longer:  Heading error estimation requires changes of velocity vector  Heading error must be stable, therefore should not increase with time, due to gyro drift (gyro bias)  Converging time is shorter when heading is already quite accurately computing wit gyrocompassing

13. 13 Long legs trajectory  Heading errors cannot be estimated during long straight legs :

14. 14 Long legs trajectory  Heading error will grow at the speed of the uncompensated gyro bias :  Bias must be well estimated  Bias must be stable (back to the Allan variance curve).

15. 15 Long legs trajectory  Velocity does not change, so acceleration is zero  Accelerometers measurements are zero  When you integrate zeo, orientation of zero vector does not matter !!  In order to make heading error observable, it is necessary to have non zero acceleration :  Increase velocity,  Reduce velocity  Turn

16. 16 End of alignment Start

17. 17 Attitude accuracy along trajectory  On long straight legs, roll error and accelerometer bias cannot be separated  Accelerometer bias must be as stable as possible  Roll error must be as stable as possible (no gyro drift)

18. 18 How to mount IMU and rest of system ?  IMU and other sensors must be mounted as rigidly as possible one wrt the other, so that IMU measures exactly the orientation of the sensors  They must be close together, so that there is no torsion between IMU and sensors  It is better when there is no vibration isolators “between IMU and sensors

19. 19 How to mount IMU and rest of system ?  Why isolators ?  Some inertial sensors are sensitive to vibration : their characteristics may vary with vibrations  This is the case with most of accelerometers (levels of sensitivity may vary from one technology to the other)  Very often isolators are included inside the IMU box and cannot be removed

20. 20 IMU SENSORS IMU SENSORS IMU SENSORS

21. 21 ATLANS - C  Innovative coupling bringing to our customers the best of two companies, iXBlue – expert in FOG inertial products & Septentrio – expert in GNSS  No ITAR component inside  2 OEM in 1  All-in-one, plug & play  Easy to integrate (low weight, low volume)  IMU class : gyro 0.1 deg/hour, accelerometer 1 mg

22. 22 AIRINS  Compatible withy any GNSS receiver  No ITAR component inside  plug & play  Easy to integrate (low weight, low volume)  IMU class : gyro 0.01 deg/hour, accelerometer 100 µg

23. 23 FUTURE

24. 24 Future vision : Gyros Bias error Bias stability 100°/h 5°/h 10°/h 0,5°/h 1°/h 0,05°/h 0,1°/h 0,005°/h 0,01°/h <0,005°/h 0,001°/h <1 E -4°/h <0,0001°/h <1 E -5°/h 2013 MEMS Si Prototypes à qqs °/h FOG Prototypes Open loop FOG MEMS Quartz RLG HRG * Hors technologie mécanique toupie Bias error Bias stability 100°/h 5°/h 10°/h 0,5°/h 1°/h 0,05°/h 0,1°/h 0,005°/h 0,01°/h <0,005°/h 0,001°/h <1 E -4°/h <0,0001°/h <1 E -5°/h +10 ans MEMS SiFOG Mini FOG MEMS Quartz RLG « MEMS » Quartz?

25. 25 Future vision Accelerometers Bias error 100 miliG10 milliG 1 milliG 100 microG 10 microG1 microG 2013 MEMS Si Prototypes Pendulaire « QA3000 » PIGA Pendulaire « QA700 » Quartz vibrant Bias error 100 miliG10 milliG 1 milliG 100 microG 10 microG1 microG + 10 ans MEMS Si ?PIGA Pendulaire « QA3000 » Prototype atomique ? Pendulaire « QA700 » Quartz vibrant?