CALIBRATION OF INERTIAL SENSOR BY USING PARTICLE SWARM OPTIMIZATION AND HUMAN OPINION DYNAMICS ALGORITHM

International Journal Of Instrumentation And Control Systems (IJICS) Vol.7, No.1, January 2017
DOI : 10.5121/ijics.2017.7101 1
CALIBRATION OF INERTIAL SENSOR BY USING
PARTICLE SWARM OPTIMIZATION AND HUMAN
OPINION DYNAMICS ALGORITHM
Vikas Kumar Sinha1
, Avinash Kumar Maurya2
1
Assistant professor, Dept. of Electrical Engineering, MATS University, Raipur,
Chhattisgarh, India
2
Assistant professor, Dept. of Electronics Engineering, G. H. Raisoni College of
Engineering, Nagpur, Maharashtra, India
ABSTRACT
An Inertial Navigation System (INS) can easily track position, velocity and orientation of any moving
vehicle. Generally, deterministic errors are present in an uncalibrated Inertial Measurement Unit (IMU)
which leads to the requirement of an accurate estimation of navigation solution. These inertial sensors,
thus, needs to be calibrated to reduce the error inherent in these systems. By mathematical model of IMU
including both accelerometer and gyroscope is utilized for the purpose of error calibration. Particle
Swarm Optimization (PSO) and Human Opinion Dynamics (HOD) Optimization based calibration
techniques have used to obtain error parameters such as bias, scale factor and misalignment errors.
KEYWORDS
PSO Algorithm, Inertial Measurement Unit, Calibration, HOD Algorithm
1. INTRODUCTION
Today Inertial Navigation System (INS) is used in missile guidance, space navigation, marine
navigation and navigation sensor in cellular mobile phones. Inertial sensor consists of
accelerometers and gyroscopes for three dimension linear and angular motion, respectively.
Initially, when inertial sensor was developed, it was very costly and large in size, but after many
improvements now inertial sensor is available in solid state chip and cheaper. To overcome the
limitations of Global Positioning System (GPS) and for high quality of navigation INS is being
used. A tri-axial Inertial Measurement Unit (IMU), includes a traid of accelerometers and
gyroscopes. Accelerometers are mounted to estimate the velocity and position (linear motion) of
the aircraft or vehicle and gyroscopes are mounted to keep the orientation in the space (to
measure angular motion) [1]. By using accelerometers and gyroscopes the location of any aircraft
or vehicle can be tracked easily. But, in accelerometers and gyroscopes some errors are present
which is inherent to the IMU.
To use Inertial Navigation System properly, calibration is required. Because, IMUs are typically
not compensated initially, present errors increase gradually with time and distance. Errors in
inertial sensor can be classified into two main parts [2]: deterministic or systematic and random

