PATENT DOCUMENT

Publication Number: US-9151610-B2
Application Number: US-201313913420-A
Country: US
Kind Code: B2

Title: Validating calibrated magnetometer data

Abstract:
Implementations are disclosed for validating data retrieved from a calibration database. In some implementations, calibrated magnetometer data for a magnetometer of a mobile device is retrieved from a calibration database and validated by data from another positioning system, such as course or heading data provided by a satellite-based positioning system. In some implementations, one or more context keys are used to retrieve magnetometer calibration data from a calibration database that is valid for a particular context of the mobile device, such as when the mobile device is mounted in a vehicle. In some implementations, currently retrieved calibration data is compared with previously retrieved calibration data to determine if the currently retrieved calibration data is valid.

Claims:
What is claimed is:  
     
       1. A method comprising:
 receiving a reading from a magnetometer of a mobile device; 
 selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold; 
 estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; 
 determining a first heading for the mobile device using the estimated external field; 
 comparing the first heading with a second heading obtained from data provided by a location processor of the mobile device; and 
 validating the first heading in response to the comparing, 
 
       where the method is performed by one or more hardware processors. 
     
     
       2. The method of  claim 1 , further comprising:
 determining the estimated external field does not match the magnitude range of the cluster; 
 iteratively estimating an external field using representative bias offsets for different clusters in the plurality of clusters; and 
 for each iteration, determining whether the estimated external field matches a magnitude range for a cluster selected during that iteration. 
 
     
     
       3. The method of  claim 1 , further comprising:
 receiving calibrated magnetometer data including a bias offset; 
 comparing the bias offset to the plurality of clusters; 
 determining the bias offset does not match any cluster in the plurality of clusters; and 
 identifying the bias offset as novel. 
 
     
     
       4. The method of  claim 3 , further comprising:
 determining that a number of novel bias offsets exceeds a specified number; and 
 automatically applying a clustering technique to historical magnetometer data to generate new clusters of bias offsets. 
 
     
     
       5. The method of  claim 1 , wherein the plurality of clusters are formed using a quality threshold clustering. 
     
     
       6. The method of  claim 1 , wherein the external magnetic field is estimated by subtracting the representative bias offset for the selected cluster from the readings. 
     
     
       7. The method of  claim 1 , wherein the magnitude threshold is based on a standard deviation of magnitudes in the selected cluster. 
     
     
       8. The method of  claim 1 , where the location processor is a satellite positioning system. 
     
     
       9. A method comprising:
 receiving a reading from a magnetometer of a mobile device; 
 selecting, at a first time, a first cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected first cluster has a first representative bias offset, a first mean of magnitudes in the selected first cluster, and a first magnitude threshold; 
 selecting, at a second time, a second cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected second cluster has a second representative bias offset, a second mean of magnitudes in the selected second cluster, and a second magnitude threshold; 
 comparing the first and second representative bias offset; 
 validating the second representative bias offset based on the comparing; 
 estimating an external magnetic field based on the reading and the second representative bias offset for the selected second cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the second mean magnitude and the second mean magnitude plus the second magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and 
 determining a heading for the mobile device using the estimated external field, where the method is performed by one or more hardware processors. 
 
     
     
       10. A computer-implemented method, comprising:
 receiving a reading from a magnetometer of a mobile device; 
 selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold, where the selecting uses a context key; 
 estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and 
 determining a first heading for the mobile device using the estimated external field, 
 
       where the method is performed by one or more hardware processors. 
     
     
       11. The method of  claim 10 , where the context key indicates that the mobile device was mounted in a vehicle at the time of the previously calibrated readings. 
     
     
       12. A system comprising:
 one or more processors; 
 memory coupled to the one or more processors and configured for storing instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising: 
 receiving a reading from a magnetometer of a mobile device; 
 selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold; 
 estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; 
 determining a first heading for the mobile device using the estimated external field; 
 comparing the first heading with a second heading obtained from data provided by a location processor of the mobile device; and 
 validating the first heading in response to the comparing. 
 
     
     
       13. The system of  claim 12 , further comprising:
 determining the estimated external field does not match the magnitude range of the cluster; 
 iteratively estimating an external field using representative bias offsets for different clusters in the plurality of clusters; and 
 for each iteration, determining whether the estimated external field matches a magnitude range for a cluster selected during that iteration. 
 
     
     
       14. The method of  claim 12 , further comprising:
 receiving calibrated magnetometer data including a bias offset; 
 comparing the bias offset to the plurality of clusters; 
 determining the bias offset does not match any cluster in the plurality of clusters; and 
 identifying the bias offset as novel. 
 
     
     
       15. The system of  claim 14 , further comprising:
 determining that a number of novel bias offsets exceeds a specified number; and 
 automatically applying a clustering technique to historical magnetometer data to generate new clusters of bias offsets. 
 
     
     
       16. The system of  claim 12 , wherein the plurality of clusters are formed using a quality threshold clustering. 
     
     
       17. The system of  claim 12 , wherein the external magnetic field is estimated by subtracting the representative bias offset for the selected cluster from the readings. 
     
     
       18. The system of  claim 12 , wherein the magnitude threshold is based on a standard deviation of magnitudes in the selected cluster. 
     
     
       19. The system of  claim 12 , where the location processor is a satellite positioning system. 
     
     
       20. A system comprising:
 one or more processors; 
 memory coupled to the one or more processors and configured for storing instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising: 
 receiving a reading from a magnetometer of a mobile device; 
 selecting, at a first time, a first cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected first cluster has a first representative bias offset, a first mean of magnitudes in the selected first cluster, and a first magnitude threshold; 
 selecting, at a second time, a second cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected second cluster has a second representative bias offset, a second mean of magnitudes in the selected second cluster, and a second magnitude threshold; 
 comparing the first and second representative bias offset; 
 validating the second representative bias offset based on the comparing; 
 estimating an external magnetic field based on the reading and the second representative bias offset for the selected second cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the second mean magnitude and the second mean magnitude plus the second magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and 
 determining a heading for the mobile device using the estimated external field. 
 
