MAC LAYER ASSISTED LOCALIZATION IN WIRELESS ENVIRONMENTS WITH MULTIPLE SENSORS AND MULTIPLE EMITTERS A Thesis Presented to The Academic Faculty by Paul W. Garver In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Electrical and Computer Engineering Georgia Institute of Technology December 2017 Copyright c 2017 by Paul W. Garver MAC LAYER ASSISTED LOCALIZATION IN WIRELESS ENVIRONMENTS WITH MULTIPLE SENSORS AND MULTIPLE EMITTERS Approved by: Dr. Edward J. Coyle, Advisor Dr. Randal T. Abler School of Electrical and Computer School of Electrical and Computer Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. David Anderson Dr. Kishore Ramachandran School of Electrical and Computer College of Computing Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. John Barry Date Approved: 25 September 2017 School of Electrical and Computer Engineering Georgia Institute of Technology To my late mother, Francis Del Garver, for her persistent encouragement. iii ACKNOWLEDGEMENTS I am grateful to my advisor, Dr. Edward J. Coyle, for challenging me academically and providing guidance throughout this dissertation. His insights and useful discussion helped to frame my thinking and approach to problem solving. Thank you for the committee members for their constructive criticism. I am also grateful for the financial contributions by BIT Systems, especially Tom Ladd, and Harris Corporation to complete my studies. The testbed in Bobby Dodd Stadium would not have been possible without the hard work of the Intelligent Digital Communications Vertically Integrated Projects (VIP) team and Dr. Randal T. Abler. Dr. Abler's implementation insight, networking experience, and equipment was invaluable to the success of the testbed. Undergraduate students Jaison George, Orin Lincoln, and Hayden Flinner made significant contributions for which I am grateful. Additionally, the resources of the Senior Design Laboratory staffed by James Steinberg and Kevin Pham were critical in constructing the testbed. Finally, Doc and others from Georgia Tech Athletics were instrumental in providing access and supporting our living laboratory in the stadium. Finally, this dissertation would not have been possible without the tireless support, encouragement, and advice from my beautiful wife, Jill. Thank you for always being for me, and for all you have done for our family. Taking such great care of our boys, James and Caleb, working part time, and walking with me through graduate school was no easy task. I am truly grateful for you, Jill. iv TABLE OF CONTENTS DEDICATION ...................................... iii ACKNOWLEDGEMENTS .............................. iv LIST OF TABLES ................................... viii LIST OF FIGURES .................................. ix SUMMARY ........................................ xiv I INTRODUCTION ................................. 1 II TESTBED DEVELOPMENT AND DEPLOYMENT ........... 5 2.1 Comparison of High Performance Software Radios . .5 2.1.1 Background . .8 2.1.2 Hardware Configuration . .9 2.1.3 Software Configuration . 11 2.1.4 Methodology . 12 2.1.5 Results . 16 2.1.6 Conclusions . 18 2.2 Extreme Emitter Density Testbed . 19 2.2.1 Background . 20 2.2.2 Design and Deployment . 20 2.2.3 Analysis and Simulation . 26 2.2.4 Conclusions . 29 III MAC ASSISTED DATA ASSOCIATION .................. 31 3.1 Background . 32 3.1.1 RF Fingerprinting . 32 3.1.2 Data Association . 34 3.2 System Model . 36 3.3 Problem Formulation . 39 3.4 Single Client Analysis . 42 3.4.1 PHY-Only (L1) . 42 3.4.2 MAC-Only (L2) . 46 v 3.4.3 MAC-Assisted (L1/L2) . 47 3.4.4 Probability of Error as a Function of SNR . 48 3.4.5 Multiple Sensors . 51 3.5 Multi Client Analysis . 52 3.6 Model Validity . 56 3.7 Conclusions . 58 IV MAC ASSISTED LOCALIZATION ...................... 60 4.1 Background . 62 4.1.1 Time Delay Estimation . 62 4.1.2 Round-Trip Time-of-Flight . 63 4.2 System Model . 64 4.3 Stage I - Decoding Sensors . 68 4.4 Stage II: All Participating Sensors . 70 4.4.1 Non-Decodable Sensor (Sm Γndec) Time Delay Estimation . 71 2 4.4.2 Non-Decodable Sensor Estimator Variance . 75 4.4.3 Decodable Sensor (Sm Γdec) Time Delay Estimation . 80 2 4.4.4 Position Estimation Using All Sensors . 80 4.5 Stage III: MAC-Assisted Positioning . 81 4.5.1 Analysis of MAC-Assisted Positioning . 82 4.5.2 MAC-Assisted Position Estimation . 88 4.6 Simulation . 90 4.6.1 Cross-Correlation Distribution . 90 4.6.2 Three-Stage Algorithm . 90 4.7 Conclusion . 95 V CONCLUSION AND FUTURE DIRECTIONS .............. 96 APPENDIX A | POSITION SOLVER CALCULATIONS ........ 98 APPENDIX B | DISTRIBUTION OF THE CROSS-CORRELATION FUNCTION ..................................... 101 APPENDIX C | WINDOWED INTEGER LAG PROBABILITIES . 107 APPENDIX D | MEAN AND VARIANCE FOR R.V. L ........ 110 vi APPENDIX E | TIME DELAY ESTIMATION SIMULATIONS ... 111 REFERENCES ..................................... 