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INS : State of the art Yves PATUREL

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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

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3 Allan variance

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4 Examples of Allan variance measured on different gyro technologies

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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

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

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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 :

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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

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

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

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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

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

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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).

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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

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16 End of alignment Start

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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)

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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

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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

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20 IMU SENSORS IMU SENSORS IMU SENSORS

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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

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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

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23 FUTURE

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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?

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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?

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