UNIVERSITI PUTRA MALAYSIA

INERTIAL NAVIGATION SYSTEM DATA PROCESSING FOR POSITION DETERMINATION

KHURRAM NIAZ SHAIKH

FK 2005 35

INERTIAL NAVIGATION SYSTEM DATA PROCESSING FOR POSITION DETERMINATION

By

KHURRAM NIAZ SHAIKH

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfilment of the Requirements for the Degree of Master of Science

March 2005

D E D I C A T I O N

I dedicate this research to my parents especially my mother who bore the

absence of her beloved son and prayed all the time for my success and

accomplishment due to which it became possible for me to write this thesis.

ii

Abstract of thesis presented to the Senate of University Putra Malaysia in

fulfilment of the requirement for the degree of Master of Science

INERTIAL NAVIGATION SYSTEM DATA PROCESSING FOR POSITION DETERMINATION

By

KHURRAM NIAZ SHAIKH

March 2005

Chairman: Associate Professor Abdul Rashid Mohammad Shariff, PhD Faculty: Engineering

Locating the positions and mapping the spatial information is of critical

significance in the field of Precision Farming. Global Positioning System

(GPS) is the main tool being utilized for this purpose but it is dependent on

the satellite signals. Unfortunately these signals may get lost due to the

blockage by canopy of the orchards or plantation. Inertial Navigation System

(INS) can address this problem and support the non-availability of GPS

signal for a short time. INS is capable of individually calculating the vehicle’s

position without any external references. However, its high cost and time

dependent errors are its major drawbacks.

The research focuses on the mapping solution by INS only so that it can

provide solution in the absence of GPS signal. Low cost inertial sensor

(Xbow RGA300CA) was used for data collection and processing. Data

Processing was done in Matlab/Simulink environment. A Simulink processing

model is presented in detail to give an insight of the Strapdown INS

Mechanization. Low pass filter and wavelet denoising model was used to

iii

assess the margin of improvement for noise filtering. Accurate GPS

information was used as a reference of comparison.

The model was tested in the lab as well as in the field for its validity. Before

going to the field the Inertial sensor was tested in the lab for yaw rate drift

and for stationary drift. For kinematic field testing, inertial sensor with GPS

was mounted on the vehicle to get the positions for straight trajectories up to

100 meters.

Results obtained are presented in detail. A gradual error growth was

observed in the INS data and the sensor was found to be stable for short

term only. Wavelet denoising was found to be better over short distances up

to 40 meters while low pass filtering showed better performance over longer

distances up to 100 meters.

iv

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains

PEMPROSESAN DATA SISTEM NAVIGASI INERSIA BAGI PENENTUAN

KEDUDUKAN

Oleh

KHURRAM NIAZ SHAIKH

Mac 2005

Pengerusi: Profesor Madya Abdul Rashid Mohammad Shariff, PhD Fakulti: Kejuruteraan

Menentukan kedudukan dan memetakan maklumat spatial sangat kritikal

dalam bidang Pertanian Presisi. Sistem Posisi Global (GPS) ialah sistem

utama yang digunakan untuk tujuan ini, tetapi ia bergantung kepada isyarat

daripada satelit, sedangkan isyarat-isyarat tersebut mungkin hilang akibat

halangan kanopi pokok di dalam ladang. Sistem Navigasi Inersia (INS) boleh

mengatasi masalah ini dan boleh menampung ketiadaan isyarat GPS bagi

masa yang singkat. INS mampu mengira kedudukan kenderaan tanpa

rujukan luaran. Tetapi kosnya yang tinggi dan ralatnya yang bergantung

pada masa adalah kelemahan sistem ini.

Oleh itu penyelidikan ini tertumpu kepada penyelesaian pemetaan

menggunakan INS agar ia dapat menghasilkan penyelesaian tanpa isyarat

GPS. Penderiaan Inersia berkos rendah (Xbow RGA300CA) digunakan

untuk pengumpulan dan pemprosesan data. Permprosesan data dibuat di

dalam Matlab/Simulink. Sebuah model pemprosesan Simulink

dipersembahkan dengan terperinci untuk menjelaskan Mekanisasi INS

v

“Strapdown model”. Tapisan laluan rendah dan “wavelet denoising” telah

digunakan untuk menilai tahap pembaikan bagi “noise filtering”. Maklumat

GPS yang jitu telah digunakan sebagai rujukan bandingan.