     
     
       21. A system comprising:
 one or more processors; 
 memory coupled to the one or more processors and configured for storing instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising: 
 receiving a reading from a magnetometer of a mobile device; 
 selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold, where the selecting uses a context key; 
 estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; 
 determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; 
 determining a gravitational vector; 
 determining an inclination angle between the gravitational vector and the estimated magnetic field; 
 determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; 
 determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and 
 determining a first heading for the mobile device using the estimated external field.

Description:
TECHNICAL FIELD 
     This disclosure relates generally to magnetometer bias estimation. 
     BACKGROUND 
     A mobile device (e.g., smart phone, navigational device, notebook computer, electronic tablet, wearable computer) can be equipped with a magnetometer. Magnetic readings from the magnetometer can be used to provide a user with a direction, which may be a “heading” (typically given relative to the Earth&#39;s true North), and/or an arrow pointing to true North. The direction information may be provided for the user&#39;s own navigation knowledge, for example, to tell the user which way is north while the user is walking or driving in unfamiliar surroundings. The direction information can also be used by a navigation or map application that may be running on the mobile device. 
     The magnetometer obtains a measure of the magnetic field that is present in the immediate surroundings of the mobile device as a two or three-component vector in a Cartesian coordinate system using 2-axis or 3-axis magnetic sensors. The sensed magnetic field can contain a contribution of the Earth&#39;s magnetic field and a contribution by a local interference field (device co-located interference fields). The latter is a magnetic field that is created by components in the local environment of the mobile device. This may include contributions by one or more magnetic field sources that are near the magnetic sensors, such as the magnet of a loudspeaker that is built into the mobile device. The interference field may also have a contribution due to one or more metal objects found in the external environment close to the device, such as when the user is driving an automobile, riding in a train or bus, or riding on a bicycle or motorcycle. In most cases, the interference field is not negligible relative to the Earth&#39;s magnetic field. Therefore, a calibration procedure is needed to reduce the adverse impact of the interference field contribution from the sensors&#39; measurements to allow the magnetometer to calculate a more accurate direction. 
     There are several types of 3-axis calibration procedures. In one such technique, the user is instructed to rotate the mobile device (containing the magnetometer) according to a set of geometrically different orientations and azimuth angles, while measurements by the magnetometer and by an orientation sensor are collected and analyzed to isolate or quantify the interference field. The quantified interference field can then be subtracted from the measurement taken by the magnetic sensor to yield the Earth&#39;s geomagnetic field. The Earth&#39;s geomagnetic field can be further corrected to get the true north direction, such as correcting for magnetic variation (declination) due to the variation of the Earth&#39;s magnetic field based on geographic location. 
     In another 3-axis calibration technique, rather than instruct the user to rotate the mobile device in a predetermined manner, measurements are collected from the magnetometer, continuously over a period of time, while the mobile device is being used or carried by the user. This can lead to random (albeit sufficient) rotations of the mobile device, such that the magnetometer measurements define a desired, generally spherical measurement space. The sphere is offset from the origin of a coordinate system for the Earth&#39;s geomagnetic field vector by an unknown offset vector, which can represent a substantial part (if not all) of the interference field. Mathematical processing of the measurements can be performed to “re-center” the sphere by determining the offset vector. This technique is transparent to the user because the user is not required to go through a calibration procedure where the user deliberately rotates the device through a specified set of orientations. 
     The calibration techniques described above are effective but time consuming. As the user travels with the mobile device, the magnetometer will encounter different magnetic environments with varying local interference. These different magnetic environments can require a recalibration procedure and the calculation of a new offset vector. Even if the user returns to a previous location, a recalibration procedure may be required due to a change in the local interference field. 
     To avoid recalibrating the magnetometer for each use, a calibration database may be constructed with previously calibrated readings. In these instances, raw magnetometer data is compared to a lookup table of previously calibrated readings, including thresholds, to determine matches with the raw magnetometer data. If a match is found, the bias offset for the matching calibrated reading may be applied to the raw magnetometer data to determine an estimated geomagnetic field. The calibration database may include gaps such that no previously calibrated readings match the raw calibration data. These gaps in the calibration database may result in erroneous calibration data being used to calibrate the magnetometer readings resulting in large errors in heading estimates. 
     SUMMARY 
     Implementations are disclosed for validating data retrieved from a calibration database. In some implementations, calibrated magnetometer data for a magnetometer of a mobile device is retrieved from a calibration database and validated by data from another positioning system, such as course or heading data provided by a satellite-based positioning system. In some implementations, one or more context keys are used to retrieve magnetometer calibration data from a calibration database that is valid for a particular context of the mobile device, such as when the mobile device is mounted in a vehicle. In some implementations, currently retrieved calibration data is compared with previously retrieved calibration data to determine if the currently retrieved calibration data is valid. 
     In some implementations, a method comprises: receiving a reading from a magnetometer of a mobile device; selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold; estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; determining a gravitation vector; determining an inclination angle between the gravitational vector and the estimated magnetic field; determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; determining a first heading for the mobile device using the estimated external field; comparing the first heading with a second heading obtained from data provided by a location processor of the mobile device; and validating the first heading in response to the comparing. 
     In some implementations, a method comprises: receiving a reading from a magnetometer of a mobile device; selecting, at a first time, a first cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected first cluster has a first representative bias offset, a first mean of magnitudes in the selected first cluster, and a first magnitude threshold; selecting, at a second time, a second cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected second cluster has a second representative bias offset, a second mean of magnitudes in the selected second cluster, and a second magnitude threshold; comparing the first and second representative bias offset; validating the second representative bias offset based on the comparing; estimating an external magnetic field based on the reading and the second representative bias offset for the selected second cluster; determining whether a magnitude of the estimated external field is within a magnitude range defined by the second mean magnitude and the second mean magnitude plus the second magnitude threshold; determining a gravitation vector; determining an inclination angle between the gravitational vector and the estimated magnetic field; determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and determining a heading for the mobile device using the estimated external field. 
     In some implementations, a method comprises: receiving a reading from a magnetometer of a mobile device; selecting a cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings, wherein the selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold, where the selecting uses a context key; estimating an external magnetic field based on the reading and the representative bias offset for the selected cluster; determining whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold; determining a gravitation vector; determining an inclination angle between the gravitational vector and the estimated magnetic field; determining whether the inclination angle is within an angle range defined by a mean inclination angle for the selected cluster and the mean angle plus an angle threshold; determining the magnitude of the estimated external field matches the magnitude range and the inclination angle matches the angle range; and determining a first heading for the mobile device using the estimated external field. 
     Particular implementations disclosed herein provide one or more of the following advantages. Validating magnetometer calibration data retrieved from a database prevent erroneous calibration data from being used to correct magnetometer readings, which could result in errors in computing a heading for the mobile device. 
     Other implementations are disclosed for systems, methods and apparatus. The details of the disclosed implementations are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates an exemplary Cartesian coordinate system describing the Earth&#39;s geomagnetic field in accordance with some implementations. 
         FIG. 2  illustrates an exemplary 3-axis magnetometer in accordance with some implementations. 
         FIG. 3  illustrates an example system for evaluating readings against clustered data. 
         FIG. 4  is a three-dimensional graph illustrating clustering on bias vectors. 
         FIG. 5  is a flow chart illustrating an example method for comparing an estimated external field to clustered data. 
         FIG. 6  is a flow chart illustrating an example method for re-execution a clustering algorithm in response to a trigger event. 
         FIG. 7  is a block diagram of the cluster matching module of  FIG. 3 . 
         FIG. 8  is a flow chart illustrating an example method for validating calibration data retrieved from a calibration database. 
         FIG. 9  is a block diagram of exemplary architecture of a mobile device employing the processes of  FIGS. 5-8  in accordance with some implementations. 
     
