Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar (Radar, Sonar and Navigation)

Halftitle Page
Series Page
Title Page
Copyright
Contents
About the Editors
Preface
1 HF radar in a maritime environment
	1.1 HF radar as an ocean remote sensor – introduction
		1.1.1 A few fundamentals
		1.1.2 Common classes and properties of ocean-mapping HFSWR
	1.2 A brief historical perspective on relevant theory and technology
		1.2.1 Relevant propagation and scattering theory
		1.2.2 Technological advances
	1.3 RCSs of the ocean
		1.3.1 A technique for developing an RCS of the ocean
		1.3.2 Other cross-section results
		1.3.3 RCS depictions and discussion
	Acknowledgment
	References
2 Oceanographic applications of high-­frequency (HF) radar backscatter
	2.1 Factors influencing HF backscatter
		2.1.1 The electromagnetic spectrum and the speed of light
		2.1.2 Factors related to the use of HF transmissions
		2.1.3 Impacts of noise and averaging
		2.1.4 Relevant time and space scales
		2.1.5 Depths observed by HF radar
	2.2 Real-time applications of HF radar backscatter
		2.2.1 Considerations of real-time applications
		2.2.2 Examples of real-time applications
	2.3 Example of an intermediate-scale observation
	2.4 Process studies using HF radar backscatter
	2.5 Conclusions
	References
3 Symbiosis of remote sensing and ocean surveillance missions of HF skywave radar
	3.1 Modelling the radar observation process
		3.1.1 The radar process model
		3.1.2 Calibration
		3.1.3 Sea clutter modelling I: the direct problem
		3.1.4 Sea clutter modelling II: the inverse problem
	3.2 Characteristics of OTHR radar missions
	3.3 Remote sensing information for enhanced surveillance
		3.3.1 Detection
		3.3.2 Location
		3.3.3 Target classification
		3.3.4 Resource management
		3.3.5 Tactical intelligence
	3.4 Summary
	References
4 Sea surface current mapping with HF radar – a primer
	4.1 Introduction
	4.2 Theory behind radial and vector current derivation from HF radar Doppler spectrum
	4.3 Factors affecting current measurements
		4.3.1 HF radar system types
		4.3.2 Range resolution
		4.3.3 Geometrical dilution of precision
		4.3.4 Signal propagation and sea state
	4.4 HF radar current observations on the West Florida Shelf
	4.5 Ongoing HF radar investigations on the West Florida Shelf
		4.5.1 An event of offshore working range drop
		4.5.2 Average background noise and RFI effect
		4.5.3 Atmospheric radio Refractivity (
		4.5.4 Wind speed effect
	4.6 Summary
	Acknowledgment
	References
5 An initial evaluation of high-­frequency radar radial currents in the Straits of Florida in comparison with altimetry and model products
	5.1 Introduction
	5.2 Data sets
		5.2.1 High-frequency radar current data and post-processing
		5.2.2 Satellite altimetry-derived current products
		5.2.3 Numerical model output
	5.3 Evaluation metrics
	5.4 Comparison with geostrophic currents derived from along-track altimetry
	5.5 Comparison with geostrophic currents derived from gridded altimetry
	5.6 Comparison with data assimilative model output
	5.7 Summary and discussion
	Acknowledgment
	References
6 Ocean wave measurement
	6.1 Introduction to ocean waves
	6.2 Waves in the Doppler spectrum
		6.2.1 First order
		6.2.2 Second order
	6.3 Inversion
		6.3.1 Approximations and empirical methods
		6.3.2 Integral inversion
		6.3.3 The constrained iteration method
	6.4 Examples and validations
		6.4.1 Time series
		6.4.2 Statistics
		6.4.3 Spatio-temporal wave development
	6.5 Sources of error and limitations
		6.5.1 Radar data quality
		6.5.2 Averaging
		6.5.3 The scattering model
