Advanced signal processing handbook : theory and implementation for radar, sonar, and medical imaging real time systems

Preface......Page 3
Editor......Page 5
Contributors......Page 6
Dedication......Page 8
Contents......Page 9
1.2 Overview Of A Real-time System......Page 15
1.3 Signal Processor......Page 17
1.3.2 Tomography Imaging CT/X-Ray and MRI Systems......Page 19
1.3.4 Active and Passive Systems......Page 21
1.4 Data Manager And Display Sub-system......Page 22
1.4.1 Post-Processing for Sonar and Radar Systems......Page 23
1.4.2 Post-Processing for Medical Imaging Systems......Page 25
1.4.3 Signal and Target Tracking and Target Motion Analysis......Page 28
1.4.5 Multi-Sensor Data Fusion......Page 31
2.1 The Filtering Problem......Page 36
2.2 Adaptive Filters......Page 37
2.3.1 Transversal Filter......Page 39
2.3.2 Lattice Predictor......Page 40
2.3.3 Systolic Array......Page 42
2.4 Approaches To The Development Of Linear Adaptive Filtering Algorithms......Page 43
2.4.1 Stochastic Gradient Approach......Page 44
2.4.2 Least-Squares Estimation......Page 46
2.5 Real And Complex Forms Of Adaptive Filters......Page 48
2.6 Nonlinear Adaptive Systems: Neural Networks......Page 49
2.6.1.1 Multilayer Perceptrons and Back-Propagation Learning......Page 50
2.6.1.2 Radial-Basis Function (RBF) Networks......Page 53
2.6.2.1 Principal Components Analysis......Page 55
2.6.2.2 Self-Organizing Maps......Page 56
2.6.5 Dynamically Driven Recurrent Networks......Page 57
2.7 Applications......Page 59
2.7.1 System Identificatio......Page 62
2.7.2 Spectrum Estimation......Page 64
2.7.3 Signal Detection......Page 65
2.7.4 Target Tracking......Page 67
2.7.5 Adaptive Noise Canceling......Page 70
2.7.6 Adaptive Beamforming......Page 75
2.7.6.1 Adaptive Beamformer with Minimum-Variance Distortionless Response......Page 76
2.7.6.2 Adaptation in Beam Space......Page 78
2.8 Concluding Remarks......Page 80
Gaussian Mixtures And Their Applications To Signal Processing......Page 85
3.1 Introduction......Page 86
3.2.1 The Approximation Theorem......Page 88
3.2.2 The Identifiability Proble......Page 89
3.3.1 The Maximum Likelihood Approach......Page 91
3.3.2 The Stochastic Gradient Descent Approach......Page 92
3.3.3 The EM Approach......Page 93
3.3.4 The EM Algorithm for Adaptive Mixtures......Page 96
3.4 Computer Generation Of Mixture Variables......Page 97
3.5 Mixture Applications......Page 99
3.5.1 Applications to Non-Linear Filtering......Page 100
3.5.2 Non-Gaussian Noise Modeling......Page 105
3.5.3 Radial-Basis Functions (RBF) Networks......Page 110
3.6 Concluding Remarks......Page 116
4.1 Introduction......Page 121
4.2.1 Basic Concept and Formulation......Page 122
4.2.2 Identifiability......Page 123
4.2.3 Bartlett Matched Field Processor Family......Page 124
4.3.2 Deterministic Sources......Page 129
4.3.3 Non-Stationary Random Sources......Page 131
4.3.4 Wide-Sense Stationary Random Sources......Page 133
4.4.1 Background Theory......Page 134
4.4.2 Formulation......Page 136
4.5.1 Background Theory......Page 137
4.5.2 Formulation......Page 139
4.6.1 Simulation Results......Page 142
4.6.2 Experimental Results......Page 146
Model-based Ocean Acoustic Signal Processing......Page 154
5.1 Introduction......Page 155
5.2.1 Motivation......Page 158
5.2.2 Overview......Page 162
5.2.4 Model-Based Processor (Kalman Filter)......Page 163
5.2.6 Extended Kalman Filter......Page 165
