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Improving the Inertial Navigation System of the CV90 Platform Using Sensor Fusion

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Improving the Inertial Navigation System of the CV90 Platform Using Sensor Fusion ISSN 0280-5316 ISRN LUTFD2/TFRT--5912--SE Improving the Inertial Navigation System of the CV90 Platform Using Sensor Fusion Johan Ambrius Jimmie Jönsson Lund University Department of Automatic Control December 2012 Lund University Document name MASTER THESIS Department of Automatic Control D at e of i ssu e Box 118 January 2013 SE-221 00 Lund Sweden D ocu m en t N u m ber ISRN LUTFD2/TFRT--5912--SE Aut hor (s) Su per v i sor Johan Ambrius Erik Nygård, BAE Systems, Karlskoga, Sweden Jimmie Jönsson Rolf Johansson, Dept. of Automatic Control, Lund University, Sweden (examiner) Sponsoring organization Title and subtitle Improving the Inertial Navigation System of the CV90 Platform Using Sensor Fusion Abst r act The aim of this thesis was to synthesize and evaluate an inertial navigation system (INS) for the Combat Vehicle 90 Platform. The INS that was created utilize sensor fusion in order to combine the different signals coming from the vehicle’s multitude of sensors to estimate the vehicle’s position and heading in some known global reference frame. The CV90 standard INS, the NAV90 system, had performed the task of navigation with an unpredictable behavior due to the fact that it relied on heading estimates from a magnetic compass that is strongly influenced by nearby metallic objects, e.g. other vehicles. It will be demonstrated in this thesis that with a two-axis gyroscope mounted on the weapon’s rotational axis, the position and heading estimate from the INS can continue to provide reliable information even during long periods with without GPS signal reception. K ey w or d s inertial navigation system, Kalman filter, sensor fusion, NAV90, CV90 Classification system and/ or index terms (if any) Su ppl em en t ar y bibliographical information ISSN and key title ISBN 0280-5316 L anguage N u m ber of pages R eci pi en t ’s n ot es English 1-106 Secur i ty cl assi fi cat i on http://www.control.lth.se/publications/ Improving the Inertial Navigation System of the CV90 platform using Sensor Fusion A M.Sc. Thesis Authors Johan Ambrius Jimmie J¨onsson Advisor Erik Nyg˚ard Examiner Rolf Johansson Department of Automatic Control December 2012 Abstract The aim of this thesis was to synthesize and evaluate an inertial navigation system (INS) for the Combat Vehicle 90 Platform. The INS that was created utilize sensor fusion in order to combine the different signals coming from the vehicle's multitude of sensors to estimate the vehicle's position and heading in some known global refe- rence frame. The CV90 standard INS, the NAV90 system, had performed the task of navigation with an unpredictable behavior due to the fact that it relied on heading estimates from a magnetic compass that is strongly influenced by nearby metallic objects, e.g. other vehicles. It will be demonstrated in this thesis that with a two- axis gyroscope mounted on the weapon's rotational axis, the position and heading estimate from the INS can continue to provide reliable information even during long periods with without GPS signal reception. Keywords: inertial navigation system, Kalman filter, sensor fusion, NAV90, CV90 iii Table of Contents Abstract iii List of Figures vii List of Tables ix Acknowledgments1 1 Introduction3 2 The CV90 Platform5 3 Theoretical Background7 3.1 Cartesian Coordinate Systems.........................7 3.2 Euclidean Coordinate Transformations....................8 4 Available Sensors 11 4.1 Compass..................................... 12 4.2 Angle Sensors Turret/Weapon......................... 12 4.3 Odometer.................................... 12 4.4 Two-axis Gyroscope.............................. 13 4.5 GPS receiver.................................. 14 4.6 NAV90...................................... 14 5 A Mathematical Model of CV90 15 5.1 Linearization.................................. 17 5.2 Discretizing the Linearized State Space Equations............. 18 6 Simulink Models 19 6.1 Signal Generator................................ 19 6.2 Gyroscope Model................................ 20 6.2.1 Gyroscope Errors............................ 21 6.2.2 Testing the gyroscope Model..................... 25 6.3 Odometer Model................................ 26 6.3.1 Odometer Errors............................ 27 6.3.2 Testing the Odometer Model..................... 28 6.4 GPS Model................................... 29 6.4.1 GPS Errors............................... 29 6.4.2 Testing the GPS Model........................ 29 7 Data Acquisition from the Real Vehicle 30 7.1 Relevant Data.................................. 30 7.2 Data Extraction................................ 