Model ini telah diuji di dalam makmal dan juga di lapangan bagi menentukan

keberkesananya. Sebelum deria inersia dibawa ke lapangan, ianya terlebih

dulu diuji didalam makmal bagi menentukan kadar halaju pecutan dan kadar

pemberhentian. Bagi tujuan ujian kinematik, deria inersia dan GPS diletakkan

di atas kenderaan untuk mendapatkan posisi bagi projector laluan lurus

sehingga 100 meter.

Keputusan yang didapati daripada ujikaji ini telah diterangkan dengan

lengkap. Peningkatan ralat yang biasa telah diperhatikan di dalam data INS

dan deria inersia itu didapati stabil dalam jangka waktu yang pendek sahaja.

“Wavelet denoising” telah dedapati lebih berkesan bagi jarak dekat, sehingga

40 meter, manakala tapisan aluan rendah menampakkan performasi yang

lebih baik bagi jarak melebihi 100 meter.

vi

A C K N O W L E D G E M E N T S

I would like to express my appreciation and gratitude to my supervisor Dr.

Rashid Shariff for his continuous trust and support throughout my master’s

research. I would also like to thank for his careful review, corrections and

suggestions for the improvement of this thesis.

Special thanks to Dr. Farrukh Nagi for his precious time, help to get used to

the Matlab software and guidance during the lab experiments at College of

Engineering, University Tenaga National (UNITEN).

I gratefully appreciate the advice and suggestions of Dr. Raul Dorobantu from

Technische Universität München, Institut für Astronomische und

Physikalische Geodäsie, München, Germany, who always gave a quick reply

of email and shared his vast experience in the field of geomatics. The help of

Mr. Riduan Mohd. Junusi, Mr. Ng Eng Boon and Mr. Tan Chye Hee

(Graduate students at the department of Biological and Agricultural

Engineering) during the kinematic testing surveys is gratefully acknowledged

without whose help it would not have been possible to finish this research

within the stipulated time frame.

I am also grateful to Mr. Zakaria Ghazali (Technical Officer at Department of

Civil Engineering) for his help and sharing of experience in the use of survey

equipments.

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I certify that an Examination Committee met on date of viva to conduct the final examination of Khurram Niaz Shaikh on his Master of Science thesis entitled “Inertial Navigation System Data Processing For Position Determination” in accordance with Universiti Putra Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the candidate be awarded the relevant degree. Members of the Examination Committee are as follows:

Abdul Rahman Ramli, PhD Associate Professor Faculty of Graduate Studies Universiti Putra Malaysia (Chairman) Ahmed Rodzi, PhD Associate Professor Faculty of Graduate Studies Universiti Putra Malaysia (Member) Hj Mohd Amin Mohd Som, PhD Professor Faculty of Graduate Studies Universiti Putra Malaysia (Member) Mustafa Din bin Subari, PhD Associate Professor Faculty of Graduate Studies Universiti Putra Malaysia (Independent Examiner)

______________________________________

GULAM RASOOL RAHMAT ALI, PhD Professor/Deputy Dean School of Graduate Studies Universiti Putra Malaysia

Date:

viii

This thesis submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfillment of the requirement for the Degree of Master of Science. The members of the Supervisory Committee are as follows:

Abdul Rashid bin Mohammad Shariff, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman) Hishamuddin Jamaluddin Lecturer Faculty of Engineering Universiti Putra Malaysia (Member) Shattri Mansoor, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Member)

__________________________

AINI IDERIS, PhD Professor/Dean School of Graduate Studies Universiti Putra Malaysia Date:

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D E C L A R A T I O N

I hereby declare that the thesis is based on my original work except for quotations and citations, which have been duly acknowledged. I also declare that it has not been previously or concurrently submitted for any other degree at UPM or other institutions.

_____________________

KHURRAM NIAZ SHIAKH

Date:

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TABLE OF CONTENTS

Page DEDICATION iiABSTRACT iii ABSTRAK v ACKNOWLEDGEMENTS vii APPROVAL viii DECLARATION x LIST OF TABLES xiii LIST OF FIGURES xiv LIST OF ABBREVIATIONS xvii

CHAPTER

1 INTRODUCTION 1.1 1.1 Background 1.1 1.2 Research Problems 1.4 1.3 Goals and Objectives 1.5 1.4 Summary of Methodology 1.6 1.5 Scope of Study 1.7 1.6 Summary of Results Obtained 1.8 1.7 Previous Work 1.9 1.8 Thesis Outline 1.10

