Download Context Aware High Dynamics GNSS-INS for Interference Mitigation PDF

TitleContext Aware High Dynamics GNSS-INS for Interference Mitigation
File Size5.3 MB
Total Pages218
Table of Contents
                            Ahmed Mohsen Mohamed Kamel
Context Aware High Dynamics GNSS-INS for Interference Mitigation
Ahmed Mohsen Mohamed Kamel
List of Figures and Illustrations
Chapter One: Introduction
	1.1 Background and Motivation
Figure ‎1-1: Guidance, Navigation and Control (GNC) processes
Figure ‎1-2: Missile Guidance system classification
Figure ‎1-3: Midcourse cruise missile guidance using GNSS
Figure ‎1-4: Guided munitions accuracy
	1.2 Literature Review and Limitations of Previous Work
Table ‎1-1: Types of interference and typical sources (Kaplan & Hegarty 2006)
	1.3 Review on Interference Mitigation Techniques
Figure ‎1-5:  Overview of various interference mitigation techniques (Shanmugam 2007)
	1.3.1 Spatial Interference Mitigation Technique
Figure ‎1-6: GPS antenna beam forming to avoid non-LOS jammers (Malmstrom 2003)
	1.3.2 Temporal Interference Mitigation Technique Pre-correlation temporal processing Post-correlation temporal processing
Table ‎1-2: Interference mitigation techniques against several interference types
	1.4 Integration of GPS and INS
Figure ‎1-7: Block diagram illustration of a loosely coupled integration algorithm
Figure ‎1-9: Block diagram of an ultra-tightly coupled integration algorithm
	1.5 Research Objective and Contributions
	1.6 Thesis outline
Chapter Two:  GPS Tracking Theory and Limitations
	2.1 GPS Signal Structure
Figure ‎2-1: Spectrum representation of GPS signals
Table ‎2-1: GPS Frequencies and Usage
	2.2 Carrier and Code Tracking
Figure ‎2-2: Generic Carrier and Code Tracking Loops
Figure ‎2-3: Carrier phase tracking loop
Table ‎2-2: Commonly Used Discriminators
	2.3 Filter Design
Table ‎2-3: Loop Filter Characteristics
Figure ‎2-4: PLL 3rd order analog loop filter
Figure ‎2-5: Digital bilinear z-transform integrator
Table ‎2-5: Typical values of filter gains
Figure ‎2-6: Digital FLL-assisted-PLL using bilinear z-transform integrator
	2.4 PLL Measurements Error Sources and limitations
		2.4.1 Thermal Noise Effect on Tracking Accuracy
Figure ‎2-7: PLL thermal noise jitter at PIT = 1 ms
Figure ‎2-8: PLL thermal noise jitter at PIT = 20 ms
Figure ‎2-9: PLL thermal noise jitter at BW = 1 Hz
Figure ‎2-10: PLL thermal noise jitter at BW = 18 Hz
	2.4.2 Dynamic Stress Error Effect on Tracking Accuracy
Table ‎2-6: Loop filters dynamic response steady state error
Figure ‎2-11: Max. acc. stress for 2nd order PLL to maintain certain phase errors
Figure ‎2-12: Max. Jerk stress for 3rd order PLL to maintain certain phase errors
	2.5 FLL Measurements Error Sources and Limitations
Figure ‎2-16: Frequency jitter calculated with PIT = 1 ms
Figure ‎2-17: Frequency jitter calculated with PIT = 10 ms
Figure ‎2-18: Tradeoff of loop bandwidth, PIT, tracking jitter and dynamic stress
	2.6 Parameters Used to Describe the Dynamic Performance of Tracking Loop
		2.6.1 Tracking Robustness
		2.6.2 Pull-in Frequency
		2.6.3 Transient Time Response
Figure ‎2-19: 3rd order loop filter step response for four bandwidths
Figure ‎2-20: Relationship between bandwidth and rise time
	2.7 Interference Effect on GPS Signal Tracking
	2.8 GPS Signal Tracking Using Kalman Filter Based PLLs
		2.8.1 Introduction to Kalman Filter
Figure ‎2-22: Kalman filter process
	2.8.2 Kalman Filter Based PLLs
Figure ‎2-24: Kalman filter based PLL
	2.9 Summary
Chapter Three:  Design of Fuzzy Tracking System
	3.1 Introduction
Figure ‎3-1: Fuzzy membership functions
Figure ‎3-2: Fuzzy inference process
	3.1.1 Fuzzy Sets
Figure ‎3-3: Crisp and fuzzy sets
Table ‎3-1: Commonly used membership functions
Figure ‎3-4: Commonly used membership functions
	3.1.2 Construction of a Fuzzy Inference System (FIS)
(i) Conjunctive system of rules:
(ii) Disjunctive system of rules:
Figure ‎3-5: Defuzzification using centroid or centre-average method
Figure ‎3-6: An example of two input-one output Mamdani FIS
	3.1.3 Relation Between Fuzzy and Probability
Furthermore if   equals one as in classical set theory, then
	3.2 Design of a GPS Signal Fuzzy Tracking System
		3.2.1 Introduction to PLL Fuzzy Tracking
Figure ‎3-7: FFPLL filter construction
Figure ‎3-8: Hardware configuration used for GPS signal generation and testing
	3.2.2 Membership Functions Design
Table ‎3-2: Distribution of fuzzy membership functions
Figure ‎3-10: Phase membership functions
