Software calibration for AK8963 magnetometer based on optimal ellipsoidal fitting

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 2743∼2751
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp2743-2751 ❒ 2743
Software calibration for AK8963 magnetometer based on
optimal ellipsoidal fitting
Aziz El fatimi, Adnane Addaim, Zouhair Guennoun
Smart Communications Research Team, University Center for Research in Space Technologies, Mohammadia School of Engineering,
Mohammed V University, Rabat, Morocco
Article Info
Article history:
Received Apr 29, 2022
Revised Sep 10, 2022
Accepted Oct 1, 2022
Keywords:
AK8963 Magnetometer
Earth’s magnetic field
Ellipsoidal fitting
MPU-9250
Software calibration
STM32-Nucleo
ABSTRACT
With the rapid development of mechatronics, systems in package (SiP), in par-
ticular the MPU-9250 inertial measurement Unit 9DOF (MPU-6050 6DOF and
AK8963 3DOF), are becoming ubiquitous in applications for autonomous navi-
gation purposes. Nevertheless, they suffer from some accuracy problems related
to axis misalignment, disturbances, and deviation over time that make them un-
able to work autonomously for a long time. This paper will present a simple and
practical calibration method using a least-squares based ellipsoid fitting method
to calibrate and compensate for the error interference of the AK8963 sensor.
Towards the end of this paper, a comparison between before and after the cali-
bration is presented to study the software compensation effect and the stability
of the magnetic sensor under study.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Aziz El fatimi
Smart Communications Research Team, University Center for Research in Space Technologies, Mohammadia
School of Engineering, Mohammed V University
Rabat, Morocco
Email: azizelfatimi@research.emi.ac.ma
1. INTRODUCTION
As for the gravitational field, the magnetic field, also called the geomagnetic field, is an intrinsic
property inherent to the Earth. The Earth’s magnetic field is a vector field that is defined at any point in space
by its magnitude (expressed, generally, in Gauss (G) or Tesla (T)), a direction, and a sense [1], [2]. According
to the National Oceanic and Atmospheric Administration, the Earth behaves like a giant bar magnet. The
direction of the geomagnetic field is similar to a magnetic field produced by a bar magnet, but with inverted
poles according to Figure 1, i.e. on the axis of rotation of the Earth. Indeed, the magnetic south pole is close
to the geographic north pole and the magnetic north pole is close to the geographic south pole. According
to Figure 1, about 90% of the magnetic field is generated from the Earth’s outer core, with a relatively small
variation in intensity and position over time [3]. Its origins of appearance have been revealed for hundreds of
millions of years [2], [4].
The True North Pole represents true North, and the Earth’s magnetic field lines start at the True South
Pole and end at the True North Pole [5], [6]. Its direction is from the magnetic south pole to the magnetic north
pole. It should be noted that the magnetic north direction does not coincide with the true north direction, and
there is an included angle called the declination angle. The geomagnetic intensity at any point on the Earth’s
surface can be represented by the geomagnetic vector
−
→
F , and its magnitude and direction are represented by
the seven elements of geomagnetism (
−
→
F , Fh, D, I, Fx, Fy, Fz), as shown in Figure 2 [7].
Journal homepage: http://ijece.iaescore.com