    
    
     The same reference symbol used in various drawings indicates like elements. 
     DETAILED DESCRIPTION 
     Raw Magnetic Field 
     Overview 
       FIG. 1  illustrates an exemplary Cartesian coordinate system for describing the Earth&#39;s geomagnetic field {right arrow over (E)} in accordance with some implementations. The geomagnetic field vector {right arrow over (E)} can be described, in device coordinates, by the orthogonal components E x  (toward top of a mobile device), E y  (toward right side of mobile device) and E z  (back side of mobile device, positive downwards); the magnitude |{right arrow over (E)}|; and inclination (or dip relative to a horizontal line) I. Similarly, the gravitational acceleration vector {right arrow over (g)} can be described by the orthogonal components g x  (toward top of a mobile device), g y  (toward right side of mobile device) and g z  (backside of mobile device, positive downwards); and magnitude |{right arrow over (g)}|. The vectors {right arrow over (E)} and {right arrow over (g)}, and the horizontal line are coplanar, and the intersection of {right arrow over (g)} and the horizontal line form a 90° angle. The magnitude |{right arrow over (E)}|, the magnitude |{right arrow over (g)}|, and the inclination angle I can be computed from the orthogonal components using the following equations: 
     
       
         
           
             
               
                 
                   
                     
                        
                       
                         E 
                         ⇀ 
                       
                        
                     
                     = 
                     
                       
                         
                           E 
                           x 
                           2 
                         
                         + 
                         
                           E 
                           y 
                           2 
                         
                         + 
                         
                           E 
                           z 
                           2 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   [ 
                   1 
                   ] 
                 
               
             
             
               
                 
                   
                     
                        
                       
                         g 
                         ⇀ 
                       
                        
                     
                     = 
                     
                       
                         
                           g 
                           x 
                           2 
                         
                         + 
                         
                           g 
                           y 
                           2 
                         
                         + 
                         
                           g 
                           z 
                           2 
                         
                       
                     
                   
                   , 
                   and 
                 
               
               
                 
                   [ 
                   2 
                   ] 
                 
               
             
             
               
                 
                   I 
                   = 
                   
                     90 
                     - 
                     
                       ( 
                       
                         
                           
                             cos 
                             
                               - 
                               1 
                             
                           
                           ⁡ 
                           
                             ( 
                             
                               
                                 
                                   E 
                                   ⇀ 
                                 
                                 · 
                                 
                                   g 
                                   ⇀ 
                                 
                               
                               
                                 
                                    
                                   
                                     E 
                                     ⇀ 
                                   
                                    
                                 
                                 ⁢ 
                                 
                                    
                                   
                                     g 
                                     ⇀ 
                                   
                                    
                                 
                               
                             
                             ) 
                           
                         
                         * 
                         
                           180 
                           π 
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   [ 
                   3 
                   ] 
                 
               
             
           
         
       
     
