Polarimetric Radar Signal Processing

Contents
About the Authors
Foreword
Notations
1 Adaptive target detection with polarimetric radars
1.1 Introduction
1.2 Problem formulation & design issues
1.3 Detection schemes for targets embedded in Gaussian clutter
1.3.1 Performance assessment
1.4 Detection schemes for targets embedded in compound-Gaussian clutter
1.4.1 Performance assessment
1.4.1.1 Performance assessment on simulated data
1.4.1.2 Performance assessment on measured clutter data
1.5 Conclusions
References
2 Exploiting polarimetric diversity in passive radar
2.1 Introduction
2.1.1 Organization
2.2 Multi-polarimetric PCL architectures
2.2.1 Background and motivations
2.2.2 Alternative architectural approaches to polarimetric PCL
2.3 Cascaded polarimetric PCL processing architecture
2.3.1 Polarimetric noncoherent integration detector
2.3.2 Polarimetric generalized likelihood ratio test detector
2.3.3 Experimental validation
2.3.3.1 Case study A: FM radio-based PCL
2.3.3.2 Case study B: DVB-T based PCL
2.3.3.3 Case study C: DVB-S based PCL
2.4 Integrated polarimetric PCL processing architecture
2.4.1 Polarimetric extensive cancellation algorithm
2.4.2 Polarimetric autoregressive model based adaptive detector
2.4.2.1 Polarimetric AR-based adaptive matched filter
2.4.2.2 Modified Pol-AR-AMF detection scheme for PCL application
2.4.3 Experimental validation
2.5 Summary
Acknowledgements
References
3 Mainlobe jamming suppression for polarimetric multi-channel radar
3.1 Introduction
3.2 Signal model
3.2.1 Target echo model
3.2.2 Jamming echo model
3.2.3 Total echo model
3.3 Jamming suppression via the P-ICA method
3.3.1 Pre-whitening processing
3.3.2 Estimation of the fourth-order cumulant matrix
3.3.3 Joint diagonalization processing
3.3.4 Signal separation
3.4 Numerical results
3.5 Conclusions
References
4 Coherent change detection in multi-polarization synthetic aperture radar images
4.1 Introduction
4.2 System model
4.3 Homogeneous unstructured environment
4.3.1 Data reduction and invariance issues
4.3.2 Maximal invariant design
4.3.3 Induced maximal invariant design
4.3.4 Design of the optimum and sub-optimum invariant detectors
4.3.4.1 Design of the MPI detector
4.3.4.2 Maximal invariant-based detectors
4.4 Homogeneous structured environment
4.5 Partially homogeneous environment
4.5.1 Maximal and induced maximal invariant design
4.5.2 GLRT derivation
4.5.3 Sub-optimum invariant detectors
4.6 Performance assessment
4.6.1 Homogeneous unstructured environment
4.6.2 Homogeneous structured environment
4.6.3 Partially homogeneous environment
4.7 Conclusions
References
5 Classification of covariance symmetries in full-polarimetric SAR images
5.1 Introduction
5.2 Preliminary definitions
5.3 Polarimetric covariance symmetries
5.3.1 Absence of symmetry
5.3.2 Reflection symmetry
5.3.3 Rotation symmetry
5.3.4 Azimuthal symmetry
5.3.5 Transformed matrix domain
5.4 Problem formulation and GML computation
5.4.1 Homogeneous case
5.4.2 Heterogeneous case
5.5 Model order rules for classification of symmetries
5.6 Detection of symmetries in PolInSAR applications
5.7 Performance assessment
5.7.1 Analysis of simulated data
5.7.2 Test on real-recorded SAR images
5.8 Conclusion
References
6 Polarimetric information to enhance synthetic aperture radar automatic target recognition capabilities
6.1 Introduction
6.2 Target decompositions
6.2.1 Coherent target decompositions
6.3 Feature-based ATR techniques
6.3.1 PZ moments with applications to Polarimetric SAR classification