Figure ‎3-11: Frequency membership functions
Figure ‎3-12: NCO tuning frequency membership functions
	3.2.3 Fuzzy Rules Design
Figure ‎3-13: Illustration of signal phase tracking
Figure ‎3-14: Receiver motion and manoeuvre
Figure ‎3-15: Receiver dynamic profile
Figure ‎3-16: Estimated Doppler during tracking of PRN 5
Figure ‎3-18: PD output
Figure ‎3-20: Relation between θ and δf at different BWs
Figure ‎3-21: Linear relation between BW and PLL filter noise gain
Figure ‎3-22: θ-δf linear relation at different Bandwidths
In general, the fuzzy rules relating all the linguistic variables can be expressed then as
Table ‎3-3: Fuzzy rules
Figure ‎3-23: Fuzzy control surface
	3.2.4 Interference Effects and Online Adaptation
	3.3 Summary
Chapter Four:  Performance Assessment of FFPLL
	4.1 Introduction
Table ‎4-1: Summary of tests types
Table ‎4-2: Algorithms used for comparative analysis of GPS tracking loops
	4.2 High Dynamics and Bandwidth Effects on Tracking Accuracy
		4.2.1 Scenario Design
Figure ‎4-1: Receiver dynamics profile
The scenario comprises first a static period of around 50 seconds and then the total missile velocity is increased gradually to reach 450 m/s while performing sharp manoeuvres that reach up to 15 g. The manoeuvres are also associated with high and su...
Figure ‎4-2: Receiver 3D motion plot
	4.2.2 Tracking Results
Table ‎4-3: Doppler convergence times using different algorithms
	4.2.3 Interference Effect on Tracking Accuracy
Figure ‎4-10: Sky plot of the GPS satellites in view during experiment
Figure ‎4-11: Effect of increasing J/S on C/N0 level changes for PRN 23
Figure ‎4-16: Phase Lock Indicator calculated for the five tracking algorithms used for PRN 23
	4.3 Combined Interference and High Dynamics Scenarios
Figure ‎4-17: Receiver dynamics profile
The same scenario is repeated five times with different CW interference powers. Due to missile high dynamics, narrow bandwidth PLL or FLL-assisted-PLL was not able to provide continuous signal tracking and loss of lock occurred, which is consistent wi...
Figure ‎4-18: 3D plot of the missile manoeuvres near an interference source
	4.4 Effect of Predetection Integration Time on Tracking Robustness and Continuity
		4.4.1 Effect of Integration Time on Estimated Doppler Jitter
Figure ‎4-24: FFPLL Doppler calculated for PRN 12 using different integration times
	4.4.2 Effect of Integration Time on Dynamic Robustness
Figure ‎4-26: Missile manoeuvring trajectory
Figure ‎4-27: Missile dynamics profile
The collected signal is processed using FFPLL and standard PLL with different bandwidths and PITs. A standard PLL with minimum bandwidth of 10 Hz and PIT of 1 ms is empirically found to be sufficient to provide continuous signal tracking through the w...
	4.5 Pull-in Frequency Calculation
Figure ‎4-29: PRN 24 acquisition results
	4.6 Summary
Chapter Five:  Design and Testing of an INS Assisted FFPLL
	5.1 INS Assisted FFPLL Design
		5.1.1 Calculating INS Doppler
Figure ‎5-1: Standard carrier tracking loop using INS Doppler aiding
Figure ‎5-2: Strap-down mechanization blocks
Secondly, the INS Doppler (fd-INS) is derived from Equation  using
	5.1.2 Design of Reduced FFPLL Using INS Assistance
Figure ‎5-3: INS assisted FFPLL
	5.2 Test Description
		5.2.1 Simulator Tests
		5.2.2 Experimental Test Description
Figure ‎5-9: GPS antenna mounted on the SUV roof top and IMU with other equipments inside the vehicle
Figure ‎5-10: Test Setup
	5.3 GPS/INS Reference Solution
Table ‎5-3: HG1700 IMU system specifications
	5.3.1 INS Doppler
	5.3.2 RF Data
	5.3.3 Data Collection Site
	5.4 Data Processing and Results
Table ‎5-4: Algorithms used for comparative analysis of GPS tracking loops
Figure ‎5-18: Estimated Doppler for PRN 16 using KF+INS, FFPLL, and INS+FFPLL
Figure ‎5-19:  Estimated Doppler for PRN 13 using KF+INS, FFPLL, and FPLL+INS
	5.5 Summary
Chapter Six: Design of Dual Frequency FFPLL and System Integration
	6.1 Introduction
	6.2 GPS L2C Signal Overview
Figure ‎6-1: GPS L2C signal structure
	6.3 FFPLL L2C CM Code Tracking and Aiding
Figure ‎6-3: Integrated INS/L2C/L1 FFPLL
	6.4 Test Results
Figure ‎6-4: L1 and L2 Doppler values estimated for PRN 5
Figure ‎6-5: L1 and L2 C/N0 estimated for PRN 5
	6.5 System Integration
	6.6 Summary
Chapter Seven:  Conclusions and Recommendations
	7.1 Conclusions
		7.1.1 System Design and Test Conclusions
		7.1.2 INS Integration
		7.1.3 L2C Aiding
	7.2 Recommendations for Future Work
Document Text Contents
Page 1