     The inclination angle I can be defined as the angle between the geomagnetic field {right arrow over (E)} and the Earth&#39;s gravitational acceleration vector {right arrow over (g)}. The most commonly used International System of Units (SI) unit of magnetic-field magnitude is the Tesla. 
     Overview Of Magnetometers 
       FIG. 2  illustrates an exemplary 3-axis magnetometer in accordance with some implementations. In general, a magnetometer is an instrument that can sense the magnitude and direction of a magnetic field in its vicinity. Magnetometers can be 2-axis or 3-axis and the processes described here apply to both types of sensors. In the interest of brevity, only a 3-axis magnetometer is described. 
     In some implementations, 3-axis magnetometer sensor configuration  100  can be used to calculate a heading for a variety of applications, including applications running on a mobile device. For example, magnetometers may be used that require dead reckoning or headings, such as navigation applications for vehicles, aircraft, watercraft and mobile devices (e.g., smart phones). Sensor configuration  200  can include three magnetic field sensors  202 ,  204 ,  206  mounted orthogonally on a board, substrate or other mounting surface. Magnetic sensors  202 ,  204 ,  206  can be included in an integrated circuit (IC) package with or without other sensors, such as accelerometers and gyros. 
     Sensor configuration  200  can be deployed in a host system environment that contains interfering magnetic fields. Since the Earth magnetic field is a weak field (˜0.5 Gauss), other magnetic fields can interfere with the accuracy of sensors  202 ,  204 ,  206 . For example, the sensors  202 ,  204 ,  206  may pick or otherwise detect magnetic fields generated by components of the mobile device, which may be referred to as bias offset. A calibration procedure can be deployed to isolate and remove the bias offset. One technique is to determine an offset vector, which can be subtracted from sensor measurements to get accurate measurements of the Earth&#39;s magnetic field. 
     In one exemplary calibration procedure for a 3-axis magnetometer, each heading computation is made with a number of valid X, Y, and Z sensor readings, which can be taken with a minimal delay between each reading. For this sensor configuration, sensors  202 ,  204 ,  206  are at right angles with respect to each other and lie level with respect to the Earth&#39;s surface. As discussed above, the positive end of the X-axis points to the top of the mobile device, the positive end of the Y-axis points to the right side of the mobile device when facing the screen, and the positive end of the Z-axis points to backside of the mobile device. During calibration, two consecutive sensor readings may be made 180 degrees apart. These measurements can be represented by reading (Rx1, R y1 , R z1 ) and reading (R x2 , R y2 , R z2 ), which are measurements of the raw magnetic field including Earth&#39;s magnetic field plus bias offset. The Earth&#39;s magnetic field in any given direction as measured with no interfering field can be represented by values (E x , E y , E z ). Magnetic interference can be represented by values (B x , B y , B z ). Using these mathematical conventions, the two sensor readings can be represented by
 
 R   x1   =E   x   +B   x ;
 
 R   y1   =E   y   +B   y ;
 
 R   z1   =E   z   +B   z ;
 
 R   x2   =−E   x   +B   x ;
 
 R   y2   =−E   y   +B   y ; and
 
 R   z2   =−E   z   +B   z .  [4]
 
     Assuming the magnetometer is fixed with respect to the host system (e.g., a magnetometer installed in a mobile phone), the readings (R x1 , R y1 , R z1 ) and (R x2 , R y2 , R z2 ) taken during calibration may both contain substantially the same interference values (B x , B y , B z ). Since the magnetometer readings taken during calibration are 180 degrees apart the readings of the Earth&#39;s magnetic field (E x , E y , E z ) are equal but opposite in sign (−E x , −E y , −E z ). Solving the equations above for B x , B y , and B z  yields:
 
 B   x =( R   x1   +R   x2 )/2,
 
 B   y =( R   y1   +R   y2 )/2, and
 
 B   z =( R   z1   +R   z2 )/2.  [5]
 
     Using the determined bias offsets for each component, the Earth&#39;s magnetic field (E x , E y , E z ) may be determined by the following equations:
 
 E   x   =R   x1   −B   x ;
 
 E   y   =R   y1   −B   y ; and
 
 E   z   =R   z1   −B   z .  [6]
 
     At any given position on Earth, a magnitude |{right arrow over (E)}| is substantially constant, regardless of magnetometer orientation. In addition, at any given position on Earth, the inclination angle I between the gravitational vector {right arrow over (g)} and the Earth&#39;s magnetic field {right arrow over (E)} is substantially constant because the orientation of the gravitational acceleration vector {right arrow over (g)} and the orientation of geomagnetic field vector {right arrow over (E)} are substantially constant. 
     A heading ψ of the mobile device may be calculated from the determined {right arrow over (E)} and {right arrow over (g)} using equations [7]-[11] as followed: 
     
       
         
           
             
               
                 
                   
                     θ 
                     = 
                     
                       - 
                       
                         
                           sin 
                           
                             - 
                             1 
                           
                         
                         ⁡ 
                         
                           ( 
                           
                             
                               g 
                               x 
                             
                             
                                
                               
                                 g 
                                 ⇀ 
                               
                                
                             
                           
                           ) 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   [ 
                   7 
                   ] 
                 
               
             
             
               
                 
                   
                     ϕ 
                     = 
                     
                       
                         sin 
                         
                           - 
                           1 
                         
                       
                       ⁡ 
                       
                         ( 
                         
                           
                             g 
                             y 
                           
                           
                             
                                
                               
                                 g 
                                 ⇀ 
                               
                                
                             
                             ⁢ 
                             cos 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             θ 
                           
                         
                         ) 
                       
                     
                   
                   , 
                 
               
               
                 
                   [ 
                   8 
                   ] 
                 
               
             
             
               
                 
                   
                     X 
                     h 
                   
                   = 
                   
                     
                       
                         E 
                         x 
                       
                       ⁢ 
                       
                         cos 
                         ⁡ 
                         
                           ( 
                           θ 
                           ) 
                         
                       
                     
                     + 
                     
                       
                         E 
                         y 
                       
                       ⁢ 
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           ϕ 
                           ) 
                         
                       
                       * 
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           θ 
                           ) 
                         
                       
                     
                     - 
                     
                       
                         E 
                         z 
                       
                       ⁢ 
                       
                         cos 
                         ⁡ 
                         
                           ( 
                           ϕ 
                           ) 
                         
                       
                       * 
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           θ 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   9 
                   ] 
                 
               
             
             
               
                 
                   
                     Y 
                     h 
                   
                   = 
                   
                     
                       
                         E 
                         x 
                       
                       ⁢ 
                       
                         cos 
                         ⁡ 
                         
                           ( 
                           ϕ 
                           ) 
                         
                       
                     
                     + 
                     
                       
                         E 
                         y 
                       
                       ⁢ 
                       
                         sin 
                         ⁡ 
                         
                           ( 
                           ϕ 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   10 
                   ] 
                 
               
             
             
               
                 
                   ψ 
                   = 
                   
                     
                       
                         tan 
                         
                           - 
                           1 
                         
                       
                       ⁡ 
                       
                         ( 
                         
                           
                             Y 
                             h 
                           
                           
                             X 
                             h 
                           
                         
                         ) 
                       
                     
                     . 
                   
