Academic Press Library in Signal Processing, Vol.7 Array, radar and communications engineering

✍ Scribed by Chellappa, Rama; Theodoridis, Sergios

• Publisher: Academic Press
• Year: 2018
• Tongue: English
• Leaves: 654
• Series: Academic Press library in signal processing 7
• Category: Library

✦ Synopsis

Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering is aimed at university researchers, post graduate students and R & D engineers in the industry, providing a tutorial-based, comprehensive review of key topics and technologies of research in Array and Radar Processing, Communications Engineering and Machine Learning. Users will find the book to be an invaluable starting point to their research and initiatives. With this reference, readers will quickly grasp an unfamiliar area of research, understand the underlying principles of a topic, learn how a topic relates to other areas, and learn of research issues yet to be resolved.

✦ Table of Contents

Front Cover......Page 1
Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering......Page 4
Copyright......Page 5
Contents......Page 6
Contributors......Page 16
About the Editors......Page 20
Section Editors......Page 22
Introduction......Page 26
Section 1: Radar signal processing......Page 28
1.1. Introduction......Page 30
1.2.1. Radar Waveforms......Page 31
1.2.2. Waveform Performance Metrics......Page 36
1.3.1. Transmitter Effects......Page 39
1.3.2. Receive Effects......Page 43
1.4. Holistic Waveform Implementation and Design......Page 46
1.4.1. Polyphase-Coded FM......Page 47
1.4.2. Spectrum-Shaped FM Waveforms......Page 51
1.4.3. Transmitter-in-the-Loop Optimization......Page 58
1.5.1. Spatial Modulation......Page 62
1.5.2. Holistic Wideband MIMO Radar......Page 65
References......Page 71
2.1. Introduction......Page 78
2.2.1.1. A Nonassociative Product of Vectors......Page 84
2.2.1.2. An Associative Product of Vectors......Page 88
2.2.1.3. The Geometric Product of Vectors......Page 93
2.2.2.1. Geometric Algebra in Two Dimensions......Page 101
2.2.2.2. Geometric Algebra in Three Dimensions......Page 104
2.2.2.3. Geometric Algebra in Three Dimensions......Page 106
2.2.2.4. Caution-The Pseudoscalar is Not Simply -1 in Higher Dimensions......Page 108
2.2.2.5. Geometric Product of Multivectors......Page 109
2.2.3. What is a Complex Number?......Page 114
2.2.3.1. Rotation of Vectors via Spinors......Page 116
2.2.4.1. N-Dimensional Complex Vector as a 2N-Dimensional Real Vector......Page 119
2.2.4.2. Geometric Interpretation of a Complex Data Vector as a Spinor Expansion......Page 121
2.2.4.3. Projecting a Vector into a Subspace......Page 125
2.2.4.3.1. Examples......Page 127
2.2.5. What is a Complex Matrix?......Page 130
2.2.5.1. Geometry of the Matrix Inverse......Page 136
2.3.1. Hermitian Inner Product......Page 140
2.3.2.1. Multivariate Gaussian PDF and a Simple Detection Problem......Page 146
2.3.2.2. A Geometric Approach to Formulating Detectors......Page 151
2.3.3.1. Linear Processing to Steer Nulls......Page 155
2.3.3.2. Geometric Approach to Designing a Notch Filter......Page 158
2.3.3.3. Choosing the Frequencies That Define the Constraint Subspace......Page 163
2.3.3.4. Generalized Sidelobe Canceller......Page 165
2.4. Conclusion-Future Research Opportunities......Page 175
References......Page 176
3.1. Background......Page 180
3.2. Early Research Contributions......Page 183
3.3. Enabling Hardware and Processing Technologies......Page 184
3.4.1. Waveform Design......Page 186
3.4.1.1. Deterministic, Known Target Impulse Response......Page 187
3.4.1.2. Random Target Impulse Response......Page 190
3.4.2. Sequential Hypothesis Testing......Page 195
3.4.2.1. Binary Sequential Hypothesis Testing......Page 196
3.4.2.2. Sequential Testing with Multiple Hypotheses......Page 198
3.4.3. Partially Observable Markov Decision Process......Page 199
3.5. Canonical Examples......Page 200