5.2.7 Model-Based Processor Design Methodology......Page 166
5.3 State-space Ocean Acoustic Forward Propagators......Page 169
5.4.1 Ocean Acoustic Data: Hudson Canyon Experimental Data......Page 177
5.4.2 Ocean Acoustic Application: Adaptive Model-Based Signal Enhancement......Page 179
5.4.2.1 Model-Based Signal Enhancement: Parametrically Adaptive Model......Page 180
5.4.3 Ocean Acoustic Application: Adaptive Environmental Inversion......Page 185
5.4.3.1 Adaptive Environmental Inversion: Augmented Gauss-Markov Model......Page 187
5.4.3.2 Adaptive Environmental Inversion: Sound Speed Estimation......Page 188
5.4.4.1 Model-Based Localization: Non-Linear Optimizer......Page 190
5.4.4.2 Model-Based Localization: Parametrically Adaptive Processor......Page 193
5.4.4.3 Model-Based Localization: Application to Hudson Canyon Data......Page 194
5.4.5 Ocean Acoustic Application: Model-Based Towed Array Processor......Page 197
5.4.5.1 Model-Based Towed Array Processor: Adaptive Processing......Page 198
5.4.6 Model-Based Towed Array Processor: Application to Synthetic Data......Page 200
5.5 Summary......Page 203
Advanced Beamformers......Page 208
6.1 Introduction......Page 210
6.2 Background......Page 211
6.3.1 Space-Time Processing......Page 214
6.3.2 Definition of Basic Parameter......Page 216
6.3.3 Detection and Estimation......Page 217
6.3.4 Cramer-Rao Lower Bound (CRLB) Analysis......Page 219
6.4 Optimum Estimators For Array Signal Processing......Page 221
6.4.1.1 Linear Array Conventional Beamformer......Page 222
6.4.2 Multi-Dimensional (3-D) Array Conventional Beamformer......Page 225
6.4.2.1 Decomposition Process for 2-D and 3-D Sensor Array Beamformers......Page 226
6.4.3 Influence of the Mediums Propagation Characteristics on the Performance of a Receiving Array......Page 229
6.4.4 Array Gain......Page 232
6.5.1 Synthetic Aperture Processing......Page 233
6.5.1.1 FFT Based Synthetic Aperture Processing (FFTSA Method)......Page 234
6.5.1.2 Yen and Carey’s Synthetic Aperture Method......Page 236
6.5.1.4 Spatial Overlap Correlator for Synthetic Aperture Processing (ETAM Method)......Page 237
6.5.2.1 Minimum Variance Distortionless Response (MVDR)......Page 240
6.5.2.2 Generalized Sidelobe Canceller (GSC)......Page 241
6.5.2.3 Steered Minimum Variance (STMV) Broadband Adaptive Beamformer......Page 242
6.6 Implementation Considerations......Page 244
6.6.1 Evaluation of Convergence Properties of Adaptive Schemes......Page 246
6.6.1.1 Convergence Characteristics of GSC and GSC-SA Beamformers......Page 247
6.6.1.2 Convergence Characteristics of STMV and STMV-SA Beamformers......Page 248
6.6.2.1 Sub-Aperture Configuration for Line Array......Page 249
6.6.2.2 Sub-Aperture Configuration for Circular Arra......Page 250
6.6.2.3 Sub-Aperture Configuration for Cylindrical Arra......Page 252
6.6.3 Signal Processing Flow of a 3-D Generic Sub-Aperture Structure......Page 254
6.7 Concept Demonstration: Simulations And Experimental Results......Page 258
6.7.1.1 Synthetic Data: Passive......Page 260
6.7.1.2 Synthetic Data: Active......Page 262
6.7.1.3 Real Data......Page 267
6.7.2.1 Synthetic Data Results for Ultrasound Systems Deploying Line Arrays......Page 269
6.8 Conclusion......Page 273
Advanced Applications Of Volume Visualization Methods In Medicine......Page 278
7.1.2 Methods......Page 279
7.1.2.1 Preprocessing......Page 280
