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WO2013025639A1 - Polarisation de magnétomètre et détecteur d'anomalie - Google Patents

Polarisation de magnétomètre et détecteur d'anomalie Download PDF

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Publication number
WO2013025639A1
WO2013025639A1 PCT/US2012/050646 US2012050646W WO2013025639A1 WO 2013025639 A1 WO2013025639 A1 WO 2013025639A1 US 2012050646 W US2012050646 W US 2012050646W WO 2013025639 A1 WO2013025639 A1 WO 2013025639A1
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WIPO (PCT)
Prior art keywords
magnetometer
bias
running sums
calculating
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2012/050646
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English (en)
Inventor
William Kerry KEAL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
InvenSense Inc
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InvenSense Inc
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Filing date
Publication date
Priority claimed from US13/572,441 external-priority patent/US8577640B2/en
Application filed by InvenSense Inc filed Critical InvenSense Inc
Publication of WO2013025639A1 publication Critical patent/WO2013025639A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C17/00—Compasses; Devices for ascertaining true or magnetic north for navigation or surveying purposes
    • G01C17/38—Testing, calibrating, or compensating of compasses

Definitions

  • the present invention relates generally to devices that utilize magnetometers and more specifically to detecting magnetometer bias in such devices.
  • the present invention addresses such a need.
  • a computer implemented method, system or computer program product of estimating magnetometer bias and axis sensitivity for a device comprises collecting magnetometer data from the device; and calculating a center of a shape of the magnetometer data as a result of minimization.
  • the minimization of calculating the center of the shape further comprises calculating a plurality of running sums of the magnetometer data; storing the plurality of running sums; storing a count of the number of terms in each of the running sums; and calculating the center of the shape and setting the estimated magnetometer bias to the center of the shape.
  • the present invention provides a fast closed form method to determine magnetometer bias which is robust with noise measurements.
  • the present invention also provides methods to evaluate convergence of bias.
  • Figure 1 is a block diagram of a system that utilizes a magnetometer detection algorithm in accordance with an embodiment.
  • Figure 2 is a flow chart which illustrates generally detecting magnetometer bias in the device.
  • Figure 3 is an example of a location of raw magnetometer data.
  • Figure 4 is a flow chart for estimating a bias in accordance with an embodiment.
  • Figure 5 is an example of computing the center point of a sphere.
  • the present invention relates generally to devices that utilize magnetometers and more specifically to detecting magnetometer bias in such devices.
  • the following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements.
  • Various modifications to the preferred embodiment and the generic principles and features described herein will be readily apparent to those skilled in the art.
  • the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein.
  • the present invention is directed to determining the magnetometer bias to ensure the device is in its proper orientation.
  • FIG. 1 is a block diagram of a system 10 that utilizes a magnetometer estimator algorithm 100 in accordance with an embodiment.
  • the system 10 includes a device 1 1 .
  • the device 1 1 may include any type of mobile device including but not limited to, a cell phone, a tablet PC, or other type of portable electronic device.
  • the device 1 1 receives input data from a magnetometer 12.
  • the device 1 1 includes memory 18 for storing data.
  • Data 20 stores data from the
  • Memory 18 also includes a bias detector algorithm 100 in accordance with the present invention.
  • a processor 24 executes the algorithm 100 which operates on the data received from magnetometer 12.
  • the processor 24 provides the executed data to sensor fusion system 16.
  • the sensor fusion system 16 provides orientation information of the device.
  • Figure 2 is a flow chart which illustrates generally detecting magnetometer bias in the device.
  • first magnetometer data is collected, via step 102.
  • the center of an object is calculated as a result of minimization, via step 104.
  • the center of the object is then stored in the memory, via step 106.
  • the object could be a sphere or ellipsoid depending on the device movement.
  • a system and method in accordance with the present invention estimates the bias for a magnetometer sensor with three degrees of freedom.
  • a system and method in accordance with the present invention is utilized to recognize magnetic disturbances both before and after a bias has converged.
  • a system that utilizes a magnetometer bias estimation algorithm in accordance with the present invention can take the form of an entirely hardware
  • the magnetometer bias estimation algorithm is implemented in software, which includes, but is not limited to, application software, firmware, resident software, microcode, etc.
  • the magnetometer bias estimation procedure can take the form of a computer program product accessible from a computer-usable or computer- readable medium providing program code for use by or in connection with a computer or any instruction execution system.
  • a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium.
  • Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk, and an optical disk.
  • Current examples of optical disks include DVD, compact disk-read-only memory (CD-ROM), and compact disk - read/write (CD-R/W).
  • Figure 3 is an example of raw magnetometer data.
  • Figure 3 illustrates that as magnetometer data is being gathered, a spherical shape of the data is starting to form. What is desired is, which has been described before, is to determine the center of an object such as a sphere so that the magnetometer bias can be determined. To describe determining the center of the sphere of the
  • dot product value is it is the distance between the center of the sphere and the plane created by the midpoint of the two points and the normal vector created by the two points.
  • S' equation represents squaring the dot product for all combinations of points:
  • Equation (2) can be minimized by taking the partial derivatives and setting the partial derivatives to zero. Taking partial derivatives of S with respect to a, b, c and setting it to zero gives the set of equations (3):
  • Equation 8b With initialization shown in Equation 8b for the first sample
  • Equation 8a Another way to interpret Equation 8a is the normalized average of sensor data. If the absolute value of Q terms in Equation 8a are below a threshold, then the compass sample will be added to the overall solution in equations 5,
  • Equation 8c One implementation of the logic is shown below in Equation 8c.
  • Equation 8c Other deviations from Equation 8c that would work would be to allow 1 or more of the comparisons to pass the threshold and to use multiple thresholds. For instance, an acceptable method would be to have a lower threshold for at least 1 comparison to be true and a higher threshold all 3 comparisons to be true before allowing the update to equations 5,6,7,8.
  • Figure 4 is a flow chart for estimating a bias in accordance with an embodiment.
  • magnetometer data is collected, via step 202.
  • the center of a shape of the magnetometer data is then calculated as a result of minimization.
  • To provide the minimization of calculating the center of the shape further includes the steps described below.
  • First a plurality of running sums of the magnetometer data are calculated, via step 204.
  • the plurality of running sums comprise running sums of the magnetometer data, the square of the
  • the plurality of running sums is stored in memory, via step 206.
  • a count of the number of times that the plurality of running sums calculated is stored, via step 208.
  • the estimated magnetometer bias is calculated from the stored running sums when the count is less than a predetermined threshold, via step 210.