3.5.1.1. Waveform Design......Page 201
3.5.1.2. Detection Performance......Page 203
3.5.1.3. Information Gained......Page 205
3.5.2. Detecting a Known Signal With a Nuisance Parameter......Page 206
3.5.2.1. Waveform Design Applied to Adaptive Beamshaping......Page 207
3.5.2.2. Carryover and Adaptation Performance Gains......Page 209
3.5.3. Parallel Estimation......Page 212
3.5.4. Summary......Page 218
References......Page 219
4.1. List of Symbols and Functions......Page 224
4.2. Introduction......Page 225
4.3. Problem Statement and Motivations......Page 227
4.4.1. Regular Models......Page 228
4.4.2. MS-Unbiased Estimators and the MCRB......Page 229
4.4.3. The Mismatched Maximum Likelihood (MML) Estimator......Page 232
4.4.4. A Particular Case: The MCRB as a Bound on the Mean Square Error (MSE)......Page 233
4.4.5. The Constrained MCRB: CMCRB......Page 234
4.4.5.1. The MCRB for the intrinsic parameter vector......Page 235
MS-unbiasedness and MCRB in ξ0......Page 236
4.5. Two Illustrative Examples......Page 237
4.6. The MCRB for the Estimation of the Scatter Matrix in the Family of CES Distributions......Page 242
4.6.1. Misspecified Estimation of the Scatter Matrix With Perfectly Known Extra Parameters......Page 243
4.6.1.1. Case Study 1. Assumed pdf: complex Normal; true pdf: t-student.......Page 244
4.6.1.2. Case Study 2. Assumed pdf: complex Normal, true pdf: Generalized Gaussian......Page 249
4.6.1.3. Case Study 3. Assumed pdf: Generalized Gaussian; true pdf: t-student......Page 251
4.6.2. Misspecified Joint Estimation of the Scatter Matrix and of the Extra Parameters......Page 257
4.6.2.1. Derivation of the constrained MML (CMML) estimator......Page 258
Evaluation of the matrix Aθ0......Page 260
Evaluation of the matrix Bθ0......Page 261
4.6.2.3. Performance analysis......Page 262
4.7.1. The ANMF Detector......Page 267
4.7.2. Detection Performance......Page 269
4.8. Conclusions......Page 272
Appendix B. A Generalization of the Bangs Formula Under Misspecification......Page 273
Compact Expression for the Matrix Bθ......Page 274
Compact Expression for the Matrix Aθ......Page 275
References......Page 276
5.2. Characteristics of Multistatic Radar......Page 280
5.4. Signal Processing in Multistatic Radar......Page 283
5.5. Target Detection......Page 284
5.6. Target Resolution......Page 285
5.7. Target Localization......Page 286
5.8. Synchronization Considerations for Multistatic Radar......Page 289
5.9.1. NetRAD......Page 290
5.9.2. NeXtRAD......Page 295
5.9.3. Calibration of Multistatic Polarmetric Radar......Page 296
5.9.4. Corner Reflectors FEKO Simulation......Page 298
5.10. Conclusions......Page 300
References......Page 301
6.1. Introduction......Page 304
6.2. Temporal Sparsity......Page 307
6.2.1. Sparse Sampling in Range......Page 308
6.2.2. Sparse Sampling in Range and Doppler......Page 310
6.3.1. Recovery of Missing or Corrupted Spectral Information......Page 314
6.3.2. Sub- or Co-prime Sampling in the Spectral Domain......Page 316
6.4.1. Direction-of-Arrival (DOA)......Page 318
6.4.1.2. DOA with a 2D array......Page 320
6.4.2. 3D-SAR......Page 321
6.4.2.1. Experimental results......Page 323
6.5. Group Sparsity......Page 327
6.5.2. Example: SIMO Radar Network......Page 329
6.5.3. Example: MIMO Radar Network......Page 332
6.5.4. Example: SFN Radar......Page 333
6.5.4.1. Signal model......Page 335
6.5.4.2. Verification......Page 336
6.6. Conclusion......Page 339
References......Page 340
Further Reading......Page 342
7.1.1. System Design Challenges: Size and Cost......Page 344
7.1.3. Antenna Systems......Page 345
7.1.4. Interference Challenges......Page 347
7.1.5. Automotive Radar: Trends and Standardization Efforts......Page 348
7.2.1. Propagation Properties in Millimeter-Wave......Page 349
7.2.2. Millimeter-Wave Radar Equation......Page 350
7.2.3. Ray Tracing for Millimeter-Wave Radar......Page 351
