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Drone Net Architecture for UAS Traffic Management

ICARUS GroupMulti-modal Sensor Networking

Experiments

November 27, 2017

https://pluto.pr.erau.edu/%7Eicarus/

ICARUS Group - Drone NetDrone Net - Multi-Modal Sensor Network For Small UASShared Air Space Safety And SecurityUAS Traffic Management (RT Catalog of Aerial Objects)Focus on Rural and Urban UTM Scenarios

Sam Siewert Drone Net, ICARUS Group Slide 2

https://pluto.pr.erau.edu/%7Eicarus/

https://utm.arc.nasa.gov/ Kopardekar, Parimal, et al. "Unmanned aircraft system traffic management (UTM) concept of operations. AIAA Aviation Forum. 2016.

https://utm.arc.nasa.gov/

Significance

Slide 4

Motivation Large Numbers of sUAS Droneii, FAA, Sandia, ASSURE Counter UAS Challenge senseFly Catalog of Uses

Problem Default solution Part 107 for sUAS and beyond ADS-B for sUAS insufficient, infeasible RADAR/LIDAR feasibility

Drone Net hypothesis Networked, multi-modal

(passive/active), information and sensor data fusion for de-confliction

EO/IR + acoustic, spectral fusion, machine learning

Compare to and validate with LIDAR/RADAR, ADS-B

sUAS ATM Proposed Solutions1) Blacksage, 2) Droneshield3) Dedrone, 4) Gryphon, 5) AARONIA, 6) UTM, 7) LATAS, 8) UTM partners, 9) ERAU Drone Net

Test, Compare, Train, and Stimulate UTM Development

http://droneii.com/https://www.faa.gov/data_research/aviation/aerospace_forecasts/media/2015_national_forecast_report.pdfhttp://prod.sandia.gov/techlib/access-control.cgi/2015/156365.pdfhttp://www.assureuas.org/https://www.mitre.org/news/press-releases/mitre-challenge-counter-uas-team-selects-finalistshttps://www.sensefly.com/drones/example-datasets.htmlhttps://www.faa.gov/uas/getting_started/part_107/https://www.droneii.com/top20-drone-company-ranking-q3-2016http://www.uasmagazine.com/uploads/posts/magazine/2016/10/World-Civil-UAS-Market-Forecast-01_14763701921453.jpghttps://www.blacksagetech.com/https://www.droneshield.com/http://www.dedrone.com/en/http://gryphonsensors.com/http://www.aaronia.com/products/solutions/Aaronia-Drone-Detection-Systemhttps://utm.arc.nasa.gov/http://www.flylatas.com/https://www.nasa.gov/aeroresearch/programs/aosp/saso/partnershttp://mercury.pr.erau.edu/%7Esiewerts/extra/papers/ICARUS-Drone-Net-Proposal-and-Concept.pdf

Drone Net - UAS Traffic Management Concepts

Sam Siewert, ICARUS Group Drone Net Concept, 11/1/2017

Air-column Test Range[Drone Net NodeSensor Network]

1 Km

1 Km

ADS-B[Ping 2020i]

ADS-B truth

Machine Vision & Learning[Real-Time and Simulation]

sUAS

Compliant Flight Configuration

Passive Sensing Net

AcousticArray

EO/IR Narrow Field

Performance

ROC, PR, F-measure,new metrics

S or X-band RADAR

Active Sensing

Ground LIDARLimited Range

Detect, Track, Classify, Identify, Localize

Cluster &GP-GPU

GA Traffic

MAVlink

UAS LIDAR For Proximity

Operations

NASDatabase

All-skyHemispherical

Navigation Logtruth

Slide 5

https://aerospace.honeywell.com/en/blogs/2015/november/it-is-a-bird-it-is-a-plane-it-is-a-drone

Urban Scenario Roof MountUTM Scenarios such as Parcel Delivery

Embry Riddle flight line provides lots of light aircraft traffic

Campus (semi-Urban) environment

Wildlife insects, bats, birds, etc.

Sam Siewert Drone Net, ICARUS Group Slide 6

STEM 125 EMVIA Research LabDrone Net Sensor Data Capture and Processing

EO/IR, Acoustic and Embedded Systems Integration and Test

UAV, Instrument Integration and Test

General Research for Embedded Machine Vision and Intelligent Automation

Sam Siewert Drone Net, ICARUS Group Slide 7

7.5 TFLOP, 10TB RAID-10Machine Learning

GPS

DSRCWireless

Access Point

Non-compliant sUAS

All-skyHemispheric

al

Local Drone NetMachine Learning Server

Drone NetMaster DBMS

Acoustic array

ADS-BTx/RxCompliant sUAS

LIDAR

EO/IR with IMU

EO/IRwith IMU

All-skyHemispheric

al

Acoustic array

EO/IRwith IMU

ADS-B Rx ADS-B Rx

Drone Net Passive Instrument Experimental Configuration

Slide 8

EO/IR - Software Defined MSIRGB/Panchromatic Visible Cameras1 LWIR Camera with ZnSe WindowJetson Tegra K1/X1/X2, 802.11 Wireless DSRC, USB3 Hub, Power, NEMA Enclosure

Sam Siewert Drone Net, ICARUS Group Slide 9

CPU0

CPU1

CPU2

CPU3

Low-Power CPU

PowerManagement

Controller

wake-up lines

Kepler GPU(192 Stream Co-Processor

Cores)