International Journal Of Instrumentation A
or stochastic errors. In recent past, it is studied that, for INS calibration differ
are used such as six-position method, improved six
To calibrate all deterministic errors which are available in INS, these twelve errors are such as
three biases, three scale factors and six m
INS can be calibrated easily without external equipment
In this work of low cost INS calibration, two sophistic optimization techniques are being used
such as Particle Swarm Optimization (PSO) and Human Opinion Dynamics (HOD). PSO is
inspired from natural behavior of birds or animals and HOD is inspire
It may take few minutes to give output during optimization of given fitness function. HOD is
inspired from social behavior and social influence, which has observed from human’s social life.
After completion of these optimization t
as a result, which are required for calibrated IMU.
2. INERTIAL MEASUREMENT
An Inertial Measurement Unit (IMU) is an electronic device that measures velocity,
forces, and angular motion with the help of accelerometers and gyroscopes. An inertial
measurement unit works even when GPS (
any signal from the satellite like as inside the tunnels and bui
electronic signals interference. An inertial measurement unit measures linear and angular motion
by using accelerometer and gyroscope respectively, which are mounted on an IMU
navigation sensor contains a tri-axial
Figure 1: Flow of physical quantity input to measured signals for accelerometers and gyroscopes
It can be seen that, Deterministic and stochastic errors are present in IMU by which getting low
quality navigation from the sensor. In this paper deterministic errors are going to be discussed.
Deterministic errors include, bias, scale factor, misalignment and non
accelerometer and gyroscope respectively as shown in figure 2. Acceler
without linear motion measurable offset has collected and similarly without any angular motion
gyroscope bias offset can be observed. Ideally scale factors must be unity, but if the values of
scale factors are greater than or les
accelerometers and gyroscopes respectively. Accelerometer misalignment errors can be seen if all
three accelerometers are not aligned properly with the reference frame. Similarly gyroscope
misalignment errors also can be detected if all three gyroscopes are not aligned properly with
respective axis.
or stochastic errors. In recent past, it is studied that, for INS calibration different types of methods
position method, improved six-position method and multi-position method.
three biases, three scale factors and six misalignments [3]. After many improvements, low cost
INS can be calibrated easily without external equipment [4].
inspired from natural behavior of birds or animals and HOD is inspired from opinions of human.
After completion of these optimization techniques twelve unknown parameters can be evaluated
as a result, which are required for calibrated IMU.
EASUREMENT UNIT (IMU) SENSOR ERROR MODEL
An Inertial Measurement Unit (IMU) is an electronic device that measures velocity,
measurement unit works even when GPS (Global Positioning System) receiver is not receiving
any signal from the satellite like as inside the tunnels and buildings or in presence of any
by using accelerometer and gyroscope respectively, which are mounted on an IMU
axial IMU.
navigation from the sensor. In this paper deterministic errors are going to be discussed.
Deterministic errors include, bias, scale factor, misalignment and non-orthogonality errors for
accelerometer and gyroscope respectively as shown in figure 2. Accelerometer bias can be seen if
scale factors are greater than or less than the unity, scale factor errors are available in
errors also can be detected if all three gyroscopes are not aligned properly with
January 2017
2
ent types of methods
position method.
. After many improvements, low cost
d from opinions of human.
echniques twelve unknown parameters can be evaluated
ODEL
An Inertial Measurement Unit (IMU) is an electronic device that measures velocity, gravitational
) receiver is not receiving
ldings or in presence of any
by using accelerometer and gyroscope respectively, which are mounted on an IMU [5]. Inertial
navigation from the sensor. In this paper deterministic errors are going to be discussed.
orthogonality errors for
ometer bias can be seen if
s than the unity, scale factor errors are available in
errors also can be detected if all three gyroscopes are not aligned properly with

3
Figure 2: Classification of deterministic or systematic errors for accelerometer and gyroscope
The IMU sensor model represents the measurements from the actual physical quantity to the IMU
sensor output as shown in figure 1. If an IMU sensor is given which is calibrated, its bias, scale
factor and misalignment or non-orthogonality errors are available for generating a true value from
uncalibrated sensor data. Therefore, the accelerometer error model for these deterministic errors
is as followed:
= ( ) ∗ + (1)
Where,
= × (2)
Equation 2.1 can be rearranged in the form:
= ( ) ∗ − (3)
Where
= raw measured output vector (3 1) from sensor for accelerometer =
= scale factor vector (3 3)
= misalignment vector (3 3)
= bias vector (3× 1) =
= true value of sensor measurement vector (3× 1) =
Here scale factor can be written in matrix form in their respective axis , and !:
=
0 0
0 0
0 0
(4)