2744 ❒ ISSN: 2088-8708
Magnetic north
Geographic north
Geographic south
Magnetic south
Figure 1. An illustration of the Earth’s magnetic field
lines and a simple bar magnet
Z (down)
X (true north)
Y (east)
Fx
Fz
Magnetic north
Fh
D
I
F
O
Fy
Figure 2. Schematic diagram of geomagnetic
elements
In Figure 2, point O is a stationary point on the Earth. The X-axis is parallel to the line of geographic
longitude and directed north; the Y-axis is parallel to the line of geographic latitude and oriented towards
the east; the Z-axis is perpendicular to the ground plane; its positive direction is directed downward.
−
→
F is
the geomagnetic vector, and the projection Fh of
−
→
F on the XOY plane is called the geomagnetic horizontal
component; Fx, Fy, Fz are the components of the geomagnetic vector in the north, east, and vertical directions;
the angle D between Fh and the direction of true north is called the magnetic declination; the angle I between
the plane XOY and F is called the magnetic tilt angle. Furthermore, several mathematical models exist, such
as the international geomagnetic reference model (IGRF) [8] and the worldwide geomagnetic model (WMM)
[9], that can deduce the seven geomagnetic elements from the longitude, latitude, and altitude of a given
location. Table 1 presents the seven elements of the magnetic field of the city of Rabat (latitude = 33.9692851 N,
longitude = 6.8922822 W, height = 75 m ), taken by the two models (WMM) [10] and (IGRF) [11].
On the other hand, several types of non-contact sensors, called magnetometers, can determine the
components of the Earth’s magnetic field. The magnetometer is an instrumentation device that is mainly com-
posed of a physical element sensitive to the magnetic field which transforms the variation of a magnetic quantity
into a variation of an electrical quantity, of a conditioner which maintains the optimal functioning of the sen-
sor, and of a signal processing unit which recovers the useful signal of the electrical quantity [12], [13]. In
general, magnetic sensors are used in many fields for navigation purposes; such as unmanned aerial vehicles
(UAV), autonomous vehicles (AVS), drones, robots, and spacecraft. Magnetometers are often used to establish
the directional heading of a craft to allow navigation between two geographical points. An accurate heading is
important for successful navigation. Since magnetometers are imperfect and the Earth’s magnetic field varies
in different locations, calibration is required to ensure heading accuracy.
The remaining parts of this paper are organized into three sections. The first section deals with the
basic notions of the magnetometer and the elliptical method adopted for its calibration. The second section
describes the hardware means used during the experiment to determine the soft and hard error compensation
parameters. The last section presents the analysis of the experimental results obtained in the static and dynamic
states.
Table 1. Magnetic field of Rabat city [10, 11]
Model D [nT] I [nT] Fx [nT] Fy [nT] Fh [nT] Fz [nT] F [nT]
IGRF -1.007 46.065 28027 -493 28031 29094 40401
WMM -1.003 46.061 28030 -491 28035 29093 40402
2. RESEARCH METHOD
Some known calibration methods using Helmholtz coils require placing the magnetometers to be
calibrated in an area where a homogeneous magnetic field is created by the Helmholtz coils. These techniques
have the disadvantage that it becomes necessary to use coils with a very large radius to be able to calibrate
the magnetometer, which poses space problems [14]–[18]. In addition, other methods have the advantage of
calibrating the magnetometer in its environment of use. For example, the Merayo technique, which is both
precise and simple to implement, does not require a specific place or tools [19, 20].
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 2743-2751

Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 2745
2.1. Magnetometer model
The Earth’s magnetic field is always given in the Cartesian coordinate system (
−
→
i ,
−
→
j ,
−
→
k ) by by (1), as
shown in Figure 2, with Fx, Fy, and Fz representing the North, East, and Vertical components of the magnetic
field vector.
−
→
F = Fx
−
→
i + Fy
−
→
j + Fz
−
→
k (1)
In the literature, several works have been carried out on the modeling of the measurements coming from the
magnetometer. Current models make it possible to determine and identify the various errors in the measure-
ments of a magnetic sensor. Generally, the main sources of errors can be separated into two forms external and
internal to the sensor. Those due to disturbances in the magnetic fields around the sensor (magnetic distortion),
and those caused by manufacturing defects (instrumentation errors) [19], [21]–[23]. Consequently, the approx-
imate model for a magnetometer can be modeled as (2) (the influence of temperature on the measurements
from the magnetometer is not taken into account in this model):
−̃
→
F = ϵno.ϵsf .ϵms

ϵsi.
−
→
F + ϵhi

+ ωb + ωη (2)
where
−̃
→
F is the Earth’s magnetic field vector measured by the magnetometer, ϵno is the non-orthogonality error
matrix, ϵsf is the scale factor error matrix, ϵms is the misalignment error matrix, ϵsi is the soft iron error matrix,
−
→
F is the real vector of the Earth’s magnetic field, ϵhi is the hard iron error vector, ωb is the measurement bias
vector, and ωη is the vector of stochastic type errors representing white noise.
2.2. Algorithm formulation
Merayo et al. [19] propose a non-iterative magnetometer calibration algorithm that estimates bias,
scaling, and misalignment errors based on magnetometer measurements taken in multiple random orientations.
Plotting the measurements of the three axes in a Cartesian system results in an ellipsoid, while perfect measure-
ments result in a sphere centered around the origin (0,0,0) [19]. Calibration, therefore, consists of determining
the parameters of the ellipsoid given by (3):
(v − c)T
A(v − c) = k (3)
where v = (x, y, z) is a triaxial magnetometer measurement, c = (x0, y0, z0) is the center of the ellipsoid,
A = (Ai,j)1≤i≤3,1≤j≤3 is a positive definite matrix with real coefficients. Its eigenvectors define the axes of
the ellipsoid, and its eigenvalues are equal to the inverse of the square of the semi-axes [24], and k 0.
To find the previous parameters, the equation of the ellipsoid will be rewritten in quadratic form. The
(3) becomes (4):