Memory Controller

Memory

Real-timeClock

I/O Controllers

USB3Port1Port2VDD_CPU

VDD_GPU

VDD_CORE

Mini PCIe

PCIe8

Channel Decoder

4.5-18VDC, 1.1W

NTSC

Narrow Field8-14 bit LWIR

8-12 bit panchromatic

Machine Vision

EthernetController

12VDC5000

mAmp

802.11Controller

Jetson - Tegra X1/X2 System-on-Chip

Wide Field All-sky camera andMPTS acoustic array

NarrowField

Visible

Slide 10

MV/ML Flight EO/IR Frames[OEM Snapshot for prototype,MV/ML future enhancement]

MV/ML Ground EO/IR Frames[Detection, Classification and Identification

Subset of frames fromContinuous 10Hz baseline]

MATLABGeometric Analysis

& Re-Simulation

OEM NavigationLog Data

HF NavigationLog Data

[future enhancement]

ADS-B Log Data[sUAS, GA compliant

identification]

MV/MLDetection Performance

Receiver Operator CharacteristicPR (Precision/Recall), etc.

Human ReviewDetection, Classification, and Identification

{TP, FP, TN, FN}

Localization Error &ADS-B Identification, Detection

{TP, FP, TN, FN}

Simulated HFOV, VFOVAnd Cross Section of Tracked sUAS

Synthetic Frame Generation

Time Correlated Frame Retrieval

HF truthOEM truth

Optical Navigation truth

Frame Compare

ADS-B truth

Actual

sUAS Not sUAS

PredictedsUAS 250 TP 43 FP

Not sUAS 0 FN 3 TN

Slide 11

Needs Debugging Literally!Many Insects Detected in Visible to LWIR

Opportunity to work on Bird / Aviation Interaction Testing

Sam Siewert Drone Net, ICARUS Group Slide 12

2017/18 Team ERAU ARI SponsoredERAU Drone Net

Dr. Sam Siewert, PI, SE/CE Dr. Iacopo Gentilini, Co-I, ME Dr. Stephen Bruder, Co-I, EE Dr. Mehran Andalibi, Co-I, ME/AE

ERAU Graduate and Undergraduate Jonathan Buchholz (ME Robotics) David Olson (EE) Garrison Bybee (SE) Jonathan Buchholz MS CESE RA (2018/19, 19/20)

CU Boulder Embedded Systems Graduate Vijoy Sunil Kumar MS, ESE Aasheesh Dandupally ME, ESE Soumyatha Gavvala ME,ESE Omkar Ajit Prabhu ME, ESE

Research Collaboration Participants ERAU Prescott Aviation Science, ATC and UAS programs University of Alaska (ACUASI, Fairbanks)

Industry Advising/Collaboration Participants Randall Myers, Mentor Graphics (PCB, CAD, Systems Fabrication) Google (Applied, Faculty Research Proposal)

Sam Siewert Drone Net, ICARUS Group Slide 13

http://mercury.pr.erau.edu/%7Esiewerts/extra/papers/ICARUS-Drone-Net-Proposal-and-Concept.pdf

Next StepsCollaboration with ERAU Prescott Aviation Science Jennah Perry (Assistant Prof. ATC) Integration of Drone Net Detection, Tracking (Localization) and

Identification with ATC Grid FSS Management (FAA System Ops)ATC sUAS Integration Operations Concept VideoParallel talk submissions to AUVSI Denver 2018

Curtis James (Chair, Applied Aviation Sciences, Prof. Meteorology) NSF MRI for Dual-pole X-band RADAR (Ranger X)

Feasibility comparisons of sUAS Tracking and Identification with Active vs. Passive MethodsRange, Accuracy, Precision comparisons

ERAU UTM Collaboration - host field tests and sUAS Fly-ins Summer 2017 (Educational Outreach Program) ARI Research - Demonstration at Daytona Plan Aviation Science Joint Testing in Prescott (target 2019)

Sam Siewert Drone Net, ICARUS Group Slide 14

http://faculty.erau.edu/search?q=Jennah+Perryhttps://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/systemops/https://www.dropbox.com/s/omvko5y75oahsye/UTM_Research_Final.mp4?dl=0http://faculty.erau.edu/search?q=Curtis+Jameshttp://www.eecweathertech.com/ranger-x-band-radar-systems.php

SummaryDrone Net Will Produce Significant Detection, Tracking, Classification and Identification Data

Nodes Linked Together to SAN/NAS Database and File System on a Campus, Airport, Facility

Locations Can Uplink and Share Detection Signatures to Cloud for Improved Classification and Identification

Combined Machine Vision, Machine Learning and Big Data Analytics Challenge

Sam Siewert Drone Net, ICARUS Group Slide 15

Drone Net Related Talks & Publications1. S. Siewert, et al., Drone Net, a passive instrument network driven by machine vision and machine

learning to automate UAS traffic management, (submitted), AUVSI Xponential, Denver, Colorado, May 2018.

2. S. Siewert, S. Bruder, I. Gentilini, M. Andalibi, Drone Net Architecture for UAS Traffic Management Multi-modal Sensor Networking Experiments, (in preparation), IEEE Aerospace Conference [program], Big Sky, Montana, March 2018.

3. S. Siewert, et al. for ICARUS Group, Drone Net Big Data, Machine Vision and Learning Challenge and Opportunity,