6.4 Krogager-enhanced pseudo-Zernike-based algorithm
6.4.1 Performance evaluation
6.4.1.1 GOTCHA data set
6.4.1.2 Noise conditions
6.4.1.3 Results
6.5 Conclusions
References
7 Polarimetric inverse synthetic aperture radar
7.1 Introduction
7.2 Polarimetric ISAR
7.2.1 Pol-ISAR signal model
7.2.2 Image formation
7.2.3 Polarimetric autofocus
7.3 Classification with Pol-ISAR
7.3.1 Feature extraction
7.3.2 Pol-CLEAN
7.3.3 Target decomposition
7.3.3.1 ICTD
7.3.3.2 CTD
7.4 ATR using Pol-ISAR images
7.4.1 Feature-based classifier
7.4.1.1 Support vector machines
7.4.1.2 Bayesian classifier
7.4.2 Features pre-processing
7.4.2.1 Fisher discriminant ratio
7.4.2.2 Scatter matrices
7.4.3 Application to real data set
7.4.3.1 Setup description
7.4.4 Results
7.4.5 Neural network
7.4.5.1 NN-based ATR using Pol-ISAR images
7.4.5.2 NN-based ATR using Pol-ISAR images sequences
7.5 Conclusions
References
8 UWB short-range polarimetric imaging and its potential for target classification
8.1 Introduction
8.2 Data acquisition for short-range polarimetric imaging
8.2.1 Scattering formulation
8.2.2 Wavefield extrapolator for rotated antennas
8.2.3 Rotated antenna arrays design
8.2.3.1 Array topologies
8.2.3.2 Sampling criteria
8.3 Image reconstruction
8.4 Numerical simulations of UWB full-polarimetric imaging
8.5 Experimental studies of UWB polarimetric imaging
8.6 Conclusions
References
9 Robust transceiver design for polarimetric radars
9.1 Introduction
9.2 System model
9.2.1 Target model
9.2.2 Signal model
9.3 Problem formulation
9.4 Code and filter bank design
9.4.1 Receive filter optimization solution to problem Pw (m)
9.4.2 Radar code optimization: solution to problem Ps (m)
9.4.3 Transmit-receive system design: optimization procedure
9.5 Numerical results
9.5.1 Monotonic property of the proposed method and the impact of the similarity constraint
9.5.2 The effects of the TAA uncertainty sets and receive filter bank size
9.5.3 Performance comparison with other methods
9.6 Conclusion
9.7 Appendix
9.7.1 Proof of proposition 1
9.7.2 Proof of proposition 2
9.7.3 Proof of proposition 3
9.7.4 Proof of proposition 4
References
10 An ambiguity suppression scheme for quad-pol SAR based on quasi-orthogonal waveforms
10.1 Introduction
10.1.1 Main contribution and organization of the chapter
10.2 The design of quasi-orthogonal NLFM waveforms
10.2.1 Signal model
10.2.2 Correlation function
10.2.3 Optimization method
10.3 Conventional quad-pol SAR
10.3.1 Ambiguity performance using quasi-orthogonal NLFM waveforms
10.3.2 Polarimetric probing strategy via combination of quasi-orthogonal NLFM waveforms and APC
10.4 Simulation and performance analysis
10.4.1 Waveform performance evaluation
10.4.2 Point target simulation
10.4.3 Distributed target simulation
10.5 Conclusion
References
11 Fully polarimetric monopulse spaceborne radar for space situational awareness
11.1 Introduction and phenomenology for space situational awareness
11.2 Fully polarimetric SBR architecture for SSA
11.3 Polarimetric sensor data and operative strategies
11.3.1 Time hierarchies and complex data hypercube
11.3.2 Low-PRF RRRS and PWS strategy
11.4 Payload system architecture
11.4.1 Microprocessor and ultra-stable oscillator
11.4.2 Arbitrary waveform generator
11.4.3 RF upconversion
11.4.4 AESA subsystem
11.4.5 RF downconversion
11.4.6 Multichannel complex envelope acquisition
11.4.7 Notes on the guard channel
11.5 Digression towards cognitive Bayesian tracking
11.6 Conclusions
References