                 
               
               
                 
                   [ 
                   11 
                   ] 
                 
               
             
           
         
       
     
     The heading may be calibrated using other implementations. For example, the heading may also be calibrated based on the orientation of the device obtained from an accelerometer, inclination, GPS, and other types of corrections or calibrations. 
     If a magnetometer is included in a mobile device, such as a mobile phone, the bias offset may change. For example, if the user docks his mobile device (containing the magnetometer) in his car, magnetic objects in the car could change the local interference, which could result in the calibration offsets becoming invalid. If the offsets are invalid, then the magnetometer can perform a recalibration procedure to generate new offsets. This recalibration procedure can be a tedious process for the user if performed often and may require the user to manipulate the mobile device through a number of angles. 
     Example Cluster Evaluation of Magnetometer Data 
       FIG. 3  illustrates an example system  300  for cluster evaluation of magnetometer data. For example, the system  300  may store calibrated magnetometer data including magnitudes |{right arrow over (E)}| of the geomagnetic field, inclination angle I, and associated bias offsets (B x , B y , B z ). As illustrated, the system  300  includes a magnetometer  302  for detecting magnetic fields, a magnetometer calibration module  304  for calibrating magnetometer data, magnetometer database  306  for storing calibrated magnetometer data, a clustering module  308  for determining clusters of the calibrated magnetometer data, clustered magnetometer database  310  for storing clustered data, and a cluster matching module  312  for determining whether magnetometer data matches any clusters in the clustered magnetometer database  310 . In particular, the magnetometer  302  may be 2-axis or 3-axis magnetometer as discussed with respect to  FIG. 2  that measures different components of the raw magnetic field (R x , R y , R z ) in, for example, device coordinates. 
     In response to a recalibration trigger event, the magnetometer  302  may pass magnetometer data to the magnetometer calibration module  304 . A recalibration trigger event can be any event that triggers a recalibration procedure on the mobile device. The trigger event can be based on time, location, mobile device activity, an application request, magnetometer data, expiration of a time period, or other events. In connection with recalibration, the magnetometer calibration module  304  may determine bias offsets (B x , B y , B z ) based on a raw magnetometer reading (R x , R y , R z ) and determine the Earth&#39;s magnetic field vector {right arrow over (E)} based on the bias offsets (B x , B y , B z ) and the raw magnetometer reading (R x , R y , R z ) as discussed with respect to  FIG. 2 . In addition, the magnetometer calibration module  304  may determine the magnitude |{right arrow over (E)}| of the geomagnetic field given by Equation [1] and determine the inclination angle I based on the determined geomagnetic field vector {right arrow over (E)} and the gravitational vector {right arrow over (g)} given by Equation [3]. The magnetometer calibration module  304  may receive the gravitational vector {right arrow over (g)} from a location processor, accelerometer readings, or other sources. 
     Each time a calibration procedure is performed, the magnetometer calibration module  304  may store, in the magnetometer database  306 , the magnitude |{right arrow over (E)}|, the inclination angle I, and associated bias offsets (B x , B y , B z ). As previously mentioned, the magnitude |{right arrow over (E)}| and the inclination angle I for each location should be theoretically constant regardless of the position of the magnetometer on the Earth or its orientation. If these parameters are not constant then the bias offset may have changed. The magnetometer database  306  may store other parameters such as, for example, temperature, calibration level or timestamp. The calibration level can be used to determine the accuracy or quality of a set of calibration data (e.g., offset values), so that an accurate set of calibration data are not overwritten with a less accurate set of calibration data. The timestamp can be used to manage entries in the magnetometer database  306 . For example, the timestamp can be used with an “aging” algorithm for overwriting entries in the magnetometer database  306 , so that magnetometer database  306  does not grow too large. In some implementations, entries with the oldest timestamp can be overwritten first. In some implementations, a count is kept for each entry in the magnetometer database  306  and may be incremented each time an entry is used to restore calibration offsets. The entries with the lowest count may be overwritten first. In some implementations, both timestamps and counts can be used for managing the magnetometer database  306 . The assumption that the magnitude |{right arrow over (E)}| and the inclination angle I are should be theoretically constant makes these Earth magnetic field parameters useful for determining the confidence of a match, as described in reference to  FIG. 4 . 
     The clustering module  308  can include any software, hardware, firmware, or combination thereof configured to execute a clustering algorithm on the bias offsets (B x , B y , B z ) stored in the magnetometer database  306  to form clusters. For example, the clustering module  308  may apply the well-known clustering algorithm known as quality threshold (QT) clustering to entries in the magnetometer database  306  to create clusters of bias offsets. Other clustering algorithms may be used such as connectivity based clustering, centroid-based clustering, distribution-based clustering, density-based clustering, or others. In general, cluster analysis or clustering assigns a set of objects into groups, e.g., clusters, so that the objects in the same cluster are more similar to each other based on one or more metrics than to objects in other clusters. In some implementations, the clusters may be based on the Euclidean distance between bias offset points. Further details of operations of clustering module  308  are described below in reference to  FIG. 4 . 
     The clustering module  308  stores the determined clusters in the clustered magnetometer database  310 . For each cluster, the clustering module  308  may determine a mean magnitude C m  of the Earth&#39;s geomagnetic field {right arrow over (E)} as follows: 
                       C   m     =         ∑     i   =   1     N     ⁢           ⁢            E   i     ⇀            N       ,           [   12   ]               
where N is the number of geomagnetic field vectors {right arrow over (E)} in the cluster. Also, the clustering module  308  may determine a mean inclination angle C a  of the Earth&#39;s geomagnetic field {right arrow over (E)} as follows:
 
                       C   a     =         ∑     i   =   1     N     ⁢           ⁢     I   i       N       ,           [   13   ]               
where N is the number of geomagnetic fields {right arrow over (E)} in the cluster. In addition, the clustering module  308  may determine a magnitude threshold and an angle threshold. For example, the magnitude threshold may be based on the standard deviation of the magnitudes of the geomagnetic fields |{right arrow over (E)}| in the cluster, and the angle threshold may be based on the standard deviation of the inclination angles in the cluster. In addition, the clustering module  308  may determine, for each cluster, a representative bias offset. For example, the clustering module  308  may determine, for each cluster, the geometric center of the cluster as the representative bias offset for the cluster. In some instances, the clustering module  308  determines, for each cluster, the mean of the bias offsets as the center of the cluster. The clustering module  308  may use other statistical measures for determining the center of the cluster such as determining a median of the bias offsets of the cluster.
 