2 LITERATURE REVIEW 2.1 2.1 Global Positioning System (GPS) 2.1

2.1.1 Differential GPS (DGPS): 2.2 2.1.2 GPS Error Sources: 2.3

2.2 Inertial Navigation System (INS) 2.4 2.2.1 Strapdown Inertial Navigation Systems (SINS) 2.9 2.2.2 Strapdown INS Errors 2.11 2.2.3 Types of Inertial Sensors 2.13 2.2.4 Coordinate Transformation 2.18

2.3 Mobile Mapping System (MMS) 2.19 2.4 GPS/INS Integration 2.20 2.5 Concluding Remarks 2.23

3 METHODOLOGY 3.1 3.1 Hardware Configuration 3.1 3.2 Testing Setup and Data Collection 3.3

3.2.1 Stationary Lab Testing 3.3 3.2.2 Yaw Rate Lab Testing 3.4 3.2.3 Kinematic Field Testing Setup (Survey 1) 3.5 3.2.4 Kinematic Field Testing Setup (Survey 2) 3.8

3.3 Data Processing and Simulation 3.9

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3.3.1 Data Post Processing 3.9 3.3.2 Matlab m-file script for the bias compensation 3.11 3.3.3 INS Mechanization and Simulation 3.13

4 RESULTS AND ANALYSIS 4.1 4.1 INS Noise Filtering 4.1 4.2 Static Testing Result 4.9 4.3 Yaw Rate Testing Result 4.12 4.4 Kinematic Field Testing Result 4.16

4.4.1 Field Survey 1 4.16 4.4.2 Field Survey 2 4.19

5 CONCLUSION AND RECOMMENDATION 5.1 5.1 Conclusion 5.1 5.2 Recommendation 5.2 5.3 Future Work 5.3

REFERENCES R.1 BIODATA OF THE AUTHOR B.1

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L I S T O F T A B L E S

Table Page

1 Benefits of GPS/INS Integration 2.22

2 System Specifications 3.3

3 Static Testing Result (Mean) 4.10

4 Static Testing Data (Complete) 4.11

5 Yaw Rate Lab Testing Result (Mean) 4.14

6 Yaw Rate Lab Tesing Data (complete) 4.15

7 Field Survey 1 Result 4.17

8 Field Survey 2 Result (Mean) 4.21

9 Field Survey 2 Data (complete) 4.22

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L I S T O F F I G U R E S

Figure Page 2.1 GPS Constellation Source 2.2

2.2 Differential GPS (DGPS) 2.2

2.3 Sensor Axis on Vehicle 2.6

2.4 Chart of Accuracy and Cost 2.7

2.5 Strapdown Inertial Navigation System 2.10

2.6 Flow chart of Strapdown INS 2.10

2.7 Ring Laser Gyro (RLG) (King, 98) 2.14

2.8 Fiber Optic Gyro (FOG) (Handrich, 2003) 2.14

2.9 MEMS Inertial Sensors (Barbour et al, 2003) 2.16

2.10 Evolution of Gyro Technologies (Handrich, 2003) 2.17

2.11 INS Cost as a function of Instrument Technology (Schmidt, 2003) 2.17

2.12 GPS/INS Block Diagram 2.23

3.1 Leica GPS System 500 3.2

3.2 Trimble GPS Pathfinder Pro XRS 3.2

3.3 Xbow RGA300CA Inertial Sensor 3.2

3.4 Lab Testing Setup (Yaw Rate) 3.4

3.5 Yaw rate testing model 3.5

3.6 Field Data Collection Method 3.6

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3.7 Field Survey 1 3.7

3.8 Field Survey 2 3.7

3.9 Acceleration Offset Compensation 3.11

3.10 Simulink INS Mechanization Model 3.16

3.11 Simulink subsystem of Euler Angles and DCM 3.17

3.12 Simulink subsystem of Euler Angle to Direction Cosine Matrix transformation 3.17

3.13 Wavelet Denoising Model 3.18

3.14 Low Pass Filter Design 3.18

3.15 Acceleration , Velocity and Postion 3.19

4.1 INS Data Filtering Approaches 4.2

4.2 INS Noise Observed in Static Mode 4.3

4.3 Low Pass Filtering Vs Wavelet Denoising 4.5

4.4 The Concept of Filtering Short Term Noise (Skaloud, 1999) 4.5

4.5 Wavelet Denoising 4.6

4.6 Noise Filtering Analysis Block Diagram 4.7

4.7 Low Pass Filtering and Wavelet Denoising Residuals Comparison 4.8

4.8 Static Error Plot 4.10

4.9 Angular Yaw Rate 4.13

4.10 Yaw Rate Error Plot 4.14

4.11 Error plot against distance by Low Pass Filtering technique 4.17

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4.12 Error plot against distance by Wavelet denoising technique 4.18