7.2.4. Clutter in Millimeter-Wave CMOS Radar......Page 354
7.3.1. Time-Bandwidth Product and Radar Resolution......Page 355
7.3.2. Linear FM and FMCW Radar......Page 356
7.3.3. Stepped Frequency Radar......Page 358
7.3.4. Pseudo-Random Stepped Frequency Radar......Page 360
7.3.5. Processing a PRSF Waveform......Page 363
7.3.5.1. Waveform repetition for M-times......Page 364
7.3.6. Adaptive Radar and Computationally Light Processing Techniques......Page 366
7.3.6.1. Detection of significant Doppler frequencies......Page 368
7.3.6.2. Robust range-Doppler estimation......Page 369
7.3.7. Intermediate Frequency Processing Technique......Page 370
7.4. Stochastic Geometry Technique for Modeling Automotive Consumer Radars......Page 373
7.4.1. Poisson Point Process Model......Page 374
7.4.2. Lattice Model......Page 375
7.4.3. Interference Analysis......Page 376
7.4.5. Performance Analysis and Optimization......Page 377
7.5.1. CMOS Technology Limitations......Page 381
7.5.2. Information Theory Limitations......Page 382
Acknowledgments......Page 386
References......Page 387
Section 2: Communications......Page 392
8.1. Introduction......Page 394
8.2.1. Point-to-Point MIMO......Page 397
8.2.2. Toward Massive MIMO......Page 399
8.2.3. MU-MIMO......Page 400
8.2.3.1. UL (reverse link)......Page 401
8.2.3.2. DL (forward link)......Page 402
8.3. Massive MIMO Precoding......Page 403
8.3.1. Basic Precoding Schemes......Page 404
8.3.2. Constant Envelop Precoding......Page 407
8.4. Signal Detection......Page 408
8.5. Power Control......Page 410
8.6.1. Channel Estimation......Page 411
8.6.2. Pilot Contamination......Page 413
8.6.2.1. Mitigating pilot contamination effects......Page 419
8.7. Future Research Challenges......Page 421
References......Page 423
9.1. Introduction......Page 430
9.2. End-to-End Channel Modeling......Page 431
9.3. One-Way Network Beamforming......Page 432
9.3.1.1. Single-user networks......Page 433
9.3.1.2. SNR-maximization with perfect CSI......Page 435
9.3.1.3. SNR-per-unit-power maximization......Page 437
9.3.1.4. Partial CSI......Page 439
9.3.1.5. MSE-minimization and received signal power maximization......Page 441
9.3.1.6. Multi-user networks......Page 443
9.3.1.7. Orthogonal user channels......Page 444
9.3.1.8. With user interference and perfect CSI......Page 447
9.3.1.9. With user interference and partial CSI......Page 448
9.3.2. Networks With Frequency-Selective Channels......Page 449
9.3.2.1. Single-user networks......Page 450
9.4. Two-Way Network Beamforming......Page 454
9.4.1. Synchronous Networks......Page 455
9.4.1.1. Total power minimization......Page 458
9.4.1.2. Max-min SNR approach......Page 462
9.4.1.3. Sum-rate maximization......Page 465
9.4.1.4. Individual power constraints......Page 466
9.4.2. Asynchronous Networks......Page 467
9.4.2.1. End-to-end channel model......Page 469
9.4.2.3. Max-min SNR fair design approach......Page 470
9.4.2.5. Single-carrier post-channel equalization......Page 477
9.4.2.6. Total MSE minimization......Page 479
9.4.2.7. Sum-rate maximization......Page 482
9.4.2.8. Total power minimization......Page 484
9.4.2.9. Single-carrier pre-channel equalization......Page 487
9.4.2.10. Joint pre-channel and post-channel equalization......Page 488
9.4.3. Networks With Frequency-Selective Transceiver-Relay Links......Page 491
9.4.3.1. OFDM-based channel equalization......Page 492
9.4.3.2. Filter-and-forward relaying......Page 493
9.5.1. One-Way Network Beamforming......Page 494
9.5.2. Two-Way Network Beamforming......Page 495
9.6. Summary......Page 500
References......Page 501
10.1.1. Practical SWIPT Receiver......Page 506
10.1.2. Multiantenna SWIPT......Page 508
10.2. Joint Information and Energy Beamforming Design for SWIPT......Page 509
10.2.1. Beamforming Design for SWIPT System With Separate IRs and ERs......Page 511
10.2.1.1. System model......Page 512
10.2.1.2. Problem formulation......Page 513