4
Misalignment error can be represented their respective axis , and ! in matrix form:
=
1 # #
# 1 #
# # 1
(5)
#$% are present misalignments in the sensor of the &'(
sensitive axis of acceleration around the )'(
sensitive axis of the sensor [16].
To estimate the fitness function for accelerometer [7]:
= ( − ) + # * − + + # ( − ) (6)
= # ( − ) + * − + + # ( − ) (7)
= # ( − ) + # * − + + ( − ) (8)
, = -.( )/ + ( )/ + ( )/ − ( 0)/. (9)
In this way from sensor error model deterministic errors have 12 parameters: three for biases,
three for scale factors and six for misalignments or orthogonality errors. These all 12 parameters
can be rewritten in single vector form:
∅ 2 # # # # # # (10)
For accelerometer, after calculating , by Equation 9, deterministic errors can be evaluated.
Similarly, fitness function can be estimated for gyroscope:
3 = 3 ( 3 − 3 ) + #3 3 * 3 − 3 + + #3 3 ( 3 − 3 ) (11)
3 = #3 3 ( 3 − 3 ) + 3 * 3 − 3 + + #3 3 ( 3 − 3 ) (12)
3 = #3 3 ( 3 − 3 ) + #3 3 * 3 − 3 + + 3 ( 3 − 3 ) (13)
,3 = -.( 3 )/ + ( 3 )/ + ( 3 )/ − ( 3)/. (14)
Where, 3 = 7.292115 × 10 9
rad/sec, 3 , 3 and 3 are biases in , :;< ! respective
axis, 3 , 3 and 3 are scale factors in their sensitive axis , :;< ! axis.
#3 , #3 , #3 , #3 , #3 and #3 are misalignment errors of gyroscope.
These errors can be arranged in an array form:
∅3 = 3 3 3 3 3 3 #3 #3 #3 #3 #3 #3 (15)
After estimation of fitness function for given range of parameters of accelerometers and
gyroscopes, optimization techniques can be used for optimization.

5
3. OPTIMIZATION TECHNIQUES
Optimization Technique is an act to find the best fitting parameters for a given minimization
argument. Optimization is a mathematical form which is concerned with finding the maxima or
minima of the fitness functions. Generally fitness functions can be subjected to constraint, which
needs to be provided carefully depending on the knowledge of the range of values that the
unknown variables may have. There are many optimization techniques which are developed to
minimize the cost function or fitness function. Particle Swarm Optimization and Human Opinion
Dynamics technique has explained in section 3.1 and 3.2 respectively.
3.1. PARTICLE SWARM OPTIMIZATION (PSO) METHOD
Particle Swarm Optimization (PSO) was originally published by Kennedy and Eberhart (1995)
[6]. Basically, PSO is inspired by co-operative behavior observation of social animals and birds
in the nature. Compared with other available different type of optimization techniques, PSO is
easier to implement and to get optimized solution.
In PSO optimization technique, suppose numbers of particles are available here with in the
provided rages. Each particle has four attributes including their current position vector ( $), their
local best position ( =0>'), the velocity vector of swarm particles (?$) and best global position
( =0>'). Now the position and velocity of the swarm particles can be updated by [18]:
?$(@ + 1) = A × ?$(@) + B × C$ × ( =0>' $(@) − $(@)) + B/ × C$/ × ( =0>'(@) − $(@)) (16)
$(@ + 1) = $(@) + ?$(@) × ∆@ (17)
Where, & =1, 2, 3 . . . N
A = inertial weight factor of swarm particles
B , B/ = acceleration coefficients
C$ , C$/ = random numbers uniformly distributed with in [0, 1] range
∆@ = interval discrete time (set to 1)
Here A can be calculated from:
A = AE −
(3FGH 3FIJ)×KLMM0N' $'0M '$ON
P $ELE NLE=0M OQ $'0M '$ON
(18)
In Equation 18, AE = 0.9, AE$N= 0.4, where, A can be vary with in the given range and it can
be seen that A is decreasing with the iteration and acceleration factor B = B/=2 [17]. Local best
position of particle swarms can be updated by [19]:
=0>' $(@ + 1) = =0>' $(@); if , * $(@)+ > , * =0>' $(@)+
= $(@ + 1); if , * $(@)+ ≤ , * =0>' $(@)+ (19)
To evaluate =0>'(@):