     The cluster matching module  312  can include any software, hardware, firmware, or combination thereof for determining, for each of the clusters in the clustered magnetometer database  310 , whether magnetometer data from the magnetometer  302  satisfies the mean magnitude and magnitude threshold and the mean inclination angle and angle threshold. In particular, the cluster matching module  312  may identify the representative bias offset for each of the clusters and determine an estimated external field using the equations [6]. In other words, the cluster matching module  312  may, for each cluster, estimate the geomagnetic field {right arrow over (E)} using the following equation:
 
 {right arrow over (E)}={right arrow over (R)}−{right arrow over (B)},   [14]
 
where {right arrow over (R)} is the raw magnetic field vector determined by the magnetometer  302  and {right arrow over (B)} is the representative bias offset for the cluster. Once determined, the cluster matching module  312  may determine the magnitude |{right arrow over (E)}| and the inclination angle I for the estimated geomagnetic field {right arrow over (E)} using the equations [1]-[3].
 
     After the magnitude and inclination angle are determined for the estimated geomagnetic field {right arrow over (E)} for a cluster, the cluster matching module  312  may determine whether the estimated magnitude is within the range of the mean of the Earth&#39;s geomagnetic field to the mean plus the threshold for the cluster. In addition, the cluster matching module  312  may determine whether the estimated inclination angle I is within the range of the mean of the inclination angle to the mean plus the threshold for the cluster. If a match is not found, the cluster matching module  312  iteratively executes these calculations to determine if the estimated geomagnetic field matches any of the clusters in the clustered magnetometer database  310 . If a match with a cluster is found, an estimated heading ψ can be computed using the equations [7]-[11], the estimated geomagnetic field {right arrow over (E)} determined from the representative bias offset of the matching cluster, and the gravitational vector {right arrow over (g)}. 
     Clustering Overview 
       FIG. 4  is a three-dimensional graph illustrating exemplary clustering techniques of bias offsets. In particular, the diagram is a three-dimensional space based on the bias offsets (B x , B y , B z ). The clustering module  308  (as described in reference to  FIG. 3 ) can apply a QT algorithm to create exemplary clusters of bias offsets C1, C2, and C3. The graph shows different clusters C1, C2, and C3 that are illustrated in different shades of gray. Clusters that include only a single point are illustrated in the same shade of gray. 
     The clustering module  308  can analyze the data in magnetometer database  306  as described above in reference to  FIG. 3 . The clustering module  308  can identify a first class of bias offsets having a first label (e.g., those labeled as “positive”) and bias offsets having a second label (e.g., those labeled as “negative”). The clustering module  308  can identify a specified distance (e.g., a minimum distance) between a first class bias-offset point (e.g., “positive” bias-offset point  402 ) and a second-class motion feature (e.g., “negative” bias-offset point  404 ). The clustering module  308  can designate the specified distance as a quality threshold. 
     The clustering module  308  can select the first bias-offset point  402  to add to the first cluster C1. The clustering module  308  can then identify a second bias-offset point  404  whose distance to the first bias-offset point  402  is less than the quality threshold and, in response to satisfying the threshold, add the second bias-offset point  404  to the first cluster C1. The clustering module  308  can iteratively add bias-offset points to the first cluster C1 until all bias-offset points whose distances to the first bias-offset point  402  are each less than the quality threshold has been added to the first cluster C1. 
     The clustering module  308  can remove the bias-offset points in C1 from further clustering operations and select another bias-offset point (e.g., bias-offset points  406 ) to add to a second cluster C2. The clustering module  308  can iteratively add bias-offset points to the second cluster C2 until all bias-offset points whose distances to the bias-offset point  406  are each less than the quality threshold have been added to the second cluster C2. The clustering module  308  can repeat the operations to create clusters C3, C4, and so on until all bias-offset points features are clustered. 
     The clustering module  308  can generate representative bias offsets for each cluster. In some implementations, the clustering module  308  can designate as the representative bias offsets the geometric center (e.g., mean of the bias offsets in the cluster) of the cluster such as the center  408  for cluster C1. The clustering module  308  may use other techniques for designating a bias-offset point as the representative bias offsets. For example, the clustering module  308  may identify an example that is closest to other samples. In these instances, the clustering module  308  can calculate distances between pairs of bias-offset points in cluster C1 and determine a reference distance for each bias-offset point. The reference distance for a bias-offset point can be a maximum distance between the bias-offset point and another bias-offset point in the cluster. The clustering module  308  can identify a bias-offset point in cluster C1 that has the minimum reference distance and designate the bias-offset point as the bias offsets for cluster C1. 
     Example Process for Managing Clustered Data 
       FIGS. 5 and 6  are flow charts illustrating example methods  500  and  600  for managing clustered data in accordance with some implementations of the present disclosure. Methods  500  and  600  are described with respect to the system  300  of  FIG. 3 . Though, the associated system may use or implement any suitable technique for performing these and other tasks. These methods are for illustration purposes only and that the described or similar techniques may be performed at any appropriate time, including concurrently, individually, or in combination. In addition, many of the steps in these flowcharts may take place simultaneously and/or in different orders than as shown. Moreover, the associated system may use methods with additional steps, fewer steps, and/or different steps, so long as the methods remain appropriate. 
     Referring to  FIG. 5 , method  500  begins at step  502  where magnetometer data for the raw magnetic field is detected. For example, the magnetometer  302  in  FIG. 3  may detect magnetic fields in device coordinates and pass the readings to the cluster matching module  312 . At step  504 , a plurality of clusters is identified. As for the example, the cluster matching module  312  may retrieve or otherwise identify clusters stored in the clustered magnetometer database  310 . Next, at step  506 , a representative bias offset is identified for an initial cluster. In some implementations, method  500  is iterated through all clusters such that the order may be determined based on any parameter such as timestamp, size, assigned indices, or others. An estimate of the external field is determined by the representative bias offset from the raw magnetic field at step  508 . At step  510 , the magnitude of the estimated external field is determined. 