10.2.1.3. Optimal solution via SDR......Page 514
10.2.1.4. Numerical examples......Page 516
10.2.2.1. System model......Page 518
10.2.2.3. Optimal beamforming solution......Page 520
10.2.2.4. Numerical results......Page 522
10.2.3.1. System model......Page 524
10.2.3.2. Problem formulation......Page 525
10.2.3.4. Numerical results......Page 526
10.3.1. Multipoint-to-Multipoint SWIPT......Page 529
10.3.3. CSI Acquisition at Transmitter......Page 530
References......Page 531
Section 3: Sensor array processing......Page 534
11.1. Introduction......Page 536
11.2.2. The Role of Array Geometry......Page 538
11.2.3. Parameter Identifiability......Page 540
11.3.1.1. Problem formulation......Page 542
11.3.1.2. Convex relaxation......Page 543
11.3.1.3. lq optimization......Page 545
11.3.1.4. Maximum likelihood estimation (MLE)......Page 546
11.3.2. Sparse Representation and DOA Estimation: The Link and the Gap......Page 548
11.4.1. Data Model......Page 549
11.4.2. l2,0 optimization......Page 550
11.4.3.1. l2,1 optimization......Page 551
11.4.3.3. Another dimensionality reduction technique......Page 552
11.4.4. l2,q optimization......Page 554
11.4.5.1. Generalized least squares......Page 555
11.4.5.2. SPICE......Page 556
11.4.6. Maximum Likelihood Estimation......Page 558
11.4.7. Remarks on Grid Selection......Page 559
11.5.1.1. Data model......Page 560
11.5.1.2. l1 optimization......Page 561
11.5.2.2. Algorithms......Page 563
11.6. Gridless Sparse Methods......Page 564
11.6.2. Vandermonde Decomposition of Toeplitz Covariance Matrices......Page 565
11.6.3. The Single Snapshot Case......Page 568
11.6.3.2. Atomic l0 norm......Page 569
11.6.3.3. Atomic norm......Page 570
11.6.3.4. Hankel-based nuclear norm......Page 574
11.6.3.5. Connection between ANM and EMaC......Page 575
11.6.3.6. Covariance fitting method: Gridless SPICE (GLS)......Page 577
11.6.3.7. Connection between ANM and GLS......Page 578
11.6.4.1. Gridless SPICE (GLS)......Page 580
11.6.4.2. SMV-based atomic norm minimization (ANM-SMV)......Page 582
11.6.4.3. Nuclear norm minimization followed by MUSIC (NNM-MUSIC)......Page 583
11.6.5.1. A general framework......Page 584
11.6.5.2. Atomic 0 norm......Page 585
11.6.5.3. Atomic norm......Page 586
11.6.5.4. Hankel-based nuclear norm......Page 588
11.6.6. Reweighted Atomic Norm Minimization......Page 589
11.6.6.1. A smooth surrogate for ZA,0......Page 590
11.6.6.2. A locally convergent iterative algorithm......Page 591
11.6.6.3. Interpretation as RAM......Page 592
11.6.7.1. The case of L < M......Page 593
11.6.7.2. The case of L M......Page 594
11.6.8. Computational Issues and Solutions......Page 595
11.6.8.1. Dimensionality reduction......Page 596
11.6.8.2. Alternating direction method of multipliers (ADMM)......Page 597
11.7. Future Research Challenges......Page 598
11.8. Conclusions......Page 599
References......Page 600
Section 4: Acoustic Signal Processing......Page 610
Chapter 12: Beamforming techniques using microphone arrays......Page 612
12.1. Introduction......Page 614
12.2.1. Narrowband Beamforming......Page 615
12.2.2. Wideband Beamforming......Page 620
12.3. Basic Approaches in Wideband Beamforming......Page 624
12.3.1. Superdirective Beamformer......Page 627
12.3.2. Linearly Constrained Minimum Variance (LCMV)-Based Adaptive Beamforming Techniques......Page 628
12.3.3. Practical Considerations in Covariance Matrix Estimation in LCMV-Based Beamformers......Page 631
12.4. Postfilter by PSD Estimation in Beamspace......Page 632
12.4.1. Problem Setup......Page 633
12.4.2. Beamforming and Its Output PSD......Page 634
12.4.3. PSD Estimation in Beamspace......Page 635
12.4.4. Postfiltering for Source Separation......Page 636
References......Page 637
Index......Page 640
Back Cover......Page 654

✦ Subjects

Signal processing;TECHNOLOGY & ENGINEERING--Mechanical;Electronic books;TECHNOLOGY & ENGINEERING -- Mechanical