6
=0>'(@ + 1) will be one of the appropriate =0>' $(@ + 1) which will evaluate minimum fitness
function [19].
=0>'(@ + 1) = arg min , * =0>' $(@ + 1)+ ∀ & ∈ V (20)
Figure 3: The Particle Swarm Optimization algorithm flow chart
In this way, by using PSO optimization method IMU (accelerometer and gyroscope) can be
calibrated by using fitness function from Equation9 and 14, basic PSO optimization technique
can be applied [8]. This iterative process will follow the flow chart of Particle Swarm
Optimization (PSO) algorithm is mentioned in figure 3.
3.2. HUMAN OPINION DYNAMICS OPTIMIZATION METHOD
Human opinions are very important area to get some desired conclusion in social life. Human
opinion dynamics lead to decision making ability in social life. This human opinion concept can
be used to solve complex optimization problem. In real life, it can be seen that, suppose some
human opinions are available and their opinions are influenced with each other than, if any
human opinion is providing most influence to other then that opinion will be considered as most
preferable and given a highest rank over all human opinions. Similarly, if any other human
opinion is influencing lesser then last ranker then it will get lesser rank. In this way, all human
opinion will be sorted in ascending order.
Continuous opinion dynamics optimizer (CODO) can be used to solve complex mathematical
problem, where the basic roots of this algorithm are social structure, opinion space, social
influence and updating rule. Social structure has very important role in this algorithm, because it

7
governs the interaction between two different individual human opinions and among all
individuals.
The second basic important thing is opinion space. In social physics terms, opinions are basically
of two types: discrete and continuous. Discrete opinions can be values such as {0, 1} or {-1, 1}
and continuous could be any real value. Here, continuous opinion will be preferred. W$(@) is
opinion vector at a time t where &= 1, 2, 3 ...N. The opinion vectors should be uniformly
distributed [9].
Social influence is third important rudiment of the algorithm. Each opinion should have decision
making ability. Where opinions are influenced with each-other directly or indirectly and social
influence is the combined effect of these influences. To estimate the social influence, two factors
are responsible, i.e., Social Ranking (SR) of individuals and distance between two different
individuals (d). SR can be calculated by their fitness evaluation in ascending order.
The social influence X$%(@) at a time t of individual ) on individual & can be:
X$%(@) =
YZ[(')
I[(')
(21)
Where <$%(@) is Euclidean distance between individual ) and &. And finally updating rule is one of
the important aspects to be considered in this algorithm. For updating the opinions, various
strategies are adopted as stated in the literatures [10-14]. But, here Durkheimian opinion
dynamics has been used for updating rule. The update rule can be:
∆W$(@) =
∑ (^[(') ^I('))×_I[(')`
[ab
∑ _I[(')`
[ab
+ c$(@) ; & ≠ ) (22)
Where, W%(@) is the neighbors of an individual &. (j=1, 2, 3 ... N) and c$(@) is normally distributed
random noise with mean zero and standard deviation f$(@) at a time t.
Where, S is the strength of disintegrating force of society (S=0.0001, 0.001, 0.01, 0.1, 1, 10, 100
as suitable) and ,$%(@) is the modulus of difference between fitness of the individual ) and &
respectively at a time t. If f$(@) is higher, higher is the tendency of and individual towards
individualization. It means if random noise is generated in Equation 22, then by using this
algorithm it will directly converge and get wrong converging point hence, c$(@) (normally
distributed random noise) has very important role for updating rule. So, c$(@) is referred as
adaptive noise [15]. In this way by using these equations (21, 22 and 23) optimized solution can
be determined as shown in table 1.
f$(@) = × ∑ j QI[(')k
%2 (23)