4.13 GPS and INS Trajectory (50m) 4.18

4.14 Field Survey 2 Error Plot 4.21

xvi

L I S T O F A B B R E V I A T I O N S

DCM Direction Cosine Matrix

DTG Dynamically Tuned Gyro

EU European Union

FOG Fiber Optic Gyro

GNSS Global Navigation Satellite System

GPS Global Positioning System

IFOG Interferometric Fiber Optic Gyro

IMU Inertial Measurement Unit

INS Inertial Navigation System

JUPEM Jabatan Ukur Pemetaan Malaysia

(Department of Survey and Mapping Malaysia)

MEMS Micro-Electro-Mechanical Systems

MMS Mobile Mapping System

MPOB Malaysian Palm Oil Board

RGA Rate Gyro Accelerometer

RLG Ring Laser Gyro

SINS Strapdown Inertial Navigation System

WGS World Geodetic System

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C H A P T E R 1

I N T R O D U C T I O N

1.1 Background

Humans have been trying to figure out the position on earth since stone age

so that we can use that information to know where we are and where we are

going. Today, the strife has brought us to the technological advancement that

has made things much easier and with the help of innovative technology and

computing power we have been working hard to achieve accuracy with

higher precision. Today, there is a wide range of navigation sensors available

that not only can pin point the location on earth but also can keep track of the

navigation information with velocity. In geomatics engineering sense,

navigation is understood as quasi-continuous positioning of a moving object.

Thus, we often encounter expressions such as "GPS-navigation". In fact, the

task of navigation is much more complex than just positioning as it includes

the decision making as well as steering of the moving vehicle (OmarBashich,

1998).

Global Positioning System (GPS) is the most popular navigation system in

use today. But its limitations open doors for an autonomous navigation

system such as Inertial Navigation System (INS). A decade ago inertial

sensors were only limited to aerospace and military applications due to their

high cost and restrictions by the government but now due to the reducing

cost they are finding their way in civil applications such as mobile tracking,

precision surveying, and precision farming.

1.1

INS (Inertial Navigation System) was originally developed by the military

about 20 - 25 years ago when they sought a self-contained system that didn't

rely on outside contact. Other systems at that time needed land-based

beacons and transmitter stations. It is the most complex and expensive

navigational aid. INS has been the system of choice for many years by the

military and commercial airlines. It is extremely accurate, not affected by

external factors and being independent of outside communication, it can

operate worldwide. INS uses a sophisticated form of dead reckoning. It starts

from a known position and calculates further positions from the acceleration

of the aircraft. This gives accurate data on speed and change of direction to

determine a new position. To detect changes in velocity, the INS uses

accelerometers (3 of them, mounted east-west, north-south and vertically)

(Education Queensland, 2005).

The difference between the INS for land vehicles (low dynamics) and high

dynamics is that high dynamics platforms such as Aircraft, satellites etc need

to have angles measured at all three angles accurately to give three-

dimensional accuracy. The navigation accuracy and reliability requirements

for a ballistic missile and that of an autonomous vehicle are not that different.

Both require high precision navigation solutions, in some cases that of the

autonomous vehicle is down to centimeters, and both require the system to

provide this data reliably. The major difference is the duration requirement to

which the INS is allowed to function without any external aiding, a function of

the accuracy required. For civilian applications this is quite short, in the order

of seconds, because some sort of external aiding can be used. The goal

1.2

however is to provide the navigation solution from the INS for as long as

possible without external aiding. This is due to two main reasons: 1) aiding

information cannot be guaranteed to come in at fixed intervals, and 2) in any

autonomous vehicle navigation fault detection is paramount and this requires

accurate navigation solutions from individual systems. The cost of an Inertial

Measurement Unit (IMU) governs its accuracy (an IMU is a sensor package

which provides the raw vehicle dynamic data from which the final navigation

solution is determined). In general, civilian applications require low cost

IMUs. These units however pose significant errors, which in turn cause

navigation solutions to drift significantly with time (Sukkarieh, 2000).