7.1.2.2 Segmentation......Page 281
7.1.2.3 Surface-Based Rendering......Page 285
7.1.2.4 Volume-Based Rendering......Page 287
7.1.2.7 Intelligent Visualization......Page 292
7.1.2.9 Image Quality......Page 293
7.2.1 Radiological Data......Page 295
7.2.4.1 Introduction......Page 298
7.2.4.2 Collecting 3D Ultrasound Data......Page 300
7.2.4.3 Visualization of 3D Ultrasound......Page 301
7.2.5 3D Cardiac Reconstruction from 2D Projections......Page 304
7.2.5.1 Model-Based Restoration of Teeth......Page 305
7.2.5.2 Reconstruction of Coronary Vessels......Page 307
7.2.5.3 Reconstruction of Ventricles......Page 311
7.2.6 Visualization of Laser Confocal Microscopy Data Sets......Page 312
7.2.7.2 Proposed “Virtual Simulation”......Page 319
7.2.8 Conclusions......Page 320
A7.1 Introduction......Page 331
A7.3 The Fourier Transform......Page 332
A7.5 Image Sharpening......Page 335
A7.5.3 High-Pass Filtering......Page 336
A7.6.1 Local Averaging Masks......Page 339
A7.6.3 Gaussian Filter......Page 340
A7.6.4 Low-Pass Filtering......Page 342
A7.7.2 Prewitt, Sobel, and Robinson Operators......Page 344
References......Page 346
Target Tracking......Page 350
8.1.1 Tracking Systems......Page 352
8.1.3 Bayesian Approach......Page 353
8.1.4 Sensor Fusion Aspects......Page 354
8.2.1 Basic Notions......Page 355
8.3.1 Object Dynamics......Page 356
8.3.1.1 Example: A Simplifed Model......Page 357
8.3.2.1 Example: Swerling-I Targets......Page 358
8.3.4 Resolution......Page 359
8.3.5 Data Association......Page 360
8.3.5.2 Example: Small Object Clusters......Page 361
8.4.1 Finite Mixture Densities......Page 363
8.4.2.2 Example: Innovation Statistics......Page 365
8.4.4 Retrodiction......Page 366
8.4.4.1 Example: Rauch-Tung-Striebel Smoothing......Page 367
8.5.1 Moment Matching......Page 368
8.5.2 IMM-Type Prediction......Page 369
8.5.3 PDA-Type Filtering......Page 370
8.5.3.2 Example: JPDA Filtering for Small Clusters......Page 371
8.5.4.1 Individual Gating......Page 373
8.5.4.3 Local Combining......Page 374
8.5.5.1 Example: Standard IMM Retrodiction......Page 375
8.6.1 JPDA Formation Tracking......Page 376
8.6.2 MHT and Retrodiction......Page 377
8.6.3. Summary......Page 379
Target Motion Analysis (tma)......Page 383
9.1 Introduction......Page 385
9.2.1 Various Types of Measurements......Page 386
9.2.2 Observability......Page 388
9.2.2.1 Fundamental Ambiguities......Page 389
9.2.2.2 Nth-Order Dynamics Target......Page 390
9.3.1 Bearings-Only Tracking — A Typical TMA Problem......Page 391
9.3.2.1 General Case......Page 393
9.3.2.2 Bearings-Only Tracking......Page 395
9.3.3 Step 2 — Estimation Algorithm......Page 396
9.3.3.1 The Modified Polar Extended Kalman Filte......Page 397
9.3.3.2 The Maximum Likelihood Estimator......Page 399
9.3.4 Step 3 — Optimal Observer Motion......Page 400
9.4 Conclusion......Page 401
Sonar Systems......Page 405
10.1.2 Why Exploit Sound for Underwater Applications?......Page 406
10.1.4 Sonar......Page 407
10.2.2 Sound Velocity Profile......Page 408
10.2.3 Three Propagation Modes......Page 409
10.2.4 Multipaths......Page 410
10.2.5 Sonar System Performance......Page 411
10.3 Underwater Sound Systems: Components And Processes......Page 412
10.3.1 Signal Waveforms......Page 413
10.3.2 Sonar Transducers......Page 415
10.3.4 Sonar Projectors (Transmitters)......Page 416
10.3.6 Receiving Arrays......Page 417
10.3.8 Dynamic Range Control......Page 418