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Table 1: Flow of algorithm for human opinion dynamics
4. RESULTS
For the tri-axial accelerometer, IMU can be rotated in three-dimensional space and there is no
need to put the IMU at a desired angle or rotation at particular angular speed. By rotating IMU in
space, raw data can be collected which are affected by deterministic errors such as biases, scale
factors and misalignment or non-orthogonalities for one minute and fourty seconds. Model
parameters of accelerometer and gyroscope have mentioned below in table 2 and 3 of PSO
optimization and HOD optimization techniques respectively. In the below tables #3 , #3 ,
#3 , #3 , #3 and #3 are misalignments , and are bias errors, , and are
respectively scale factors for accelerometer and gyroscope.
4.1. TEST ENVIRONMENT
In laboratory Xsens MTi-G-700 INS is available. MTi-G-700 INS includes an onboard GPS
receiver. The MTi-G-700 INS is not only to provide output and GPS-enhanced three dimension
orientation but also provides AHRS (Arithmetic Heading and Reference System)-augmented
position and velocity in three dimension space. Xsens MTi-G-700 INS is very small in size, low
weight, low cost, very flexible with a wide range of interfacing options. Xsens MTi-G-700 INS
gives output even if sensor is rotating in three dimensional. By rotating Xsens MTi-G-700 INS in
three dimension space, uncalibrated data of the sensor can be measured and collected in xlxs
format. These measured data can be direct used in MATLAB as input (uncalibrated data).

9
Figure 4: Experimental arrangement of Xsens MTi-G-700
MTi-G-700 INS is directly connected to computer via USB cable. MT manager software is used
to collect the data and can be displayed in computer's screen in real time. The motion of the
sensor can be linear and angular. The INS output data is decoded at 100 Hz. The output of the
accelerometer will be in m/s2
and output of gyroscope will be in rad/sec.
The mathematical model is developed as a fitness function from the error model of INS and
applied to standard Particle Swarm Optimization and Human Opinion Dynamics optimization
techniques, under MATLAB R2013a environment. According to the proposed methodology,
error model can be calibrated by these two optimization techniques independently to estimate the
unknown parameters.
4.2. RESULTS FOR CALIBRATION USING PSO OPTIMIZATION
After evaluation of error model, PSO optimization technique can be applied on fitness function of
Equation 9 and 14 for accelerometer and gyroscope respectively. Evaluated errors for
accelerometer and gyroscope by PSO has shown below in table 2 and these error parameters can
be arranged in form of array in Equation 10 and 15 for accelerometer and gyroscope respectively.
Table 2: Errors for accelerometer and gyroscope by PSO optimization technique
Error Parameters Accelerometer Gyroscope
32116.3048 32178.8073
31393.2765 33282.2932
31411.0244 33680.8198
0.00627 0.000204
0.00636 0.000449
0.00922 0.000215
# 0.0176 0.0025
# 0.0031 -0.0281
# 0.0008 0.0047
# 0.0225 -0.0123
# -0.0018 -0.0288
# 0.0266 -0.0034

10
4.3. RESULTS FOR CALIBRATION USING HOD OPTIMIZATION
Similarly, Human Opinion Dynamics algorithm can be applied to given fitness function of
Equation 9 and14 of IMU which includes all deterministic errors such as biases, scale factors and
misalignment in respective sensitive axes. After applying HOD algorithm, deterministic errors
can be obtained for accelerometer and gyroscope respectively. In this way, results for a set of
sequence of HOD optimization technique can be obtained by collected raw data of IMU which
are influenced by deterministic errors for accelerometer and gyroscope.
Table 3: Errors for accelerometer and gyroscope by HOD algorithm
Model parameters Accelerometer Gyroscope
32814.6839 32182.3256
32873.7602 31401.4845
32229.5274 32319.6946
0.0071 0.0002
0.0076 0.0003
0.0056 0.0002
# 0.0295 -0.0264
# -0.0285 -0.0015
# -0.0047 -0.0235
# -0.0097 -0.0237
# -0.0007 -0.0072
# -0.0087 -0.0197
It can be seen that twelve error parameters have shown in table 2 and 3 for accelerometers and
gyroscopes by using PSO and HOD optimization techniques. These twelve parameters can be
graphically represented for accelerometers of PSO and HOD optimized values which includes
bias, scale factor and misalignment error parameters as shown in figure 5. Bias errors are in m/s2
,
scale factor are unit less and misalignments are in ̊ degree in figure 5. Similarly graph
representation is shown in figure 6 for gyroscope where, bias errors are in rad/sec, scale factor are
unit less and misalignments are in ̊ degree.
Figure 5: Graph representation of PSO and HOD optimization techniques for accelerometer in respective
axis