12 Polarization information processing in insect radar
12.1 History of insect radar
12.1.1 Scanning insect radar
12.1.2 Vertical looking insect radar
12.1.3 Fully polarimetric insect radar
12.2 Insect parameters estimation based on the polarization characteristics
12.2.1 Polarization echo model of insects
12.2.2 Orientation estimation
12.2.2.1 Polarization characteristics of insect orientation
12.2.2.2 Discrimination between PA and PE insects
12.2.2.2.1 Identification method
12.2.2.2.2 Performance at different radar frequencies
12.2.2.2.3 Performance at different SNRs
12.2.2.3 Estimation of orientation
12.2.2.3.1 MRD estimation based on polarization pattern model
12.2.2.3.2 MRD estimation based on the main eigenvector of SM
12.2.2.3.3 Verification of orientation estimation based on measured insect data
12.2.2.3.4 Performance at different radar frequencies
12.2.2.3.5 Performance at different SNRs
12.2.3 Mass/body length estimation
12.2.3.1 Invariant target parameters
12.2.3.2 Mass estimation
12.2.3.3 Body length estimation
12.3 The fully polarimetric insect radar system
12.3.1 The fully polarimetric insect radar prototype
12.3.2 Experimental validation of the measurement of the fully polarimetric insect radar
12.3.2.1 Discrimination of PA and PE insects
12.3.2.2 Orientation estimation
12.3.2.3 Mass and body length estimation
12.4 Conclusion and future prospects
References
13 Polarimetric weather radar signal processing
13.1 Introduction
13.2 Principle of operation
13.3 Signal processing
13.3.1 Overview
13.3.2 Digital downconversion and matched filtering
13.3.3 I/Q sample collection
13.3.4 Ground clutter processing
13.3.4.1 Clutter detection
13.3.4.2 Clutter filtering
13.3.5 Weather radar observables
13.3.6 Covariance processing
13.3.6.1 Single-channel covariance Processing
13.3.6.2 Polarimetric covariance processing and observable estimation
13.3.7 Threshold checks
13.3.8 Beam filling effects on weather radar observables
13.4 The meteorological radar equation
13.5 Radar calibration and monitoring
13.5.1 Engineering calibration
13.5.2 Polarimetric calibration and monitoring
13.5.2.1 Solar calibration and monitoring
13.5.2.2 Bird-bath calibration and monitoring
13.6 Polarimetric data processing
13.6.1 Attenuation correction
13.6.1.1 Attenuation correction of reflectivity
13.6.1.2 Attenuation correction of differential reflectivity
13.6.2 Rain rate estimation
13.6.3 Echo and hydrometeor classification
13.6.3.1 Threshold and fuzzy-logic approaches
13.6.3.2 Other approaches
13.7 Wind turbine clutter mitigation
13.7.1 Introduction
13.7.2 Wind turbine interference for weather radar
13.7.3 Identification of WTs
13.7.4 Mitigation of interferences
13.7.4.1 Distance
13.7.4.2 Interpolation
References
14 Meteorological polarimetric phased array radar
14.1 Motivation for polarimetric phased array radar
14.1.1 Polarimetric weather radar
14.1.1.1 Transmission modes
14.1.1.2 Polarimetric variables
14.1.1.3 Applications
14.1.2 Unique capabilities of PPAR
14.1.3 Existing PPAR demonstrator systems
14.2 Challenges with PPAR
14.2.1 Cross-coupling bias mitigation in the SHV mode
14.2.2 Cost of PPAR implementation
14.3 Antennas
14.3.1 Antenna geometry
14.3.2 Array architectures
14.3.2.1 Passive phased arrays
14.3.2.2 Active phased arrays
14.3.2.3 Subarray digital phased arrays
14.3.2.4 Element-level digital phased arrays
14.3.3 Phased array calibration and thermal management
14.3.4 Rapid scanning for weather observations
14.4 The advanced technology demonstrator
14.5 Outlook
References
Index
Back Cover