x − x0 y − y0 z − z0



A11 A12 A13
A21 A22 A23
A31 A32 A33




x − x0
y − y0
z − z0

 = k (4)
The development of the (4) gives the expression (5):
(x − x0)2
A11 + (y − y0)2
A22 + (z − z0)2
A33 + (x − x0)(y − y0)(A21 + A12)+
(x − x0)(z − z0)(A31 + A13) + (y − y0)(z − z0)(A32 + A23) = k (5)
Including the constants (Cij)1≤i≤3,1≤j≤3∈ R in (5) gives a more simplified form.
C11x2
+ C22y2
+ C33z2
+ C12xy + C13xz + C23yz + C1x + C2y + C3z + C0 = k (6)
In matrix notation, (6) becomes:

x2
y2
z2
xy xz yz x y z

C11 C22 C33 C12 C13 C23 C1 C2 C3
T
= k − C0 (7)
Software calibration for AK8963 magnetometer based on optimal ellipsoidal fitting ... (Aziz El fatimi)

2746 ❒ ISSN: 2088-8708
We apply the formula (8) which simplifies the expression (7).

x2
y2
z2
xy xz yz x y z

p = 1 (8)
where p is a vector of parameters (9×1) to be estimated. Merayo et al. [19] prove that U is an upper triangular
(3×3) matrix can be found by a Cholesky factorization of a positive definite matrix formed by the elements of
p:
A = UT
U =


p1 p4/2 p5/2
p4/2 p2 p6/2
p5/2 p6/2 p3

 (9)
The estimation of the parameters of the vector p requires at least nine measurements in different orientations.
The (8) can then be rewritten as (10):
Dp = 1 (10)
where D is a matrix containing the N magnetometer measurements along the x, y, and z axes. We will then
have:
D =





x2
1 y2
1 z2
1 x1y1 x1z1 y1z1 x1 y1 z1
x2
2 y2
2 z2
2 x2y2 x2z2 y2z2 x2 y2 z2
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
x2
N y2
N z2
N xN yN xN zN yN zN xN yN zN





(11)
The solution for the least squares estimation problem is given by (12).
p =

DT
D
−1
DT
(12)
To make accurate estimates, a large number of measurements should be considered, which requires compu-
tational power and precision to calculate

DT
D
−1
. This problem can be avoided by reformulating (10) as
(13):
D∗
p∗
= 0 (13)
where D∗
contains N magnetometric measurements, reinforced by a column of units:
D∗
=





x2
1 y2
1 z2
1 x1y1 x1z1 y1z1 x1 y1 z1 1
x2
2 y2
2 z2
2 x2y2 x2z2 y2z2 x2 y2 z2 1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
x2
N y2
N z2
N xN yN xN zN yN zN xN yN zN 1





(14)
and p∗
is now a vector of (10x1) . The solution of the homogeneous equation can be computed efficiently by a
singular value decomposition (SVD), which is detailed in [25]. The center of the ellipsoid is calculated by (15).
c = A−1


p7
p8
p9

 (15)
After determining c and U, a calibrated measurement w can be calculated by (16).
w = U(v − c) (16)
2.3. Hardware configuration
2.3.1. MPU-9250
Invensense Technology’s MPU-9250 is a system in package (SiP) combining two chips. It is composed
of an MPU-6050 and an AK8963 magnetometer. It, therefore, has the six axes of the MPU-6050 (3 for the
accelerometer and 3 for the gyroscope) and the three axes of the magnetometer. This kind of circuit is also
called an inertial measurement unit (IMU-9DOF) [26]. The MPU-9250 is connected to the STM32 Nucleo 64
acquisition board via a bidirectional half-duplex synchronous serial bus I2C (SDA: serial data line and SCL:
Int J Elec Comp Eng, Vol. 13, No. 3, June 2023: 2743-2751