30 v Improving the INS for the CV90 Platform using Sensor Fusion 8 The Kalman Filter 32 8.1 Implementation................................. 32 8.2 Noise Specifications............................... 34 9 Test Cases for the Real Vehicle 36 9.1 Previous Test.................................. 36 9.2 Sk¨ovde, 2012-10-10............................... 37 9.2.1 Test 8 (8)................................ 38 9.2.2 Test 9 (9) and Test 10 (10)...................... 38 9.3 Sk¨ovde, 2012-10-26............................... 39 9.3.1 Test 2 (14)............................... 40 9.4 Sk¨ovde, 2012-11-15............................... 41 9.4.1 Test 1 (19, 20)............................. 43 9.4.2 Test 2 (21, 22)............................. 50 9.4.3 Test 4 (24)............................... 51 9.4.4 Test 5 (25, 26)............................. 53 9.4.5 Test 6 (27, 28)............................. 60 10 Problems that were Encountered 68 11 Conclusions and Recommendations 70 A How to Run Simulations 72 A.1 Real Test Cases................................. 72 A.1.1 About NAV90 Logs.......................... 72 A.1.2 Define a Test Case........................... 73 A.1.3 An Example.............................. 74 A.2 Fictional Test Cases.............................. 75 A.2.1 Define a Test Case........................... 75 A.2.2 An Example.............................. 79 A.3 Optional Run-time Parameters........................ 84 A.4 Plot Results................................... 86 A.5 Simulation Output............................... 87 A.6 Possible Sources of Errors........................... 88 B Animate Raw Sensor Data 89 B.1 I/O........................................ 89 References 91 vi LIST OF FIGURES List of Figures 2 The CV90 Platform5 2.1 CV90 with 3D camouflage, front view. Source: Lars Pihlstr¨om [13]....5 2.2 CV90 with 3D camouflage, front-side view. Source: Lars Pihlstr¨om [14]..6 3 Theoretical Background7 3.1 The 3D Cartesian coordinate system. Source: Jorge Stolfi [17]........7 3.2 Translation and rotation in two dimensions. Source: Per-Erik Bergman [4].9 3.3 Rotation and translation in two dimensions. Source: Per-Erik Bergman [3].9 3.4 Definitions of Roll, Pitch and Yaw. Source: Anja Sch¨onhardt [16]...... 10 4 Available Sensors 11 4.1 Overview of the CV90 sensors. Source: Erik Nyg˚ard[12]........... 11 4.2 Fiber optic gyroscope. Source: D. McFadden [9]............... 13 6 Simulink Models 19 6.1 Simulink model of the gyroscope....................... 20 6.2 Simulink model of the gyroscope error sources................ 21 6.3 Detailed view of the gyroscope Simulink model............... 22 6.4 Bode plot of the low-pass filter effect in the gyroscope............ 23 6.5 Testing the gyroscope model.......................... 25 6.6 Simulink model of the odometer....................... 26 6.7 Testing the odometer model.......................... 28 8 The Kalman Filter 32 8.1 Flowchart of a Kalman filter. Source: Petteri Aimonen [1].......... 33 9 Test Cases for the Real Vehicle 36 9.1 Previous INS test done by BAE Systems. Source: Erik Nyg˚ard [11]..... 36 9.2 Heading, Test 9 (9) and Test 10 (10)...................... 38 9.3 Position, Test 2 (14), with GPS........................ 40 9.4 Position, Test 2 (14), without GPS....................... 41 9.5 Position, Test 1 (19, 20), with GPS...................... 43 9.6 Position, Test 1 (19, 20), without GPS.................... 44 9.7 Detailed position, Test 1 (19, 20), with GPS................. 45 9.8 Detailed position, Test 1 (19, 20), without GPS............... 46 9.9 Error, Test 1 (19, 20), with GPS........................ 47 9.10 Error, Test 1 (19, 20), with GPS........................ 48 9.11 Heading, Test 1 (19, 20), with GPS...................... 49 9.12 Position, Test 2 (21, 22), without GPS.................... 50 9.13 Position, Test 4 (24), with GPS........................ 51 9.14 Position, Test 4 (24), without GPS....................... 52 9.15 Position, Test 5 (25, 26), with GPS...................... 53 vii Improving the INS for the CV90 Platform using Sensor Fusion 9.16 Position, Test 5 (25, 26), without GPS.................... 54 9.17 Detailed position, Test 5 (25, 26), with GPS................. 55 9.18 Detailed position, Test 5 (25, 26), without GPS............... 56 9.19 Error, Test 5 (25, 26), with GPS........................ 57 9.20 Error, Test 5 (25, 26), with GPS........................ 58 9.21 Heading, Test 5 (25, 26), with GPS...................... 59 9.22 Position, Test 6 (27, 28), with GPS...................... 61 9.23 Position, Test 6 (27, 28), without GPS.................... 62 9.24 Detailed position, Test 6 (27, 28), with GPS................. 63 9.25 Detailed position, Test 6 (27, 28), without GPS............... 64 9.26 Error, Test 6 (27, 28), with GPS........................ 65 9.27 Error, Test 6 (27, 28), with GPS........................ 66 9.28 Heading, Test 6 (27, 28), with GPS...................... 67 viii LIST OF TABLES
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