11
Figure 6: Graph representation of PSO and HOD optimization techniques for gyroscope in respective axis
5. CONCLUSION
For calibration purpose, two different and unique methods have been adopted. Low cost MEMS
sensors are having large errors compared to higher grade inertial sensors. These deterministic
errors must be calibrated to get acceptable navigation results. This study attempted to test the
performance of the PSO optimization and Human Opinion Dynamics optimization techniques for
IMU calibration. First, Particle Swarm Optimization (PSO) algorithm is applied for calibration
purpose and getting all deterministic error parameters and on the other side Human Opinion
Dynamics optimization algorithm can be applied on error model of IMU for calibration.
From results, it can be seen that, Particle Swarm Optimization and Human Opinion Dynamics
optimization techniques are giving the satisfactory result. However, since this technique does not
require any specific set of orientation sequences to be managed, it avoids any costly equipment
usage and is to be evolved further.
6. FUTURE WORKS
In future, it is aimed to study stochastic error modeling is to be also studied along with the
deterministic error. Furthermore, it is also desired to study few more optimization techniques
other than PSO and HOD which can be applied here in this scenario. And, lastly it is also aimed
in future to combine stochastic and deterministic error modeling in one framework.
REFERENCES
[1] Unsal, Derya, and Kerim Demirbas. "Estimation of deterministic and stochastic IMU error
parameters." Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION. IEEE, 2012.
[2] Nassar, Sameh. Improving the inertial navigation system (INS) error model for INS and INS/DGPS
applications. National Library of Canada= Bibliothèque nationale du Canada, 2005.
[3] Tee, Kian Sek, et al. "Triaxial accelerometer static calibration." (2011).
[4] Fong, W. T., S. K. Ong, and A. Y. C. Nee. "Methods for in-field user calibration of an inertial
measurement unit without external equipment." Measurement Science and Technology 19.8 (2008):
085202.