Int J Elec Comp Eng ISSN: 2088-8708 ❒ 2747
serial clock line). The Figures 3 and 4 show the rotation polarity and the orientation of the sensitivity axes for
the two sensors used.
Figure 3. Orientation of the axes and rotation polarity
of the gyroscope
Figure 4. Orientation of the magnetometer axes
2.3.2. STM32 Nucleo-F411RE
The STM32 Nucleo-F411RE development board is a type of 32-bit microcontroller made by the
Franco-Italian company STMicroelectronics. STM32 chips are grouped into different series of the same de-
sign, based on 32-bit ARM architecture processors, such as the Cortex-M4. It is designed to perform real-time
low-voltage digital signal processing. This board is ideal for quickly creating prototypes, and its standardized
connectivity makes it possible to build and reuse electronic modules on all the inputs and outputs of the Nucleo
board. The PCB contains an in-circuit ST-LINK/V2-1 programmer and a debugger, which can be used as an
onboard microprocessor or in standalone mode with any other application that includes an STM32 micropro-
cessor [27]. The STM32 Nucleo-F411RE module operates at a frequency of 100 MHz while ensuring relatively
low power consumption in work, sleep, and shutdown modes. Its main characteristics are mentioned in Table 2.
Table 2. Main features of the STM32 Nucleo-F411RE board
Feature Flash memory SRAM memory Operating voltage Temperature range
Value 512 Kbytes 128 Kbytes 1.7 to 3.6 V -40 to +125 ◦C
2.3.3. Electrical scheme
The final electrical scheme adopted for magnetometer data acquisition is shown in Figure 5. The
MPU-9250 is interfaced to the STM32 through an I2C serial connection. The USB port is used to transmit and
receive data between the STM32 microcontroller and the computer.
Figure 5. Electrical scheme for magnetometer data acquisition

2748 ❒ ISSN: 2088-8708
3. RESULTS AND DISCUSSION
This section highlights the implementation of the ellipsoid fitting method based on the least squares
method detailed in the 2.2 section to calibrate and compensate for the error interference of the AK8963 sensor.
Then a comparison between before and after calibration is presented to study the effect of software compensa-
tion and the stability of the magnetic sensor under study. At the end of this section, a plan is made to check that
the algorithm works correctly in both static and dynamic situations.
3.1. Magnetometer calibration parameters
The calibration of magnetometers is generally carried out by collecting the magnetic field intensities
in all directions in space. The final measurement data creates an ellipse in space. The problem, therefore, of
calibration resides in the way of finding the center and the parameters of the ellipsoid. The Figure 6 represents
7,000 samples measurements in all directions from the AK8963 sensor. Then, the calculation of the correction
factors mentioned in the section 2.2 makes it possible to recalibrate the magnetometer as accurately as possible.
As shown in Figure 7, the adjustment algorithm can essentially accurately calculate the position of the center
of the ellipsoid, which represents the offsets of the three axes of the magnetometer. The fitting parameters of
the ellipsoid U and c are given in (17) and (18).
U =


0.0285 0.0004 −0.0020
0 0.0325 −0.0031
0 0 0.0304

 (17)
and
c = [−1.4028 27.9710 − 28.1013]T
(18)
-40 -20 0 20 40 60
-60
-40
-20
0
20
40
60
XY
XZ
YZ
Magnetic
field
[
m
T]
Magnetic field [ mT]
Figure 6. Original magnetometer data
-1.0 -0.5 0.0 0.5 1.0
-1.0
-0.5
0.0
0.5
1.0
XY
XZ
YZ
Calibrated
magnetic
field
Calibrated magnetic field
Figure 7. Compensated magnetometer data
3.2. Results analysis
To better visualize the results, a uniaxial representation is used to represent the experimental data in
both static and dynamic states. Figure 8 illustrates the variation of the magnetic field acquired before and after
calibration along the three axes for ten seconds in a static state. The second experiment concerns the acquisition
of magnetometric measurements in 3D for ten seconds in a dynamic state. The Figure 9 represents the variation
evolution of the received magnetic field before and after calibration.
By comparing the data before and after calibration, it is clear to note that the two curves (calibrated
and uncalibrated data) overlap throughout the measurement acquisition interval with a slight shift. In addition,
it is obvious to underline that the measurements of the AK8963 magnetometer are faithful to the manufacturer’s
prescriptions; however, they are not correct and present a significant shift in offset specific to each axis. Using
the adopted Merayo ellipsoidal algorithm, the magnetometric measurements are adjusted around the origin
(0,0,0). As a result, the deformed measurements are rectified, which means that the disturbances that are due
to the effects of the errors of non-orthogonality, the scale factor, of hard iron, soft iron, misalignment, and the
external stochastic errors are compensated.