		6.5.4 Numerical methods
	6.6 Summary
	Acknowledgment
	References
7 A non-­linear method to estimate the wave directional spectrum by HF radar
	7.1 Introduction
	7.2 Equations of radar cross sections
	7.3 Discretization of the integral equation
	7.4 Other constraints
	7.5 Algorithm
	7.6 Procedure of wave spectrum estimation
	7.7 Example of wave estimation and issues to be addressed
	References
8 HF radar observation of nearshore winds
	8.1 Introduction
	8.2 Background
		8.2.1 Early studies
		8.2.2 Wind direction via wave spreading models
		8.2.3 Wind speed
	8.3 Winds from second-order wave estimates
	8.4 Winds from first order
	8.5 Discussion
		8.5.1 Trade off between first- and second-order wind sensing
		8.5.2 Further radar noise issues
		8.5.3 Propagation losses
		8.5.4 Future directions
	8.6 Summary
	Acknowledgment
	References
9 HF radar in tsunami detection
	9.1 The underlying physics
	9.2 Observation of surface currents
	9.3 Tsunami characteristics
		9.3.1 Physics of tsunamis
	9.4 HF ocean radar detection of tsunamis
		9.4.1 Crossed-loop HF radar systems
		9.4.2 Phased-array HF radars
	9.5 Definition of a hazardous tsunami
	9.6 Discussion and summary
		9.6.1 Oblique tsunamis
		9.6.2 Maximising the alert period
		9.6.3 Achieving surface current resolution
	9.7 Conclusion
	Acknowledgment
	References
10 High-­frequency surface wave radar for target detection
	10.1 Introduction to high-frequency surface wave radar basics
	10.2 HFSWR system configurations
		10.2.1 Bistatic c2onfiguration
		10.2.2 Monostatic
	10.3 HFSWR for target detection
	10.4 Radar power budget
		10.4.1 Radar range equation for a noise-limited environment
		10.4.2 Radar range equation for an ocean clutter limited environment
	10.5 Ocean clutter
	10.6 Surface wave propagation
	10.7 Maximum detection range
	10.8 External noise
		10.8.1 Manmade noise
		10.8.2 Atmospheric noise level
		10.8.3 Galactic noise
	10.9 Interference and clutter
		10.9.1 External interference
		10.9.2 Self interference (clutter)
		10.9.3 Ionospheric clutter
		10.9.4 Ionospheric clutter scattering modes
		10.9.5 Range wrap clutter mitigation
		10.9.6 Meteor clutter
	10.10 Radar cross section at HF
		10.10.1 Definition of RCS at HF
		10.10.2 RCS aspect angle dependency
		10.10.3 RCS sea state dependency
		10.10.4 RCS and stealth
		10.10.5 Modelling radar cross section of vessels
		10.10.6 RCS of large vessel
		10.10.7 RCS of medium vessel
		10.10.8 RCS of small vessel
		10.10.9 RCS of very small vessels
	10.11 Resolution
	10.12 Accuracy of estimates
	10.13 HFSWR and cognitive sensing
	10.14 Challenges and ongoing research
	References
11 Introduction to ocean remote sensing with marine radars
	11.1 Marine radar ocean observing instrumentation
		11.1.1 Hardware
		11.1.2 Software
	11.2 Applications
		11.2.1 Waves
		11.2.2 Currents
		11.2.3 Bathymetry
		11.2.4 Winds
	11.3 Recent developments included in this book
		11.3.1 Chapter 12: Observation of sea surface waves by noncoherent X-band marine radar
		11.3.2 Chapter 13: Wavelet-based methods to invert sea surfaces and bathymetries from X-band radar images
		11.3.3 Chapter 14: Wave field reconstruction using orthogonal decomposition of Doppler velocities
		11.3.4 Chapter 15: Current mapping from the wave spectrum
		11.3.5 Chapter 16: Bathymetry (and current) retrieval: phase-based method
		11.3.6 Chapter 17: Wind parameter measurement using X-band marine radar images
	References
12 Observation of sea surface waves by noncoherent X-­band marine radar
	12.1 Introduction
	12.2 FFT-based algorithms
		12.2.1 Retrieval of wave spectrum