  • a center of the object is determined from the estimated magnetometer bias, via step 212.
  • Radius of Sphere It is also desirable to compute the radius of the sphere to ensure accuracy in the estimator of the bias. There are several methods to compute the radius of the sphere. If the error between the radius and the distance to the center is minimized, the results are as follows.
  • the magnetometer measurements cannot be relied on. If the magnetometer measurements cannot be trusted, then the magnetometer bias cannot be computed. In the next section, several metrics will be described that can be used together to determine how accurate the bias computation is. Any combination of the listed metrics could be used.
  • the basis for finding the magnetometer bias was to minimize the S term above and below. If the measured magnetometer data is not very spherical, the minimization will be larger. To find out how well the estimate has measured the center of the circle, the value can be computed which it originally tried to minimize. The equations can be reduced to single sums as was the case when computing the partial derivatives. Applying the double sum to single sum identity twenty-one times results in a single sum equation to compute S. Matlab code canto compute S is shown below. The Matlab code takes the magnetometer data (x,y,z) and the bias or center of the spherical fit (a,b,c), then returns S, the term that was minimized. The running sums are done at the beginning of the function, then S is computed. The Matlab program is for illustrative purposes only and could be implemented in a manner that would use running sums and not need a vector of data (x,y,z).
  • the S term can be normalized and used as a metric as to the quality of the center estimate by dividing by the number of pair combinations (N)(N-1 )/2 and then taking the square root
  • the normalized value represents the average distance between the planes created by the pairs of points and the center of the sphere.
  • the square root and multiplication by 2 can be avoided for CPU savings in many cases such as if it is used as a threshold.
  • the metric is useful to know if the data that is being taken matches a circle.
  • the noise of the magnetometer will affect the normalized value, so any threshold used will need to reflect that or be large enough for the worst case. Magnetic disturbances will also cause the normalized value to increase. Signals with a magnetic disturbance should not be used because these signals will lead to errors in the bias calculation.
  • the threshold will allow a metric to gauge whether the data collected was valid or disturbed with a magnetic disturbance. Once a set of data has been declared valid, each new sample can be evaluated to see how well it fits with the rest of the data. To perform this evaluation the previous value of S (before the normalization) is stored before the new sample and the value of S with the current sample. Then, divide by the number of points minus one and take the square root. Keeping the same units as before, would be compared to a threshold. Similarly, the square root could be eliminated since the value before normalization is being compared to a constant. Further optimizations include multiplying the threshold by the divisor in these equations as multiplication is typically easier than division.
  • the metric S can be used so that it can be determined if the values have converged. It is difficult to know what the sensor has experienced enough user motion to converge.
  • the functions used to solve a compass bias can be used to simulate in real time to determine how accurately the bias can be computed.
  • the compass measurements will be simulated by computing orientation with gyroscopes and/or accelerometers. A constant field with noise added and a bias added will be converted to a compass measurement. When some or all of the constraints used to judge a convergence are noticed, it will be noticed if the actual measurements have appeared to converge. If convergence has not taken place then it is assumed that the data was corrupted with a magnetic
  • the minimum and maximum of each axis could be stored along with its companion axes values. Then it can be determined that minimum and maximum values are far enough apart but not too far apart before calling the data converged.
  • the difference of the max and min should not exceed twice the radius plus error terms. If the difference is too large, the difference can be called a magnetic disturbance. The data can be thrown way and the process will be restarted. Because the bias can be solved at any point in time, it is possible to see how large an angle is made using these maximums and minimums.
  • One metric would be to take the dot product going from the center of the sphere to the maximum with the vector going from the center of the sphere to the minimum.
  • the dot product is negative, at least a 90 degree rotation along the perpendicular axis has been observed.
  • the sign of the dot product could be used for all axes, but in practice two out of three seems to work well enough and give enough observability to come up with a reasonable magnetometer bias. A smaller angle than 90 degrees could also be allowed using trigonometry.
  • Constant bias Another metric is used to determine there is a good estimate of the
  • magnetometer bias is if the magnetometer bias stays within a small range for a period of time. Bias changing
  • One problem is to address what to do if the magnetometer bias changes. For example, if the magnetometer was in a phone and someone attached a magnetic faceplate to the phone for decoration, the magnetometer bias would change. After the data has converged, the parameters are kept for a second set of data. Data is added to the solved for set, if is below a threshold (could remove the square root), then the data is added to the old data set. Data is always added to the new data set. After the new data set meets convergence as the first data set had to go through, it will be determined how many points were added to the old data set. If a small amount of points were added to the new data set and the bias has moved, the new data set will be used as the set to compute the bias, and the old data thrown. In either case, new magnetometer data will be collected into a new data set.
  • Another quality metric could be how far the data point is from the center of the circle. The data should be within the computed radius and errors. Solving for ellipsoids
  • each axis could be scaled by the required constant to make it a sphere.
  • the algorithm requires the sensitivity to be the same along each of the 3 axes. Instead of trying to solve for each sensitivity adjustment, the adjustment could just assume 1 for one axis and solve for the other two. The computation is simpler if the scale factor is applied to both the magnetometer data and also the bias solved for.
  • Equation 16 The derivates are computed for b, c, and sz as was the case for equation 1 6 above. Then, equation 16 will be set to zero and be reduced as with the sphere case. The square of the scale factors sx 2 and sz 2 will be solved for in equation 16.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measuring Magnetic Variables (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur, un système ou un produit programme d'ordinateur qui comporte la collecte de données de magnétomètre à partir du dispositif, et le calcul du centre d'une forme des données de magnétomètre à la suite d'une minimisation. La minimisation du calcul du centre de la forme comporte en outre le calcul d'une pluralité de sommes cumulatives des données de magnétomètre ; le stockage de la pluralité de sommes cumulatives ; le stockage du compte du nombre de termes dans chacune des sommes cumulatives, et le calcul du centre de la forme et le réglage de la polarisation de magnétomètre estimée au centre de la forme. Le rayon de la sphère est calculé pour assurer une précision de l'estimateur de la polarisation de magnétomètre.
PCT/US2012/050646 2011-08-17 2012-08-13 Polarisation de magnétomètre et détecteur d'anomalie Ceased WO2013025639A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201161524703P 2011-08-17 2011-08-17
US61/524,703 2011-08-17
US13/572,441 2012-08-10
US13/572,441 US8577640B2 (en) 2011-08-17 2012-08-10 Magnetometer bias and anomaly detector

