Radar Countermeasures for Unmanned Aerial Vehicles

Title
Copyright
Contents
About the editors
List of reviewers
Introduction
	I.1 UAV development
	I.2 Radar surveillance of UAVs
	I.3 Scope of this book
	Bibliography
1 Counter UAS systems overview
	1.1 Introduction
	1.2 Too small and simple to be a threat?
	1.3 Why an integrated system of sensors and shooters is a must?
	1.4 Operational use of counter-drone solutions
	1.5 Future scenarios and enabling technologies
		1.5.1 What will be the future of counter-drone systems?
	1.6 Conclusions
	Acronyms and abbreviations
	Bibliography
2 Systems design considerations
	2.1 Introduction
	2.2 The systems design challenge
	2.3 The role of radar
	2.4 System design examples
		2.4.1 Compact electronically scanned pulse-Doppler radar
		2.4.2 Holographic radar
	References
3 Applications of millimetre wave radar for UAV detection and classification
	3.1 Millimetre wave radar systems for UAV detection
	3.2 Millimetre wave RCS of UAVs
	3.3 Millimetre wave micro-Doppler signatures of UAVs
		3.3.1 FMCW micro-Doppler signatures
		3.3.2 CW micro-Doppler signatures
		3.3.3 Alternative time–frequency analysis methods
	3.4 Signatures of UAVs equipped with payloads at a millimetre wave
		3.4.1 UAV equipped with liquid spray payload
		3.4.2 UAV equipped with simulated recoil
		3.4.3 UAV equipped with heavy payload
	3.5 Classification methods of UAVs using millimetre wave data
	3.6 Conclusions
	References
4 Detection and tracking of UAVs using an interferometric radar
	4.1 Background
	4.2 Theory of interferometry
	4.3 UAV detection
		4.3.1 Range-Doppler processing
		4.3.2 CFAR detection
		4.3.3 Clustering
	4.4 UAV tracking
		4.4.1 Range and angle
		4.4.2 2-D velocity
		4.4.3 Kalman filtering
	4.5 Simulation results
	4.6 Experimental results
	4.7 Conclusion and future work
	References
5 Passive radar detection of drones with staring illuminators of opportunity
	5.1 Introduction
	5.2 Overview of a passive bistatic radar
		5.2.1 PBR exploiting analogue signals
		5.2.2 PBR exploiting digital signals
		5.2.3 PBR exploiting radar signals
		5.2.4 Drone detection in PBR
		5.2.5 PBR with single-channel detection
	5.3 The staring radar signals
	5.4 Experimental set-up
	5.5 Detection results with reference channels
		5.5.1 Experimental results
	5.6 PBR without a reference channel
		5.6.1 Experimental results
	5.7 Comparisons
	5.8 Conclusions
	References
6 DVB-T-based passive radar for silent surveillance of drones
	6.1 Introduction
	6.2 DVB-T-based PR coverage study
		6.2.1 Coverage estimation methodology
		6.2.2 Coverage analysis
	6.3 DVB-T-based PR processing scheme and the disturbance cancellation stage
		6.3.1 DVB-T-based PR processing scheme
		6.3.2 Experimental results and impact of the cancellation stage on the drone detection performance
	6.4 Neyman–Pearson detector approximation and clutter modelling
		6.4.1 Radar clutter characterization
		6.4.2 Likelihood Ratio detector formulation
	6.5 Multi-channel signal processing strategies
		6.5.1 Exploitation of array antennas for target localization
		6.5.2 Exploitation of the frequency and spatial diversity to improve the detection and localization performance
	6.6 Conclusions
	References
7 Multiband passive radar for drones detection and localization
	7.1 Introduction
	7.2 Exploitation of different waveforms of opportunity
	7.3 Passive radar based on DVB-S
		7.3.1 DVB-S-based PR processing schemes
		7.3.2 Experimental drone detection and localization with DVB-S-based PR
		7.3.3 Phase-locked vs non-phase-locked receiver architectures
		7.3.4 Exploiting polarizations for drone detection
	7.4 Passive radar based on DVB-T
		7.4.1 DVB-T-based PR systems for simultaneous short- and long-range monitoring
		7.4.2 Tackling the different target dynamic issues
	7.5 Passive radar based on WiFi
		7.5.1 WiFi-based PR receiver architecture and processing schemes
		7.5.2 Experimental drones detection and 3D localization with WiFi-based PR
		7.5.3 WiFi joint operation of passive radar and passive source location
	7.6 Conclusions
	References
8 GNSS-based UAV detection
	8.1 Introduction
	8.2 GNSS-based PR coverage
		8.2.1 Back and forward scattering RCS of UAVs
		8.2.2 Passive bistatic radar equation
		8.2.3 Case studies
	8.3 Source signal reconstruction
		8.3.1 Signal model
		8.3.2 GNSS signal characteristics
		8.3.3 Signal reconstruction algorithm
	8.4 Target parameters estimation
		8.4.1 Target localisation
		8.4.2 Velocity estimation
	8.5 Experimental analysis
		8.5.1 Scenario 1: crossing in front of the receiver
		8.5.2 Scenario 2: descending away from the receiver
	8.6 Conclusion
	References
9 Radar UAV and bird signature comparisons with micro-Doppler
	9.1 Introduction
	9.2 Review of UAV and bird radar signatures research
	9.3 Target motion models
	9.4 Fully polarimetric, multiple observation angle laboratory measurements of UAV target
	9.5 Bistatic and multistatic radars used to gather bird and drone data
		9.5.1 NetRAD
		9.5.2 NeXtRAD
	9.6 NetRAD bird and drone S-band measurements
	9.7 NetRAD drone payload experiments
	9.8 NeXtRAD L- and X-band drone and birds measurements
		9.8.1 Drone filtering
	9.9 Concluding remarks
	Acknowledgements
	References
10 Radar recognition of multiple UAVs
	10.1 Introduction
	10.2 Recognition of multiple UAVs based on CFS
		10.2.1 Signal model
		10.2.2 Classification method and experimental results
	10.3 Recognition of multiple UAVs via dictionary learning
		10.3.1 Recognition method
		10.3.2 Experimental results
	10.4 Conclusion
	References
11 Advanced classification techniques for drone payloads
	11.1 Introduction
	11.2 Radar system and experimental setup
	11.3 Classification approaches for drones and payloads
		11.3.1 SVD and micro-Doppler centroid features
		11.3.2 Pretrained convolutional neural networks
		11.3.3 Spectral kurtosis analysis and features
	11.4 Conclusions and outlook
	Acknowledgements
	References
12 Good practices and approaches for counter UAV system developments – an industrial perspective
	12.1 Introduction
	12.2 Robust drone classification with a staring radar
	12.3 Methodology for ground truthing
		12.3.1 Control targets
		12.3.2 Targets of opportunity
	12.4 Ground-truth results for drones and birds
	12.5 Machine learning classification
	12.6 Conclusions
	Acknowledgements
	References
Conclusion
Index