		12.2.2 Estimation of wave parameters
		12.2.3 Modulation transfer function
		12.2.4 Example
	12.3 The algorithm based on EOF analysis
		12.3.1 EOF decomposition
		12.3.2 Estimation of wave parameters
		12.3.3 Physical interpretation of modes
		12.3.4 Discussion of mode choice for SWH estimation
		12.3.5 Example and validation
	12.4 Summary
	References
13 Wavelet-­based methods to invert sea surfaces and bathymetries from X-­band radar images
	13.1 Simulation of the sea surface elevation and radar images over a laterally uniform bottom profile
	13.2 Direct and inverse 2D Continuous Wavelet Transform
	13.3 The 2D Wavelet-based Surface Reconstruction method
	13.4 Bathymetry reconstruction technique
	13.5 Conclusions
	Acknowledgment
	References
14 Wave field reconstruction using orthogonal decomposition of Doppler velocities
	14.1 Potential limitations of the FFT-based wave field processing
	14.2 Proper orthogonal decomposition for wave field reconstruction
		14.2.1 Data
		14.2.2 Proper orthogonal decomposition
		14.2.3 Mode selection and physical significance of the POD modes
	14.3 Evaluation of POD-based wave field reconstructions
		14.3.1 Wave field statistics
		14.3.2 Phase resolved wave field comparisons
	14.4 Summary and limitations of pod-based wave field reconstructions
	References
15 Current mapping from the wave spectrum
	15.1 Wave propagation atop background currents
	15.2 Appearance of the linear dispersion relation in the spectrum
		15.2.1 Practical considerations
	15.3 Extracting currents from the spectrum
		15.3.1 Least squares method
		15.3.2 Normalized scalar product method
		15.3.3 Polar current shell method
		15.3.4 Algorithm comparison
	15.4 Reconstructing depth-dependent flows
		15.4.1 Effective depth method
		15.4.2 Ha-Campana method
		15.4.3 Polynomial effective depth method
	15.5 Challenges and further work
		15.5.1 Validation
		15.5.2 Interpretation of the currents: Stokes drift
	15.6 Summary
	References
16 Bathymetry (and current) retrieval: phase-­based method
	16.1 Introduction
	16.2 Brief overview
	16.3 Frequency and wavenumber estimates
		16.3.1 Fourier series representation of the imaged wave field
		16.3.2 Compute the temporal discrete Fourier transform
		16.3.3 Compute the cross-spectral coherence spectrum
		16.3.4 Extract the dominant cross-spectral eigenvector
		16.3.5 Minimize a cost function to estimate wavenumber
		16.3.6 Wavenumber estimate quality metrics
	16.4 Depth inversion
		16.4.1 Problem formulation
		16.4.2 Remove temporal water level trends
	16.5 Temporal updates
		16.5.1 Kalman filter
		16.5.2 Moving average
	16.6 Revisit current estimation
	16.7 Performance
	16.8 Summary and future work
	References
17 Wind parameter measurement using X-­band marine radar images
	17.1 Wind streaks/wind gusts based methods
		17.1.1 Local gradient based method
		17.1.2 Optical flow based method for wind vector retrieval
	17.2 Intensity information and curve fitting based methods
		17.2.1 Single curve fitting based algorithm
		17.2.2 Two-model curve fitting for rain mitigation
		17.2.3 Dual curve fitting for low sea state cases
		17.2.4 Significant wave height incorporated curve fitting
		17.2.5 Intensity level selection algorithms
		17.2.6 Modified ILS
		17.2.7 Texture analysis incorporated ILS
	17.3 Transform domain and curve fitting based methods
		17.3.1 Spectral noise based algorithm
		17.3.2 Spectral integration based algorithm
		17.3.3 Ensemble empirical mode decomposition based methods
	17.4 Nonparametric regression based methods
		17.4.1 Neural network based method