10.3.10 Displays......Page 419
10.4.1 Detection......Page 421
10.4.3 Classificatio......Page 423
10.5.1 Adaptive Beamforming......Page 424
10.5.3 Acoustic Data Fusion......Page 425
10.6 Application......Page 426
Theory And Implementation Of Advanced Signal Processing For Active And Passive Sonar Systems......Page 429
11.1.2.1 The Passive Sonar Problem......Page 430
11.1.2.2 The Active Sonar Problem......Page 432
11.2 Theoretical Remarks......Page 433
11.2.1 Definition of Basic Parameter......Page 437
11.2.2 System Implementation Aspects......Page 440
11.2.3.1 Low-Frequency Active Sonars......Page 443
11.2.3.2 Effects of Bandwidth in Active Sonar Operations......Page 445
11.2.3.3 Display Arrangements for Active Sonar Systems......Page 449
11.2.4 Comments on Computing Architecture Requirements......Page 452
11.3.1.1 Narrowband Acoustic Signals......Page 455
11.3.1.2 Broadband Acoustic Signals......Page 464
11.3.2 Active Towed Array Sonar Applications......Page 466
11.4 Conclusion......Page 469
12.1 Introduction......Page 478
12.2 Fundamental Theory Of Phased Arrays......Page 479
12.2.1.1 Focusing Properties of Arrays......Page 481
12.2.2 Linear Arrays......Page 482
12.2.2.1 Grating Lobes......Page 483
12.2.2.2 Side Lobe Level......Page 484
12.2.3 Twoand Three-Dimensional Arrays......Page 485
12.3.1 Statement of the Boundary Value Problem......Page 486
12.3.2 Solution of the N......Page 488
12.4 Array Architectures......Page 489
12.5 Conclusion......Page 490
Medical Ultrasonic Imaging Systems......Page 493
13.1 Introduction......Page 494
13.2.1 Resolution......Page 496
13.2.2 Scanning and Transducers......Page 497
13.3.3 Structure......Page 499
13.4.1 Single Channel......Page 500
13.4.2 Multi-Channel Systems, Arrays......Page 501
13.4.3 Doppler and Advanced Systems......Page 505
13.5 Conclusion......Page 507
14.1 Introduction......Page 512
14.2 Limitations Of Ultrasonography Addressed By 3-d Imaging......Page 513
14.3.1 Introduction......Page 514
14.3.2 Mechanical 3-D Scanning Devices......Page 515
14.3.2.2 Mechanical 3-D Scanning: Tilting......Page 517
14.3.2.3 Mechanical 3-D Scanning: Rotational......Page 520
14.3.3 Sensed Free-Hand 3-D Scanning......Page 521
14.3.3.3 Sensed Free-Hand 3-D Scanning: Magnetic Field Sensors......Page 522
14.3.3.3 Sensed Free-Hand 3-D Scanning: Tracking Based on Image Information......Page 523
14.3.4 Free-Hand Scanning without Position Sensing......Page 524
14.3.5 Dynamic 3-D Ultrasonography Using 2-D Arrays......Page 525
14.4.1 Feature-Based 3-D Reconstruction......Page 526
14.4.2 Voxel-Based 3-D Ultrasound Reconstruction......Page 527
14.5.1 Mechanical 3-D Scanning: Linear......Page 528
14.5.2 Mechanical 3-D Scanning: Tilting......Page 529
14.6 Viewing Of 3-d Ultrasound Images......Page 530
14.6.3 Volume-Rendering (VR)......Page 531
14.7.2 Distance Measurement......Page 534
14.7.4 Volume Measurements: Theoretical Derivation of Variability......Page 535
14.7.5 Volume Measurements: Experimental Measurements with Balloons......Page 536
14.7.8 Intraand Inter-Observer Variability in Measuring Prostate Volume......Page 537
14.8.1 Identification of Seeds......Page 538
14.9.3 Speed of Operation......Page 539
14.9.4 Applications in Image-Guided Therapy and Surgery......Page 540
15.1 Introduction......Page 547
15.2.1 Electromagnetic Radiation Interactions with Matter......Page 553
