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DRONE - Coggle Diagram

- - - - 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)
        
        Binary 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
  - - - 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
        
        Applicable Strategies
        
        Detection
        
        Classification
        
        Micro-UAV Detection and Classification
        
        from RF Fingerprints Using
        
        Machine Learning Techniques
      - Machine Learning Framework for RF-Based Drone Detection and Identification System
        https://arxiv.org/pdf/2003.02656.pdf
        
        Features
        
        off DroneRF daatset
        
        Classification
        
        XGBoost outperform DNN
      - RF-based Direction Finding of UAVs Using DNN
        https://arxiv.org/pdf/1712.01154.pdf
        https://github.com/LahiruJayasinghe/DeepDOA
        
        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
        
        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
      - 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
      - Over-the-Air Deep Learning Based Radio
        Signal Classification
        https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8267032
      - 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
        
        Introduced impairments to increase robustness
        
        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.
    - - DroneRF
        https://data.mendeley.com/datasets/f4c2b4n755/1
        
        Consists of post-processed segments of amplitude-time signals of drones and background
      - 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
      - DEEPSIG DATASET: RADIOML 2018.01A
        http://opendata.deepsig.io/datasets/2018.01/2018.01.OSC.0001_1024x2M.h5.tar.gz?__hstc=24938661.d54b40cc71f17b783e365e52a4d33e72.1599803271913.1599803271913.1599803271913.1&__hssc=24938661.1.1599803271913&__hsfp=846954124
      - DEEPSIG DATASET: RADIOML 2016.10A
        Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb
        Larger Version (including AM-SSB)
        Dataset Download
        Signal Generation Software
      - ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
        Datasets for RF Fingerprinting of Bit-similar USRP X310 Radios
        http://www.genesys-lab.org/oracle
        Dataset#1: Raw IQ samples of over-the-air transmissions from 16 X310 USRP radios
        Dataset#2: Demodulated IQ symbols of over-the-cable transmissions with 16 configurations of IQ imbalances
  - - - PiNcH: an Effective, Efficient, and Robust Solution to Drone Detection via Network Traffic Analysis
        https://arxiv.org/pdf/1901.03535.pdf
        
        Components required
        Wifi Probe sensitive enough
        to eavesdrop 2 way traffic
        
        Features
        
        Packet size
        
        Packet inter-arrival time
        
        Derivatives
        
        Multiclass Classifier
        Actions
        Status
        
        Applicable Strategies
        
        Status
        
        Detection
        
        Classification
        
        PiNcH: an Effective, Efficient,
        
        and Robust Solution to
        
        Drone Detection via Network
        
        Traffic Analysis
- - - - 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°