114 vii LIST OF TABLES 1 System SWAP Comparison . .9 2 System Processor Comparison . 11 3 RFSN Components . 22 4 Data Association Notation . 40 5 MAC Frame ID, AP sends RTS . 42 6 Notation . 83 7 Probability the Emitter Lies Within the Confidence Region for a Confidence Coefficient of 0:95 over 1000 Trials. Variance units are seconds squared. 94 viii LIST OF FIGURES 1 System A and B Size Comparison . 10 2 Analog Video Processing Block Diagram . 14 3 FM Receiver . 14 4 Distributed Digital Video Processing Block Diagram . 15 5 Analog Video Benchmark . 16 6 Hardware Threading Performance on System C . 16 7 FM Radio Receiver . 17 8 Main and Offload Node Video Processing . 18 9 RFSN Components. The GPSDO is inside the RF digitizer enclosure . 22 10 Laboratory LOC-EED. T1-T3 represent RFSNs which are cabled to split- ters, labeled S. The cable lengths from emitter i to sensor k are Lik. The combiner, C, sums the signals from all transmitters into R1-R3, which are also RFSNs. 10 MHz and 1 PPS references are distributed to all nodes for time synchronization. 25 11 Stadium LOC-EED. RFSN1 is currently deployed. Google Earth. 25 12 RFSN1 Deployment . 26 13 Ch. 6 WiFi (2437 MHz) during a football game. The white circle is an OFDM-modulated WLAN packet. The red square represents an unknown narrowband interferer. A variable frequency sinusoid can also be seen from [6,15] ms and [-10,-5] MHz. 27 14 r [n] with Emitter E identified. When the cross-correlation exceeded a j 1 j 1 threshold, the sample number nac was recorded. The WLAN packet was subsequently decoded and the emitter labeled based on MAC address. This information was associated as a tuple (nac;Ei). The left side of the red box is placed at nac and labeled accordingly. 28 15 Layer-1/Layer-2 Correlation . 29 16 System Diagram. The AP communicates with C clients using IEEE 802.11g. The AP and clients do not collaborate with sensors S1;:::;SM . The sensors use Layer 1 and, when possible, Layer 2 information to localize emitters E1;E2;:::;EN ................................... 37 17 RTS/CTS packet exchange sequence state diagram for one client. Extra thick and shaded circles are states with corresponding MAC packets containing both emitter IDs (c.f. Table 5). State order is: (RT S; E1), (RT S; AP ),(CT S; E1), ............................................ 43 ix 18 The average per packet and per packet exchange sequence probability of association error is shown in 18a and 18b, respectively for a single client and sensor. The independent variable is average SNR per symbol (dB) received at the sensor from emitter E1. The various ξ(dB) curves represent the ratio of the SNR difference (in dB) received at the sensor between emitters to the SNR from E1. The packet decode probability mapping to SNR assumes 1 QPSK 2 in a Ricean channel with K0 =5.................... 50 19 19a shows the per packet exchange sequence probability of association error for one client with fixed ξ. L1/L2 and L2 represent the MAC-Only and MAC- Assisted strategies, respectively. The various ξ(dB) curves represent the ratio of the SNR difference (in dB) received at the sensor between emitters to the SNR from E1. 19b plots the per packet exchange sequence probability of association error for one client and multiple sensors using Layer 2 information. 51 20 RTS/CTS packet exchange sequence state diagram for two clients. State transitions are not shown from the ACK state for diagram clarity. All 1−q ACK states return to (RT S; En); n = 1;:::;N 1 with probability and − 2 (RT S; EN ) with probability q. Extra thick and shaded circles signify states with corresponding MAC packets containing both emitter IDs (c.f. Table 5). ? is a dummy emitter indicating the third element is not required to fully define the state. 53 21 The per packet exchange sequence probability of association error using Layer 2 information. 21a plots this for various numbers of sensors, M. Each sensor has a local packet decode probability of p0 = 0:2 and q = 0:75, where q is the probability the RTS packet is sent by the AP. 21b plots this probability as the number of clients, C, tends to for various q............. 56 1 22 Approximate probability of error for detecting an RTS collision using inter- frame spacing. The curves represent various π0, the probability of an RTS collision. The signal is assumed to be 20 MHz channel-spaced OFDM with 1 QPSK R = 2 modulation and short timeslot Tslot = 9µS. 58 23 System Diagram. Emitter E1 transmits a signal to E2. Sensors Sm; m = 1; 2;:::;M have known position vectors qm and attempt to localize Emitter E1 with unknown position vector p1 using TDoA. E2 has a known position. Distances from E1 to Sm are denoted as dm(p1). Sensors able to decode the packet (Sm Γdec) are labeled in bold green, non-decoding (Sm Γndec) in 2 2 italicized red, and non-participating sensors (Sm Γnp) in underlined gray 2 text.