Resilient  and  Accurate  Autonomous  Vehicle  Navigation  

via  Signals  of  Opportunity

Zak  M.  Kassas

Autonomous  Systems  Perception,  Intelligence,  and  Navigation  (ASPIN)  LaboratoryUniversity  of  California,  Riverside

Stanford  University    |    Stanford,  CA    |      November  8,  20171

Future  Autonomous  Vehicles3

Future  Autonomous  Vehicles4

Navigation  with  Signals  of  Opportunity  (SOPs):  COpNav5

Kassas  (2013).  Collaborative  opportunistic  navigation.  IEEE  Aerospace  and  Electronic  Systems  Magazine,  (28)6,  38–41.

SOPs:  Opportunities  and  Challenges6

Opportunities

¤ Abundant

¤ Available  at  varying    geometries  &  frequencies

¤ Free  to  use

¤ Are  significantly  more  powerful  than  GPS

Challenges

¤ States  may  be  unknown  a  priori

¤ Observables  need  to  be  extracted

¤ Clocks  are  not  as  stable  and  not  synchronized

¤ Signal  models  and  error  budgets  are  unavailable

8

The  SOP  signal  landscape  state  space  is  NOT  stationary

SOP  Signal  Landscape

Software-­‐Defined  Radio  for  SOP-­‐Based  Navigation  (MATRIX)

9

Cellular  CDMA  Navigation  SDR  (LabVIEW)10

Experimental  Setup  – Ground  Vehicle11

MATRIX

Khalife,  Shamaei,  &  Kassas  (2016).  A  software-­‐defined  receiver  architecture  for  cellular  CDMA-­‐based  navigation.  IEEE/ION  Position,  Location,  &  Navigation  Symposium  (PLANS), 816–826,  (Best  student  paper).

12

SOP  Mapping

SOP  Mapping13

SOP  =  Fake  Tree!14

Experimental  Demo:  UAV  Navigation  with  Cellular  CDMA

https://www.youtube.com/watch?v=GkfUxie2wnA

15

Cellular  LTE  Navigation  SDR  (LabVIEW)16

Shamaei, Khalife, & Kassas (2016). Performance characterization of positioning in LTE systems. in Proceedingsof ION GNSS Conference (IONGNSS+), 2262-­‐2270, (Best paper presentation).

Cellular  LTE  Navigation  SDR  (LabVIEW)17

Shamaei,  Khalife,  &  Kassas  (2016).  Exploiting  LTE  signals  for  navigation:  Theory  to  implementation,  IEEE  Trans.  on  Wireless  Communications,  submitted.

LTE  Receiver  Structure18

ESPRIT:  estimation  of  signal  parameters  by  rotational  invariance  techniques

Shamaei, Khalife, Bhattacharya, & Kassas (2017). Computationally efficient receiver design for mitigatingmultipath for positioning with LTE signals. in Proceedings of IONGNSS Conference (IONGNSS+), 3751-­‐3760, (Bestpaper presentation).

Experimental  Demo:  Ground  Vehicle  Navigation  with  LTE

https://www.youtube.com/watch?v=fIDgNgrJuZQ

19

SOP-­‐Aided  Inertial  Navigation20

¤ Traditional  tightly-­‐coupled  GNSS-­‐aided  INS

¤ Objectives:1. Estimate  SOPs’  states  when  GNSS  pseudoranges are  

available  (mapping)2. When  GNSS  psuedoranges become  unavailable,  continue  to  

estimate  SOPs’  states  and  use                    to  correct  INS  errors  (SLAM)

SOP-­‐Aided  INS  Framework21

Inertial  Navigation  System

EKF  Prediction

EKF  Update

DetectorIMU PPS

data

GNSSReceiver

SOPReceiver

flag

Tightly-­coupled

Simulator  Overview

¤ Mission  Planning  &  Scripting¤ Accelerate/  Decelerate,  Climb/  Descend,  

Turn,  Loops,  Rolls,  Aerobatics…

¤ Kinematic  Models¤ 6DOF  Aircraft¤ 3DOF  &  6DOF  Automobile

¤ Sensor  Models¤ GPS  L1/L2  (constellation  &  sub-­‐frames)¤ IMU¤ Magnetometer¤ Air  Data:  Pitot  &  Static¤ (and  several  others…)

22

23

SOP-­‐Aided  INS:  Simulated  Environment

SOP

SOP

SOP

SOP

SOP-­‐Aided  INS:  Simulated  Environment24

SOP-­‐Aided  INS:  Simulated  Environment25

Error

EKF  Results:  Vehicle  Position  and  Velocity26

0 50 100 150 200 0 50 100 150 200

Error

SOP-­aided  INS*

Traditional  GPS-­aided  INS

GPS  Cut-­off

*With  consumer          grade  IMU

Morales,  Roysdon,  &  Kassas  (2016).  Signals  of  opportunity  aided  inertial  navigation.  ION  Global  Navigation  Satellite  System  (ION  GNSS+)  Conference, 1492-­‐1501,  (Best paper  presentation).

Navigating  with  Cellular  CDMA  &  LTE27

Kassas,  Morales,  Shamaei,  &  Khalife (2017). LTE  Steers  UAV.  GPS  World  Magazine,  (28)4,  18-­‐25  (Cover  Article).

Experimental  Results28

Kassas,  Morales,  Shamaei,  &  Khalife (2017). LTE  Steers  UAV.  GPS  World  Magazine,  (28)4,  18-­‐25  (Cover  Article).

Collaborative  Navigation29

Centralized  Collaborative  Framework30

¤ Modes  of  operation:1. Collaborative  Mapping2. Collaborative  SLAM  (C-­‐SLAM)

Central   fusion  center

o Single  point  of  failure

o Large  communication  bandwidth

Morales  &  Kassas  (2016).  Collaborative  autonomous  vehicles  with  signals  of  opportunity  aided  inertial  navigation  systems.  ION  International  Technical  Meeting  (ION  ITM),  805-­‐818.

Distributed  Collaborative  Framework31

AV  2

AV  3

AV  4

AV  1 AV  N

Morales  &  Kassas  (2017).  A  Low  communication  rate  distributed  inertial  navigation  architecture  with  cellular  signal  aiding.  IEEE  Vehicular  Technology  Conference  (VTC),  submitted.

) 289 elements

This  is  only  32  elements!

Replace:

32

Monte  Carlo  Analysis

Morales  &  Kassas  (2017).  Distributed  signals  of  opportunity  aided  inertial  navigation  with  intermittent  communication.  ION  Global  Navigation  Satellite  System  (ION  GNSS+)  Conference,2519-­‐2530.  (Best  paper  presentation).

Approximation  robustnessPerformance  robustness

Experimental  Demo:  Distributed  UAV  Navigation  with  SOP-­‐Aided  Inertial

https://www.youtube.com/watch?v=gljRk2OgspM

33

Precise  Carrier  Phase  Navigation34

Experimental  Demo:  UAV  Navigation  with  Carrier  Phase  Cellular  Signals

https://www.youtube.com/watch?v=WSqDUoLkTWo

35

Acknowledgment36