TIGHTLY-COUPLED GNSS/IMU INTEGRATION FILTER HAVING SPEED SCALE-FACTOR AND HEADING BIAS CALIBRATION
First Claim
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1. An apparatus comprising:
- an integration filter for a sensor-assisted global navigation satellite system (GNSS) receivera GNSS measurement engine for providing GNSS measurement data to said integration filter;
an inertial measurement unit (IMU); and
an inertial navigation system (INS) block for calculating INS navigation information using a plurality of inertial sensor outputs,wherein said integration filter performs a blending operation for combining said GNSS measurement data and said INS navigation information, and for estimating and compensating a speed scale-factor and a heading bias in the INS measurement.
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Abstract
Embodiments of the invention provide a tightly-coupled integration filter for inertial sensor-assisted GNSS (global navigation satellite system) receiver. The inertial measurement unit (IMU) contains inertial sensors such as accelerometer, magnetometer, and/or gyroscopes. Embodiments include blending filter based on extended Kalman filter (EKF), which optimally integrates the IMU navigation data with all other satellite measurements (tightly-coupled integration filter). The proposed blending filter includes two states for estimating/compensating the speed scale-factor and the heading bias in the INS measurement.
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Citations
23 Claims
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1. An apparatus comprising:
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an integration filter for a sensor-assisted global navigation satellite system (GNSS) receiver a GNSS measurement engine for providing GNSS measurement data to said integration filter; an inertial measurement unit (IMU); and an inertial navigation system (INS) block for calculating INS navigation information using a plurality of inertial sensor outputs, wherein said integration filter performs a blending operation for combining said GNSS measurement data and said INS navigation information, and for estimating and compensating a speed scale-factor and a heading bias in the INS measurement. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method of calibration comprising:
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creating a coordinate transformation matrix using a latest position fix (latitude and longitude); transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix; including a plurality of INS measurements in said local navigation coordinate in a measurement equation in a way that said INS measurements are a function of a plurality of velocity variables and speed scale-factor and/or heading bias variables of an integration filter state with a plurality of measurement noises; estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter. outputting a blended calibrated position fix. - View Dependent Claims (16, 17, 18, 19, 20)
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21. A method of calibration comprising:
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creating a coordinate transformation matrix using a latest position fix (latitude and longitude); transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix; including a heading measurement using an angle of said INS velocity vector on horizontal navigation plane; estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter. outputting a blended calibrated position fix.
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22. A method of calibration comprising:
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creating a coordinate transformation matrix using a latest position fix (latitude and longitude); transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix; including a speed measurement using a magnitude of an INS velocity vector on horizontal navigation plane; estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter. outputting a blended calibrated position fix. - View Dependent Claims (23)
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Specification