UCGE Reports
Number 20332

Department of Geomatics Engineering

Context Aware High Dynamics GNSS-INS for
Interference Mitigation



Ahmed Mohsen Mohamed Kamel

August, 2011

Page 109


Figure 3-16: Estimated Doppler during tracking of PRN 5

Figure 3-17: (a) Processed filter output (b) Reference filter output (c) Residual noise

δf [(a)-(b)]

Page 110


Figure 3-18: PD output

After noise calculation, the relation between δf and θ is calculated and fitted as a linear

function as can be seen in Figure 3-19. The fitted line slope is dependent on BW and can

be defined as the filter noise gain. The same relation is calculated for the same scenario

but for different bandwidths ranging from 2 Hz to 18 Hz. Figure 3-20 shows the θ-δf

relation for different selected BWs. The relation between the BWs and calculated noise

gains is also fitted as a linear function as shown in Figure 3-21. The θ-δf relation can then

be formulated, as follows and further used to estimate the corresponding fuzzy BW:

 2.7 0.42f BW   . (3.15)

Page 217


Progri, I. F. (2006) "GPS L5 signal acquisition and tracking under unintentional
interference or jamming," Monterey, CA, United states, 112-121, Institute of Navigation

Psiaki, M. L. (2001) "Smoother-Based GPS Signal Tracking in a Software Receiver,"

, Sept. 11-14, 2001, Salt Lake City, Utah, pp. 2900-2913

Psiaki, M. L., T. E. Humphreys, A. P. Cerruti, S. P. Powell, and J. Paul M. Kintner
(2007) "Tracking L1 C/A and L2C Signals through Ionospheric Scintillations,"

, Sept. 25-28, 2007, Fort Worth, TX, pp. 246-268

Psiaki, M. L., and H. Jung (2002) "Extended Kalman Filter Methods for Tracking Weak
GPS Signals," , 24-27 September 2002, Portland, OR

Qaisar, S. U. (2009) "Performance Analysis of Doppler Aided Tracking Loops in
Modernized GPS Receivers," , 22-25 September Savannah, GA

Razavi, B. (1996)

, Institute of Electrical and Electronics Engineers, INC., New York

Ross, T. J., J. M. Booker, and W. J. Parkinson (2002)

, American Statistical Association Society for Industrial and Applied
Mathematics, Alexandria, Virginia

Salem, D. R. (2010) ,
Geomatics Engineering, University of Calgary, Calgary

Saunders, S. R., and A. A. Zavala (2007)

, John Wiley & Sons Ltd.

Shanmugam, S. K. (2007) "Narrowband Interference Suppression Performance of Multi-
Correlation Differential Detection," , May 29-31,
Geneva, Switzerland

Simon, D., and H. El-Sherief (1994) "Fuzzy phase-locked loops," Las Vegas, NV, USA,
252-259, Publ by IEEE

Siouris, G. M. (2004) , Springer

Spirent (2006) "Signal Generator Hardware User Manual," Spirent Communications

Titterton, D. H., and J. L. Weston (2004) , 2nd
Edition Ed., The Institution of Electrical Engineers & The American Institute of
Aeronautics and Astronautics

Page 218


Unglaub, R. A. G., and C.-S. Tsang (1999) "Phase tracking by fuzzy control loop," in

57-66 vol.55

Ward, P. (1998) "Performance Comparisons Between FLL, PLL and a Novel FLL-
Assisted-PLL Carrier Tracking Loop Under RF Interference Conditions," in

,September 1998, Nashville, TN, pp. pp. 783–795

Ward, P. W. (1994) "GPS receiver RF interference monitoring, mitigation, and analysis
techniques," , vol 41, no 4, pp. 367-391

Wendel, J., and G. F. Trommer (2004) "Tightly coupled GPS/INS integration for missile
applications," , vol 8, no 7, pp. 627-634

Zadeh, L. A. (1965) "Fuzzy Sets," , vol 8, no 338-353,

Zoltowski, M. D., and A. S. Gecan (1995) "Advanced adaptive null steering concepts for

, 8 Nov 1995 San Diego, CA 1214 - 1218

Similer Documents