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WO2013025639A1 true WO2013025639A1 (fr) 2013-02-21

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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9443446B2 (en) 2012-10-30 2016-09-13 Trulnject Medical Corp. System for cosmetic and therapeutic training
US9792836B2 (en) 2012-10-30 2017-10-17 Truinject Corp. Injection training apparatus using 3D position sensor
US9922578B2 (en) 2014-01-17 2018-03-20 Truinject Corp. Injection site training system
US10235904B2 (en) 2014-12-01 2019-03-19 Truinject Corp. Injection training tool emitting omnidirectional light
US10269266B2 (en) 2017-01-23 2019-04-23 Truinject Corp. Syringe dose and position measuring apparatus
US10290231B2 (en) 2014-03-13 2019-05-14 Truinject Corp. Automated detection of performance characteristics in an injection training system
US10500340B2 (en) 2015-10-20 2019-12-10 Truinject Corp. Injection system
US10648790B2 (en) 2016-03-02 2020-05-12 Truinject Corp. System for determining a three-dimensional position of a testing tool
US10650703B2 (en) 2017-01-10 2020-05-12 Truinject Corp. Suture technique training system
US10743942B2 (en) 2016-02-29 2020-08-18 Truinject Corp. Cosmetic and therapeutic injection safety systems, methods, and devices
US10849688B2 (en) 2016-03-02 2020-12-01 Truinject Corp. Sensory enhanced environments for injection aid and social training

