Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review
Abstract
:1. Introduction
 UAVs can be correctly identified only at very short distances. Effective surveillance systems must be able to react and take the appropriate countermeasures promptly, also in adverse operating conditions such as low visibility, propagation environments full of obstacles, etc.
 Major security threats can arise from UAVs approaching in swarms. The adopted technologies should be able to detect multiple targets simultaneously and to track their trajectories in realtime.
 UAVs cannot be easily distinguished from other small flying objects such as birds. Advanced signal processing algorithms are then needed in order to lower the probability of false alarm and increase the correct detection rate.
2. Basic Theory for Radar Signal Processing
2.1. Radar Sensor
2.2. Moving Target Indicator (MTI)
2.3. Features Extraction Techniques
2.4. Empirical Mode Decomposition (EMD)
 The number of local extrema differs from the number of zerocrossings at most by one;
 The average of the envelope shall be zero.
2.5. Hardware Limitations and I/Q Imbalance
3. Literature on Drone Detection
3.1. Constant False Alarm Rate (CFAR)
3.2. Radar Detection Approaches
4. Literature on Drone Verification and Classification
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
CACFAR  Cell Averaging CFAR 
CFAR  Constant False Alarm Rate 
CNN  Convolutional Neural Network 
COTS  CommercialOffTheShelf 
CVD  Cadence Velocity Diagram 
CW  Continuous Wave 
DAR  Digital Array Radar 
DFM  Doppler Frequency Migration 
DOA  Direction Of Arrival 
EMD  Empirical Mode Decomposition 
FFT  Fast Fourier Transform 
FIR  Finite Impulse Response 
FMCW  Frequency Modulated Continuous Wave 
GPS  Global Positioning System 
IID  Independent and Identically Distributed 
IMF  Intrinsic Mode Function 
KNN  KNearest Neighbor 
LFMCW  Linear Frequency Modulated Continuous Wave 
MTI  Moving Target Indicator 
NB  Naive Bayes 
OSCFAR  Order Statistics CFAR 
PCA  Principal Component Analysis 
PD  Probability of Detection 
Probability density Function  
PFA  Probability of False Alarm 
RCS  Radar Cross Section 
RF  Radio Frequency 
RM  Range Migration 
SDR  Software Defined Radio 
SNR  SignaltoNoiseRatio 
SOCACFAR  Smallest of CACFAR 
STFT  Short Time Fourier Transform 
SVD  Singular Value Decomposition 
SVM  Support Vector Machine 
UAV  Unmanned Aerial Vehicle 
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Technology  Approach  Pros and Cons 

Video  One or more cameras to perform identification exploiting drone motion 

Audio  Sound generated by flying drones exploited to perform DOAbased identification 

RF (passive)  Downlink video stream or EM scattering of opportunistic RF signals 

Radar (RF active)  Backscattering of RF signal exploited to perform Dopplerbased tracking and delaybased identification 

LIDAR (laser scanner)  Similar to radar, but backscattering of laser light is exploited 

Environment  

Homogeneous  Interfering Targets  Clutter Boundaries  Interfering Targets and Clutter Boundaries  
CA  ✓  
GOCA  ✓  
SOCA  ✓  
CS  ✓  
TM  ✓  ✓  ✓  
OS  ✓  ✓  ✓  
GOOS  ✓  ✓  ✓  
GOCS  ✓  ✓  ✓ 
Paper  Radar Type  Frequency Band  CFAR 

[59]  CW  Kband  ✓ 
[60]  multistatic pulsed  Sband  ✓ 
[62]  FMCW  Kband  ✗ 
[63]  FMCW  Wband  ✓ 
[64]  multistatic pulsed  Sband  ✓ 
[65]  FMCW  Xband  ✗ 
[66]  FMCW  Kband  ✓ 
[67]  FMCW  Xband  ✓ 
[68]  FMCW  Sband  ✓ 
Paper  Radar Type  Frequency Band  Features  Classifier 

[59]  CW  Kband  MicroDoppler signature  SVM 
[60]  multistatic pulsed  Sband  MicroDoppler signature  CNN (AlexNet) 
[61]  FMCW  Sband  MicroDoppler signature  NB, DAC, Random Forest 
[71]  CW  X and K bands  MicroDoppler signature  SVM 
[72]  CW  Xband  6 physical features from [76]  LogitBoost 
[77]  CW  Xband  6 entropy measures from IMF  SVM 
[78]  FMCW  K and W bands  MicroDoppler signature  not specified 
[79]  CW  UHF  MicroDoppler signature  SVM, KNN, NB, Random Forest 
[80]  FMCW  Xband  MicroDoppler signature and 13 IMF features  TER 
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Coluccia, A.; Parisi, G.; Fascista, A. Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors 2020, 20, 4172. https://doi.org/10.3390/s20154172
Coluccia A, Parisi G, Fascista A. Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors. 2020; 20(15):4172. https://doi.org/10.3390/s20154172
Chicago/Turabian StyleColuccia, Angelo, Gianluca Parisi, and Alessio Fascista. 2020. "Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review" Sensors 20, no. 15: 4172. https://doi.org/10.3390/s20154172