     Next, at step  512 , a gravitational vector is identified. The inclination angle is determined, at step  514 , based on an estimated external field vector and the gravitational vector. If the magnitude does not match the mean magnitude and threshold of the cluster or the inclination angle for the external field does not match the mean angle and threshold of the cluster at decisional step  516 , then execution proceeds to decisional step  518 . If another cluster is available for evaluation, then, at step  520 , the representative bias offset for the next cluster is identified. If another cluster is not available to evaluate, the novel bias is stored in the database at step  522 . Returning to decisional step  516 , if both the magnitude of the estimated external field satisfy the magnitude threshold and the inclination angle of the estimated external field satisfy the angle threshold, a heading of the mobile device may be determined based on the estimated external field at step  524 . 
     Referring to  FIG. 6 , method  600  begins at step  602  where the offset bias for magnetometer data is determined during calibration. For example, the magnetometer  302  may detect multiple points, and the magnetometer calibration module  304  may directly determine the bias offset using the multiple points. At step  604 , the determined bias offset is added to the bias table. As for the example, the magnetometer calibration module  304  may store the bias offset in a lookup table stored in the magnetometer database  306 . If the determined bias offset is novel at decisional step  606 , then execution proceeds to decisional step  608 . For example, the magnetometer calibration module  304  may determine that the bias offset does not fall within the existing clusters. If the total number of bias offsets exceed a specified threshold (e.g., 2, 5, 10), then, at step  610 , the clustering algorithm applied to the bias offset data stored in the magnetometer database  310  to determine new clusters. At step  612 , the determined clusters are stored in a cluster table. As for the example, the clustering module  308  may apply the clustering algorithm on the magnetometer database  306 , generate new clusters, and store the new clusters in a table of clustered magnetometer database  310 . If either the bias offset falls within a cluster or the number of novel bias offsets have not exceed a threshold, then execution ends. 
       FIG. 7  is a block diagram of the cluster matching module  312  of  FIG. 3 . In some implementations, cluster matching module  312  includes matching module  702  and match validation module  704 . Matching module  702  receives raw magnetometer data and calibrated magnetometer data and generates an estimated external field vector according to process  500 , described in reference to  FIG. 5 . 
     Match validation module  704  receives positioning system data and one or more context keys. Positioning system data can include a heading for the mobile device. The positioning system can be a Global Positioning System (GPS) receiver onboard the mobile device. Match validation module  704  can be coupled to storage  706  (e.g., memory) which contains previously retrieved calibration data. Erroneous calibrated magnetometer data (e.g., bias offsets) may be retrieved from clustered magnetometer database  310  and applied to the raw magnetometer readings, causing an inaccurate heading for the mobile device to be computed. To prevent this from occurring, the calibration data is validated by match validation module  704 . 
     Validating Against Data from Other Sensors 
     In some implementations, calibration data for the magnetometer is validated by computing a heading for the mobile device using the retrieved calibration data, as described in reference to  FIG. 5 . The computed heading is compared to a heading for the mobile device provided by a positioning system (e.g., GPS) on board the mobile device. If the difference between the two headings exceeds a specified threshold, the retrieved data is invalid and a new set of magnetometer calibration data is retrieved from clustered magnetometer database  310 . If the difference between the two headings does not exceed the specified threshold, the retrieved calibration data is valid and can be used to compute the heading of the mobile device. 
     Validating Against Previously Retrieved Calibration Data 
     In another example scenario, a currently retrieved calibrated data is compared with a previously retrieved calibrated data to determine whether the currently retrieved calibrated data is valid. For example, calibrated data can be retrieved from the clustered magnetometer database  310  at specified rate or schedule. The calibration data can be stored in database  706  of cluster matching module  312 . If the time interval between retrievals is small, there is an expectation that the calibration data would not deviate by a large amount between retrievals. By comparing the currently retrieved calibration data with previously retrieved calibration data, the currently retrieved calibration data can be validated or invalidated depending on the outcome of the comparing. For example, if the difference between the currently retrieved calibration data and the previously retrieved calibration data exceeds a threshold, then the currently retrieved calibration data is valid. Otherwise, the currently retrieve calibration data is not valid. 
     Context Keys 
     In some implementations, one or more context keys can be used to retrieve calibration data. As previously, described the mean magnitude of the Earth&#39;s geomagnetic field and the mean inclination angle can be used to retrieve calibration data from database  310 . In addition to these retrieval keys, a context key can be used to better constrain the calibration data retrieval. One example context key is a mounted state key, which indicates that the mobile device was in a mounted state in a vehicle when the calibration occurred. When the calibration data is stored in database  310 , data indicating the mounted state (e.g., a flag) is stored together with the mean magnitude and mean inclination angle. When a database retrieval is requested, the mean magnitude, mean inclination angle and mounted state flag can be used in the matching process performed by matching module  702 . 
     Another example context key is an in-transit key, which indicates that the mobile device was in transit (e.g., on a train) when the calibration occurred. In some implementations, database  310  can be divided into sections according to context keys or separate databases can be used for different contexts. For example, calibration data for a mounted mobile device can be in one section of a database or a different database then data for a mobile device that is in transit. 
       FIG. 8  is a flow chart illustrating an example method  800  for comparing an estimated external field to clustered data, including validating the clustered data. Steps  802 ,  804 ,  806 ,  808 ,  810 ,  812  and  814  in method  800  are similar to steps  502 ,  504 ,  506 ,  508 ,  510 ,  512  and  514  in method  500  shown in  FIG. 5 , except for the addition of step  823 . If a match is found at step  816 , then a heading is determined based on the external magnetic field  824 . Otherwise, method  800  continues to step  820  and identifies another representative bias offset for a next cluster. 
     Example Mobile Device Architecture 
       FIG. 9  is a block diagram of exemplary architecture  900  of a mobile device including an electronic magnetometer. The mobile device  900  can include memory interface  902 , one or more data processors, image processors and/or central processing units  904 , and peripherals interface  906 . Memory interface  902 , one or more processors  904  and/or peripherals interface  906  can be separate components or can be integrated in one or more integrated circuits. Various components in mobile device architecture  900  can be coupled together by one or more communication buses or signal lines. 