0 1 2 3 4 5 6 7 8 9 10
-26.25
-25.50
-24.75
-24.00
-23.25
-22.50
X
Uncalibrated
[
m
T]
-0.80
-0.78
-0.76
-0.74
-0.72
-0.70
-0.68
X
Calibrated
0 1 2 3 4 5 6 7 8 9 10
57.75
58.50
59.25
60.00
60.75
61.50
Y
Uncalibrated
[
m
T]
0.82
0.84
0.86
0.88
0.90
0.92
0.94
Y
Calibrated
0 1 2 3 4 5 6 7 8 9 10
18.75
19.50
20.25
21.00
21.75
22.50
Time [s]
Z
Uncalibrated
[
m
T]
1.42
1.44
1.46
1.48
1.50
1.52
1.54
Z
Calibrated
Figure 8. Magnetometer data in a static state
0 1 2 3 4 5 6 7 8 9 10
-35
-30
-25
-20
-15
-10
X
Uncalibrated
[
m
T]
-1.0
-0.8
-0.6
-0.4
X
Calibrated
0 1 2 3 4 5 6 7 8 9 10
30
35
40
45
50
55
Y
Uncalibrated
[
m
T]
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Y
Calibrated
0 1 2 3 4 5 6 7 8 9 10
-30
-25
-20
-15
-10
-5
0
5
10
Time [s]
Z
Uncalibrated
[
m
T]
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Z
Calibrated
Figure 9. Magnetometer data in a dynamic state
4. CONCLUSION
In summary, the objective of this work is to apply a simple and practical Merayo algorithm to fit the
magnetometer measurements of a low-cost sensor (AK8963). The ellipsoidal fitting method used, based on
the least-squares method, was implemented and tested to verify the validity of the procedure. After a series
of experiments that were carried out under real geographical conditions, both static and dynamic, an offset
shift was found and the measurements were corrupted by noise from different machining, installation, and
environmental factors. The comparison before and after the calibration showed that the magnetic sensor is
precalibrated at the factory and is stable during static and dynamic measurements. On the other hand, the
software compensation presented above is not sufficient and requires filtering adapted to the frequency of
acquisition of the measurements and the external and internal disturbances of the sensor.
ACKNOWLEDGEMENT
This work was carried out in the frame of the cooperation between the Royal Center for Space Re-
search and Studies (CRERS) and the Mohammed V University in Rabat (UM5R).
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BIOGRAPHIES OF AUTHORS
Aziz El fatimi was born in Kelaa of Sraghnas, Morocco in 1985. He received the Engineer
degree in Electromechanical Engineering in 2015 and the Ph.D. degree in Electrical and Teleommu-
nications Engineering in 2021 from the Ecole Nationale Supérieure d’Arts Métiers of Meknes in
Morocco. He is currently a Ph.D. student in Aerospatial Technologies at the University Center for
Research in Space Technologies at Ecole Mohammadia d’Ingénieurs of Rabat in Morocco. His re-
search is focused on algorithms, modeling and control of multi-physical systems, full-wave analysis,
and microstrip patch antennas. He can be contacted at email: azizelfatimi@research.emi.ac.ma.
Adnane Addaim received his Master degree in 2001 and his Ph.D. degree in 2008, both in
Satellite Communication from Mohammadia School of Engineers (EMI) at Mohammed V University
in Rabat, Morocco. From 2002 to 2003, he was employed at SIEMENS.AG as Transmission Engi-
neer then as responsible of the implementation of the supervision network of the Meditelecom GSM
Microwave Network, in Morocco. He was recipient of the first prize of the best Ph.D. thesis between
2007 and 2009 in the field of Sciences and technologies at Mohammed V University. From 2010
to 2020, he was a professor lecture at ENSA Engineering School at Kenitra, Morocco. From 2021,
he is a full professor at the EMI Engineering School at Rabat, Morocco. His current research in-
terests include signal processing, satellite communication systems wireless and Nanosatellite system
engineering. He can be contacted at email: addaim@emi.ac.ma.
Zouhair Guennoun received the B.Eng. degree in electrical engineering in 1987 from
ULG, Liege Belgium, M.Sc. degrees in Electrical and Computer Engineering in 1993 from École Mo-
hammadia d’Ingénieurs (EMI), Rabat, and the Ph.D. degree in Applied Science in 1996 from EMI. He
is currently a Professor of Computer and electronic Engineering at École Mohammadia d’Ingénieurs
(EMI) where he is member of the ERSC formerly known as LEC, and member of the spatial research
center (CRES). His research interests include wireline and wireless networks, advanced communica-
tions systems, signal processing, audio and image compression, audio cryptography. He is a senior
member of IEEE. He can be contacted at email: zouhair@emi.ac.ma.

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