DRONE

Software/Firmware

AutoPilot

PX4 FlightStack

iNav

LibrePilot

Paparazzi

Ardupilot

First Person View

BetaFlight

CleanFlight

dRonin

RaceFlight

MultiWii (Inactive)

BaseFlight (Inactive)

TauLabs (Inactive)

OpenPilot (Inactive)

Techniques

Acoustic

Vision

Thermal

Radar

Existing Work

https://arxiv.org/pdf/2003.01519.pdf
Amateur Drones Detection: A machine
learning approach utilizing the acoustic
signals in the presence of strong
interference

Independant Component Analysis (ICA)

  • Unmix Drone, Birds, Rain, Wind..etc

Extract Audio Features
(E.g. MFCC, PSD..etc)

RF Fingerprinting

Data Comms (Encrypted)

Existing work

Deployed Systems

click to edit

Techniques
3D Lidar, Acoustic, Radio, VA

Range
250 to 300m

Features

Detect and Track up to 4.5KM

Verification, Identification & Classification up to 2.5KM

Neutralize up to 2KM

Azimuth coverage: 360°

Elevation: 20° up to 60°

Techniques
Lidar, Vision, Radar

PiNcH: an Effective, Efficient, and Robust Solution to Drone Detection via Network Traffic Analysis
https://arxiv.org/pdf/1901.03535.pdf

Works for
All sorts of drones

Binary Classification

Components required
Wifi Probe sensitive enough
to eavesdrop 2 way traffic

Features

  • Packet size
  • Packet inter-arrival time
  • Derivatives

Multiclass Classifier
Actions
Status

Challenges
Irrelevant signals in the same freq band

Existing Works


Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques
https://arxiv.org/pdf/1901.07703.pdf

Focus on Controller

Preprocessing

  • To improve low
    SNR detection
  • Using wavelets

Detection

  • Drone or Noise
  • Based on observed
    distinct transition signals
  • Use Markov models to detect

Classification

  • Energy: skewness, variance,
    energy spectral entropy, kurtosis
  • Time-Frequency
  • Neighborhood Component Analysis:
    Remove correlated features

Strategies

  • DOA
  • Detection
  • Classification
  • Identification
  • Status

Applicable Strategies

  • Status
  • Detection
  • Classification

Applicable Strategies

  • Detection
  • Classification

https://arxiv.org/pdf/1902.11130.pdf
Real-Time detection, classification
and DOA estimation
of Unmanned Aerial Vehicle

Applicable Strategies

  • DOA
  • Detection
  • Classification

Compute 3D space

  • Omni dir microphones
  • Time diff
  • Dir of drone

Features

  • PSD

Classification


Machine Learning Framework for RF-Based Drone Detection and Identification System
https://arxiv.org/pdf/2003.02656.pdf

Datasets

Features

  • off DroneRF daatset

Classification

  • XGBoost outperform DNN

click to edit

Hardware

  • Single channel RF Receiver
  • Directional Antennas

Applicable Strategies

  • DOA

Features

  • PSD over antennas

Sparse Representation

  • Trained with Autoencoder DNN
  • Think of this as embedding

DNN Training

  • Label is Direction

No Radio Left Behind: Radio FingerprintingThrough Deep Learning of Physical-LayerHardware Impairments
https://www.researchgate.net/publication/340572325_Deep_Learning_for_RF_Fingerprinting_A_Massive_Experimental_Study

Matthan: Drone Presence Detection by Identifying Physical
Signatures in the Drone’s RF Communication

https://dl.acm.org/doi/pdf/10.1145/3081333.3081354

Detection and Classification of UAVs Using
RF Fingerprints in the Presence of Interference

https://arxiv.org/pdf/1909.05429.pdf

Localisation of Drone Controllers from RF Signals using a Deep
Learning Approach.

http://eprints.lincoln.ac.uk/id/eprint/34434/1/localisation-drone-controllers%20(12).pdf

Towards RF-based Localization of a Drone and Its Controller
https://www.cs.colorado.edu/~rhan/Papers/2019_DRONET_RF-based_Localization.pdf

Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach
https://ieeexplore.ieee.org/document/9089657

RF-based drone detection and identification using deep learning
approaches: An initiative towards a large open source drone database


https://reader.elsevier.com/reader/sd/pii/S0167739X18330760?token=01BEFF8CDFAFDE67B4F4E74CAF00274962F071F5B6622EFF641C0619B35057A9406E9E0ABEABCD97CEFAB9E8E84FDCC5

Signal Transformation
Complex to Data

Reference Datasets for Training and Evaluating
RF Signal Detection and Classification Models

https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928336


RF Dataset of Incumbent Radar Systems in the 3.5 GHz CBRS Band
https://nvlpubs.nist.gov/nistpubs/jres/124/jres.124.038.pdf

Over-the-Air Deep Learning Based Radio
Signal Classification

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8267032

Micro-UAV Detection and Classification

from RF Fingerprints Using

Machine Learning Techniques

Consists of post-processed segments of amplitude-time signals of drones and background

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
https://arxiv.org/pdf/1812.01124.pdf

Features
IQ samples in Channel Invariant Env
Complex demodulated symbols in varying channel env

Architecture
2 CNN Layer with 2 FCN and a Softmax for classification

Pros

Cons

  • Its too sensitive, able to fingerprint different devices even if identical make/model
  • Performs badly in channel variant env
  • A pretrained model based on data collected in Time A and Location A, will not work in Time B... or Location B... due to IQ data dependance on environment which varies in time and space.

Considerations

  • HIgher quality SDR produces less impairments and make it less discrimitative for IQ data
  • The same signal received over different channels actually can become 'different'. So channel variance can be a problem.

Introduced impairments to increase robustness

click to edit

Features

  • Perform DFT on DroneRF dataset
  • Split the 80Mhz into 8 channels of 10Mhz each
  • Each channel consists of 2048 freq points
  • Input: [8x2048]

Architecture

  • 1D multi-channel CNN
  • FC Layers
  • Softmax

PiNcH: an Effective, Efficient,

and Robust Solution to

Drone Detection via Network

Traffic Analysis