1.3

1.2 Research Problems

No one denies the accuracy of GPS with the increased constellation of

satellites and improved methods of data processing but GPS is still

dependent on the satellite signal. Today, the surveyors are having problems

of accuracy in places where the signal gets lost due to blockage by buildings,

canopy, and other obstructions. Many of the studies are being carried out to

address the issue of signal loss. Inertial Navigation System (INS) is one of

the most popular navigation systems that can provide an autonomous

solution in case of signal loss.

Accurate and high performance inertial sensors are still not feasible for use in

land applications due to high cost constraints and restrictions by the

government. This research strives to utilize a low cost inertial sensor in an

optimum manner to get the position information. These low cost inertial

sensors can experience large positioning errors over short time due to low

quality of gyros and accelerometers. The software (Gyroview) that comes

with these inertial sensors only logs the raw accelerations. These raw

accelerations can not be used for mapping unless converted to positions.

The sensor used in this research is a low cost inertial sensor (RGA300CA)

that outputs raw accelerations with lots of noise in it that need to be filtered to

get accurate position information. Therefore an approach was initiated to

build a simulation algorithm do the processing of INS raw accelerations and

angles by removing the noise and converting them to positions.

1.4

1.3 Goal and Objectives

The major goal of this research was to get accurate coordinate trajectory

information from low-cost strapdown inertial navigation systems so that it can

be used as a standalone navigation system to support the short GPS

outages during mobile mapping by land vehicle.

The specific objectives to achieve this goal were:

1. To develop INS data processing algorithm in Simulink (Matlab) to get

output in terms of positions.

2. To remove the Noise from the output by testing and comparing two

filtering techniques, viz. Low Pass Filtering and Wavelet Denoising.

3. To investigate the navigation accuracy of the RGA Inertial Sensor with

accurate GPS information.

1.5

1.4 Summary of Methodology

Inertial sensors output accelerations and angles but the mapping information

needs positions. Therefore, the output given by inertial sensors need to be

processed to get the information in terms of positions. A low cost inertial

sensor (RGA300CA) from Xbow Inc. USA was used to collect and process

the data. To attain the objective of INS data processing Matlab/Simulink

software was used because of its strong computing capabilities and ease of

use due to built-in blocks in the Simulink library. The output given by the RGA

inertial sensor contains substantial noise that needs to be filtered. The

software used to log the accelerations from RGA inertial sensor is Gyroview

version 2.4 that comes with the RGA Inertial Sensor but it is not capable of

processing the accelerations to convert them to positions. The data is saved

in text format that was later loaded in Matlab workspace as individual

variables of three accelerations and three axis angular information. By

integrating the accelerations once gives velocities and integrating again gives

positions.

These accelerations multiplied by the angular information give attitude

corrections. However, the noise in the RGA inertial sensor data makes output

inaccurate and requires a proper noise filtering technique. Two noise filtering

techniques: Low pass filtering and Wavelet denoising were tested and

compared for accuracy.

The simulink processing model was tested for validity by conducting tests in

the lab as well as in the field. Stationary lab testing and yaw rate lab testing

1.6

was conducted to check the error growth with respect to time. Kinematic

testing was conducted by mounting the inertial sensor over the vehicle and

taking the measurements from the distances of 10 to 100 meters. These

measurement once processed were compared with accurate DGPS

information. Chapter three on methodology discusses the research approach

in detail comprising the data processing method and testing setup.

1.5 Scope of Study

The research focuses on the processing of INS data using RGA300CA

inertial sensor (Figure 3.3) from Xbow Inc. USA and to develop an algorithm

(model) to convert the INS output (accelerations and angles) to obtain

position information. The term ‘INS data model’ is used to refer INS data

processing simulation algorithm in Simulink (Matlab). This model gets the

coordinates (positions) from the inertial sensor output (accelerations and

angles). The model includes the noise filtering algorithms (Low Pass filter

and Wavelet denoising), however this research does not focus on noise

filtering, perse It is beyond the scope of this research to go into the details of

the noise filtering, noise filtering is itself a big area and needs focused

research to improve the results. It is the noise found in the INS data that led

us to do the noise filtering and we tried two techniques to suppress the noise

to get the meaningful result from INS data. Filtering algorithms were ‘taken’

from Matlab help manuals and were used in the model, and the ‘INS data

processing model’ was developed. This research does not go into the details

of hardware design and configuration. It also does not deal with the details of

GPS/INS integration. Field surveys were limited to the distances of up to 100

1.7