		17.4.2 Support vector regression based method
		17.4.3 Gaussian process regression based method
	17.5 Error mitigation
	17.6 Conclusions and outlook
	References
18 Introduction to remote sensing using GNSS signals of opportunity
	18.1 A quick historical review of GNSS-R
	18.2 Basic concepts on GNSS
		18.2.1 Measurement principle
		18.2.2 Structure of the GNSS signals
		18.2.3 Received power of the GNSS signals
		18.2.4 Atmospheric and ionospheric effects
		18.2.5 Satellite navigation systems
	18.3 GNSS-R
		18.3.1 Spatial resolution
		18.3.2 Received power: coherent and incoherent components
		18.3.3 The Woodward ambiguity function
		18.3.4 GNSS-R observables and techniques
		18.3.5 SNR computation
	18.4 GNSS-R ocean applications
		18.4.1 Ocean Scatterometry
		18.4.2 Ocean altimetry
		18.4.3 Ocean imaging
	18.5. Conclusions
	Acknowledgment
	References
19 Modeling and simulation of GNSS-­R delay-­Doppler maps over the ocean
	19.1 Introduction
	19.2 Observation geometry and GNSS signal propagation
		19.2.1 Ionospheric delay
		19.2.2 Tropospheric delay
	19.3 Statistically rough surfaces
	19.4 Scattering models for GNSS-R signal simulation
		19.4.1 Facet approach
		19.4.2 Zavorotny-Voronovich bistatic equation model
		19.4.3 Statistical scattering model
		19.4.4 Comments to modeling equations
	19.5 Simulation of the GNSS-R signal
		19.5.1 Observation geometry and specular point calculation
		19.5.2 Surface gridding
		19.5.3 Simulation with a facet scattering model
		19.5.4 Simulation based on the Zavorotny-Voronovich equation
		19.5.5 Simulation based on a stochastic model
	19.6 Conclusions
	References
20 Wind estimation
	20.1 Modelling ocean-reflected GNSS signals
		20.1.1 Woodward’s Ambiguity Function
		20.1.2 The Delay Doppler Map
		20.1.3 The Bistatic Radar Equation
		20.1.4 Electromagnetic scattering model
		20.1.5 Sea surface models
	20.2 Processing delay Doppler maps
		20.2.1 Feature extraction
		20.2.2 Calibration
	20.3 Retrieval techniques
		20.3.1 Empirical wind speed estimation
		20.3.2 Machine Learning
		20.3.3 Stare processing
	20.4 Summary
	20.5 Future challenges
	References
21 GNSS-­R ocean altimetry
	21.1 Historical overview: technical relevant aspects
	21.2 Altimetric tracking point
	21.3 Height precision
	21.4 Impact of GNSS odes on altimetric performance
	21.5 Experimental field campaigns
		21.5.1 Ground-based
		21.5.2 Air-borne
	21.6 Space-borne missions
		21.6.1 PARIS IoD
		21.6.2 Space-borne Imaging Radar-C
		21.6.3 UK TDS-
		21.6.4 CYGNSS
	21.7 Conclusions
	Acknowledgment
	References
22 Sea ice sensing using the GNSS-­R technique
	22.1 Background and overview
	22.2 Sea ice detection
		22.2.1 DDM observable based method
		22.2.2 Scattering coefficient retrieval based method
		22.2.3 Machine learning based method
		22.2.4 Empirical model based method
	22.3 SIC estimation
	22.4 SIT retrieval
		22.4.1 Three-layer model
		22.4.2 Empirical SIT estimation model
		22.4.3 Phase altimetry based SIT retrieval
	22.5 Ice altimetry techniques
		22.5.1 Waveform based method
		22.5.2 Phase based method
	22.6 Other applications
		22.6.1 Sea ice classification
		22.6.2 Sea ice permittivity and roughness retrieval
	22.7 Conclusions
	References
23 Triton – GNSS Reflectometry Mission in Taiwan
	23.1 Introduction
	23.2 Triton satellite mission
	23.3 GPSR development
	23.4 GNSS reflectometry mission payload
	23.5 GNSS-R payload validation
	23.6 Wind speed retrieval algorithm
		23.6.1 The MSS observation principle of the miniature buoy
	23.7 Summary
	References
Appendix: List of Reviewers
Index
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