     Sensors, devices, and subsystems can be coupled to peripherals interface  906  to facilitate multiple functionalities. For example, motion sensor  910 , light sensor  912 , and proximity sensor  914  can be coupled to peripherals interface  906  to facilitate orientation, lighting, and proximity functions of the mobile device. Location processor  915  (e.g., GPS receiver) can be connected to peripherals interface  906  to provide geopositioning. Electronic magnetometer  916  (e.g., an integrated circuit chip) can also be connected to peripherals interface  906  to provide data that can be used to determine the direction of magnetic North. 
     Camera subsystem  920  and Optical sensor  922 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as recording photographs and video clips. 
     Communication functions can be facilitated through one or more wireless communication subsystems  924 , which can include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of communication subsystem  924  can depend on the communication network(s) over which the mobile device is intended to operate. For example, the mobile device may include communication subsystems  924  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi or WiMax network, and a Bluetooth™ network. In particular, wireless communication subsystems  924  may include hosting protocols such that the mobile device may be configured as a base station for other wireless devices. 
     Audio subsystem  926  can be coupled to speaker  928  and microphone  930  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. Note that speaker  928  could introduce magnetic interference to the magnetometer, as described in reference to  FIGS. 1-2 . 
     I/O subsystem  940  can include touch surface controller  942  and/or other input controller(s)  944 . Touch surface controller  942  can be coupled to touch surface  946 . Touch surface  946  and touch surface controller  942  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  946 . 
     Other input controller(s)  944  can be coupled to other input/control devices  948 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, docking station and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of speaker  928  and/or microphone  930 . 
     In one implementation, a pressing of the button for a first duration may disengage a lock of touch surface  946 ; and a pressing of the button for a second duration that is longer than the first duration may turn power to the mobile device on or off. The user may be able to customize a functionality of one or more of the buttons. Touch surface  946  can be used to implement virtual or soft buttons and/or a keyboard. 
     In some implementations, the mobile device can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. 
     Memory interface  902  can be coupled to memory  950 . Memory  950  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory (e.g., NAND, NOR). Memory  950  can store operating system  952 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. Operating system  952  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  952  can be a kernel (e.g., UNIX kernel). 
     Memory  950  may also store communication instructions  954  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers. Memory  950  may include graphical user interface instructions  956  to facilitate graphic user interface processing; sensor processing instructions  958  to facilitate sensor-related processing and functions; phone instructions  960  to facilitate phone-related processes and functions; electronic messaging instructions  962  to facilitate electronic-messaging related processes and functions; web browsing instructions  964  to facilitate web browsing-related processes and functions; media processing instructions  966  to facilitate media processing-related processes and functions; GPS/Navigation instructions  968  to facilitate GPS and navigation-related processes and instructions; camera instructions  970  to facilitate camera-related processes and functions; magnetometer data  972  and calibration instructions  974  to facilitate magnetometer calibration, as described in reference to  FIGS. 1-8 . 
     Memory  950  may also store other software instructions (not shown), such as Web video instructions to facilitate web video-related processes and functions; and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, media processing instructions  966  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. An activation record and International Mobile Equipment Identity (IMEI) or similar hardware identifier can also be stored in memory  950 . 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  950  can include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     The features described may be implemented in digital electronic circuitry or in computer hardware, firmware, software, or in combinations of them. The features may be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and method steps may be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. 
     The described features may be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that may be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program may be written in any form of programming language (e.g., Objective-C, Java), including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors or cores, of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer may communicate with mass storage devices for storing data files. These mass storage devices may include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, ASICs (application-specific integrated circuits). 
     To provide for interaction with an author, the features may be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the author and a keyboard and a pointing device such as a mouse or a trackball by which the author may provide input to the computer. 
     The features may be implemented in a computer system that includes a back-end component, such as a data server or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system may be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include a LAN, a WAN and the computers and networks forming the Internet. 
     The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     One or more features or steps of the disclosed embodiments may be implemented using an Application Programming Interface (API). An API may define on or more parameters that are passed between a calling application and other software code (e.g., an operating system, library routine, function) that provides a service, that provides data, or that performs an operation or a computation. 
     The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer will employ to access functions supporting the API. 
     In some implementations, an API call may report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, communications capability, etc. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. The systems and techniques presented herein are also applicable to other electronic text such as electronic newspaper, electronic magazine, electronic documents etc. Elements of one or more implementations may be combined, deleted, modified, or supplemented to form further implementations. As yet another example, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims.

Metadata:
Filing Date: 20130608
Publication Date: 20151006
Grant Date: 20151006
Priority Date: 20130608
Inventors: CHOW SUNNY KAI PANG
TU XIAOYUAN
Assignee: APPLE INC
CPC Classifications: [{"code": "G01R33/02", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01C17/38", "inventive": true, "first": true, "tree": "[]"}, {"code": "G01C17/38", "inventive": true, "first": true, "tree": "[]"}, {"code": "G01R33/02", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 52004945