12
[5] Naranjo, Claudia C. Meruane. "Analysis and modeling of MEMS based inertial sensors." School of
Electrical Engineering, Kungliga Tekniska Hgskolan, Stockholm (2008).
[6] Eberhart, Russ C., and James Kennedy. "A new optimizer using particle swarm theory." Proceedings
of the sixth international symposium on micro machine and human science. Vol. 1. 1995.
[7] Zhang, Liguo, Wenchao Li, and Huilian Liu. "Accelerometer Static Calibration based on the PSO
algorithm." 2nd International Conference on Electronic & Mechanical Engineering and Information
Technology. Atlantis Press, 2012.
[8] Bai, Qinghai. "Analysis of particle swarm optimization algorithm." Computer and information
science 3.1 (2010): 180.
[9] Kaur, Rishemjit, et al. "Human opinion dynamics: An inspiration to solve complex optimization
problems." Scientific reports 3 (2013).
[10] Nowak, Andrzej, Jacek Szamrej, and Bibb Latané. "From private attitude to public opinion: A
dynamic theory of social impact." Psychological Review 97.3 (1990): 362.
[11] Deffuant, Guillaume, et al. "Mixing beliefs among interacting agents." Advances in Complex Systems
3.01n04 (2000): 87-98.
[12] Hegselmann, Rainer, and Ulrich Krause. "Opinion dynamics and bounded confidence models,
analysis, and simulation." Journal of Artificial Societies and Social Simulation 5.3 (2002).
[13] Sznajd-Weron, Katarzyna. "Sznajd model and its applications." arXiv preprint physics/0503239
(2005).
[14] Mäs, Michael, Andreas Flache, and Dirk Helbing. "Individualization as driving force of clustering
phenomena in humans." PLoS Comput Biol 6.10 (2010): e1000959.
[15] Panahandeh, Ghazaleh, Isaac Skog, and Magnus Jansson. "Calibration of the accelerometer triad of an
inertial measurement unit, maximum likelihood estimation and Cramer-Rao bound." Indoor
Positioning and Indoor Navigation (IPIN), 2010 International Conference on. IEEE, 2010.
[16] Cai, Qingzhong, et al. "Accelerometer calibration with nonlinear scale factor based on multi-position
observation." Measurement Science and Technology 24.10 (2013): 105002.
[17] Mahapatra, Prasant Kumar, et al. "Particle swarm optimization (PSO) based tool position error
optimization." International Journal of Computer Applications 72.23 (2013).
[18] Zhi-Jie, L. I., et al. "An improved particle swarm algorithm for search optimization." 2009 WRI
Global Congress on Intelligent Systems. Vol. 1. IEEE, 2009.
[19] Tan, Chin-Woo, et al. "Design of gyroscope-free navigation systems." Intelligent Transportation
Systems, 2001. Proceedings. 2001 IEEE. IEEE, 2001.
[20] Kaplan, Elliott, and Christopher Hegarty. Understanding GPS: principles and applications. Artech
house, 2005.
[21] Savage, Paul G. Strapdown analytics. Vol. 2. Maple Plain, MN: Strapdown Associates,
2000.Maybeck, Peter S. Stochastic models, estimation, and control. Vol. 3. Academic press, 1982.
[22] Godha, Saurabh. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land
vehicle navigation application. Library and Archives Canada= Bibliothèque et Archives Canada,
2006.
[23] Flenniken, W., J. Wall, and D. Bevly. "Characterization of various IMU error sources and the effect
on navigation performance." ION GNSS. 2005.
[24] Grewal, Mohinder S., Lawrence R. Weill, and Angus P. Andrews. Global positioning systems,
inertial navigation, and integration. John Wiley & Sons, 2007.
[25] Chatfield, Averil B. Fundamentals of high accuracy inertial navigation. Vol. 174. Aiaa, 1997.
[26] Titterton, David, and John L. Weston. Strapdown inertial navigation technology. Vol. 17. IET, 2004.
[27] Aggarwal, P., et al. "A standard testing and calibration procedure for low cost MEMS inertial sensors
and units." Journal of navigation 61.02 (2008): 323-336.
[28] Artese, G., and A. Trecroci. "Calibration of a low cost MEMS INS sensor for an integrated navigation
system." Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci (2008): 877-882.
[29] Cheuk, Chi Ming, et al. "Automatic calibration for inertial measurement unit." Control Automation
Robotics & Vision (ICARCV), 2012 12th International Conference on. IEEE, 2012.
[30] Skog, Isaac, and Peter Händel. "Calibration of a MEMS inertial measurement unit." XVII IMEKO
World Congress. 2006.

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Vikas Kumar Sinha received the B.E. degree in Electronics and Telecommunication from
Government Engineering College Bilaspur Chhattisgarh India in 2012 and the M.Tech
degree from Manipal Institute of Technology, Karnataka, India in 2015.He is currently
Assistant Professor in MATS University Raipur, Chhattisgarh, India. He also worked in
CSIR-CSIO Chandigarh as a M.Tech Trainee for 10 months. His specific research interests
include INS calibration techniques, optimization techniques.
Avinash Maurya received the B.E. degree in Electronics and Telecommunication from
Government Engineering College Bilaspur Chhattisgarh India in 2012 and the M.Tech
degree from SGGS IE&T Nanded, Maharastra, India in 2015. He is currently working as
Assistant Professor in G. H. Raisoni College of Engineering, Nagpur, Maharashtra, India.
His research interest is fractional order system and optimization.

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