15.2.2.1 Transmission CT 3D Imaging......Page 558
15.2.3 Image Reconstruction Algorithms......Page 562
15.2.3.1 Transmission CT Algorithms......Page 563
15.2.3.2 Single Photon Emission CT (SPECT) Algorithms......Page 564
15.2.4 Fundamentals of the CT Measurement Equipment......Page 565
15.3.1.2 Improved Prosthetic Implant Design......Page 568
15.3.2.1 3D CT Analysis of Porosity in Ball Grid Arrays......Page 575
15.3.2.2 3D CT Analysis of Metal Powder Filling Methods......Page 576
15.3.2.4 Inspection of Bridge Pins......Page 580
15.3.3.1 Material Analysis of a DC Motor......Page 582
15.3.3.2 Material Analysis of High Explosives......Page 583
15.3.3.3......Page 587
15.5 Future Work......Page 589
16.1 Introduction......Page 596
16.1.1 CT Systems......Page 597
16.1.2 The Sinogram......Page 598
16.1.3 Image Reconstruction......Page 599
16.3 Reducing Motion Artifacts......Page 601
16.3.1 Established Methods......Page 602
16.3.4 ECG Gating......Page 603
16.3.5 Single-Breath-Hold ECG Gating......Page 604
16.4.1 Spatial Overlap Correlator to Identify Motion Effects......Page 606
16.4.1.2 Hardware Implementation......Page 609
16.4.1.3 Software Implementation......Page 610
16.4.2 Adaptive Processing to Remove Motion Artifacts......Page 611
16.4.2.1 Adaptive Interference Cancellation......Page 614
16.4.3.1 Phase Selection by Correlation......Page 617
16.4.3.2 Assembling the Coherent Sinogram......Page 618
16.4.5 Phantom Experiment......Page 619
16.4.6 Human Patient Results......Page 623
16.5 Conclusions......Page 626
17.1 Introduction......Page 633
17.2 Basic Nmr Phenomena......Page 634
17.4 Nmr Signal......Page 636
17.5 Signal-to-noise Ratio......Page 639
17.6 Image Generation And Reconstruction......Page 640
17.7 Selective Excitation......Page 645
17.8 Pulse Sequences......Page 647
17.9 Influence Of Motio......Page 652
17.10 Correction Of Motion During Image Series......Page 655
17.11 Imaging Of Flow......Page 656
17.12 Mr Spectroscopy......Page 658
17.13 System Design Considerations And Conclusions......Page 659
17.14 Conclusion......Page 660
18.1.1 Rationale......Page 663
18.2 Contrast Agent Kinetic Modeling......Page 664
18.3.2 Determinants of R......Page 665
18.3.2.1 Signal Equation......Page 666
18.3.2.2 T......Page 667
18.3.2.3 Fast Acquisition Relaxation Mapping (FARM)......Page 670
18.4.1 Dynamic Range of T......Page 673
18.4.2 Cardiac T......Page 675
18.4.4 Future Enhancements and Modifications ofT......Page 676
18.5 Summary......Page 677
19.1 Introduction......Page 682
19.2.1 Literature Review......Page 683
19.2.2 Generic Medical Image Registration Scheme......Page 684
19.2.2.1 Transformation Models......Page 685
19.2.2.2 Matching Features — Measure of Match......Page 686
19.2.2.3 Determination of the Transformation Parameters Using Global Optimization Techniques......Page 687
19.2.3 Medical Image Registration Refinement Using Elastic Defomation......Page 689
19.2.3.2 Surface Matching Based on the Kohonen Model......Page 690
19.2.4.1 2-D Case: Automatic Retinal Image Registration......Page 695
19.2.4.2 3-D Case: Automatic CT-MRI Registration......Page 696
19.3.1.1 Fusion Using Logical Operators......Page 699
19.3.1.2 Fusion Using a Pseudocolor Map......Page 700
19.3.1.3 Clustering Algorithms for Unsupervised Fusion of Registered Images......Page 701
19.3.1.4 Fusion to Create Parametric Images......Page 704
19.4 Conclusions......Page 705