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040236510A1 (en) * 2002-03-01 2004-11-25 Ockerse Harold C. Electronic compass system
US20070055468A1 (en) * 2005-09-02 2007-03-08 Nokia Corporation Calibration of 3D field sensors
US20090157341A1 (en) * 2007-04-17 2009-06-18 Schlumberger Technology Corporation Methods of Correcting Accelerometer and Magnetometer Measurements
US20100312509A1 (en) * 2009-06-05 2010-12-09 Apple Inc. Calibration techniques for an electronic compass in a portable device
US20110077889A1 (en) * 2009-09-28 2011-03-31 Teledyne Rd Instruments, Inc. System and method of magnetic compass calibration
US20110106477A1 (en) * 2009-11-04 2011-05-05 Qualcomm Incorporated Calibrating multi-dimensional sensor for offset, sensitivity, and non-orthogonality

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040236510A1 (en) * 2002-03-01 2004-11-25 Ockerse Harold C. Electronic compass system
US20070055468A1 (en) * 2005-09-02 2007-03-08 Nokia Corporation Calibration of 3D field sensors
US20090157341A1 (en) * 2007-04-17 2009-06-18 Schlumberger Technology Corporation Methods of Correcting Accelerometer and Magnetometer Measurements
US20100312509A1 (en) * 2009-06-05 2010-12-09 Apple Inc. Calibration techniques for an electronic compass in a portable device
US20110077889A1 (en) * 2009-09-28 2011-03-31 Teledyne Rd Instruments, Inc. System and method of magnetic compass calibration
US20110106477A1 (en) * 2009-11-04 2011-05-05 Qualcomm Incorporated Calibrating multi-dimensional sensor for offset, sensitivity, and non-orthogonality

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9792836B2 (en) 2012-10-30 2017-10-17 Truinject Corp. Injection training apparatus using 3D position sensor
US12456393B2 (en) 2012-10-30 2025-10-28 Truinject Corp. System for cosmetic and therapeutic training
US12217626B2 (en) 2012-10-30 2025-02-04 Truinject Corp. Injection training apparatus using 3D position sensor
US9443446B2 (en) 2012-10-30 2016-09-13 Trulnject Medical Corp. System for cosmetic and therapeutic training
US11854426B2 (en) 2012-10-30 2023-12-26 Truinject Corp. System for cosmetic and therapeutic training
US11403964B2 (en) 2012-10-30 2022-08-02 Truinject Corp. System for cosmetic and therapeutic training
US10643497B2 (en) 2012-10-30 2020-05-05 Truinject Corp. System for cosmetic and therapeutic training
US10902746B2 (en) 2012-10-30 2021-01-26 Truinject Corp. System for cosmetic and therapeutic training
US10896627B2 (en) 2014-01-17 2021-01-19 Truinjet Corp. Injection site training system
US9922578B2 (en) 2014-01-17 2018-03-20 Truinject Corp. Injection site training system
US10290231B2 (en) 2014-03-13 2019-05-14 Truinject Corp. Automated detection of performance characteristics in an injection training system
US10290232B2 (en) 2014-03-13 2019-05-14 Truinject Corp. Automated detection of performance characteristics in an injection training system
US10235904B2 (en) 2014-12-01 2019-03-19 Truinject Corp. Injection training tool emitting omnidirectional light
US10500340B2 (en) 2015-10-20 2019-12-10 Truinject Corp. Injection system
US12070581B2 (en) 2015-10-20 2024-08-27 Truinject Corp. Injection system
US10743942B2 (en) 2016-02-29 2020-08-18 Truinject Corp. Cosmetic and therapeutic injection safety systems, methods, and devices
US10849688B2 (en) 2016-03-02 2020-12-01 Truinject Corp. Sensory enhanced environments for injection aid and social training
US10648790B2 (en) 2016-03-02 2020-05-12 Truinject Corp. System for determining a three-dimensional position of a testing tool
US11730543B2 (en) 2016-03-02 2023-08-22 Truinject Corp. Sensory enhanced environments for injection aid and social training
US10650703B2 (en) 2017-01-10 2020-05-12 Truinject Corp. Suture technique training system
US11710424B2 (en) 2017-01-23 2023-07-25 Truinject Corp. Syringe dose and position measuring apparatus
US10269266B2 (en) 2017-01-23 2019-04-23 Truinject Corp. Syringe dose and position measuring apparatus
US12350472B2 (en) 2017-01-23 2025-07-08 Truinject Corp. Syringe dose and position measuring apparatus

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