LEADER 05434nam 22006854a 450 001 9910829830203321 005 20230828213022.0 010 $a1-280-27762-9 010 $a9786610277629 010 $a0-470-36185-9 010 $a0-471-73348-2 010 $a0-471-73346-6 035 $a(CKB)1000000000355434 035 $a(EBL)239370 035 $a(OCoLC)475950549 035 $a(SSID)ssj0000239010 035 $a(PQKBManifestationID)11199903 035 $a(PQKBTitleCode)TC0000239010 035 $a(PQKBWorkID)10235248 035 $a(PQKB)11575535 035 $a(MiAaPQ)EBC239370 035 $a(EXLCZ)991000000000355434 100 $a20041215d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aRobust adaptive beamforming$b[electronic resource] /$fedited by Jian Li and Petre Stoica 210 $aHoboken, NJ $cJohn Wiley$d2006 215 $a1 online resource (436 p.) 225 1 $aWiley Series in Telecommunications and Signal Processing ;$vv.88 300 $aDescription based upon print version of record. 311 $a0-471-67850-3 320 $aIncludes bibliographical references and index. 327 $aRobust Adaptive Beamforming; CONTENTS; Contributors; Preface; 1 Robust Minimum Variance Beamforming; 1.1 Introduction; 1.2 A Practical Example; 1.3 Robust Weight Selection; 1.4 A Numerical Example; 1.5 Ellipsoidal Modeling; 1.6 Uncertainty Ellipsoid Calculus; 1.7 Beamforming Example with Multiplicative Uncertainties; 1.8 Summary; Appendix: Notation and Glossary; References; 2 Robust Adaptive Beamforming Based on Worst-Case Performance Optimization; 2.1 Introduction; 2.2 Background and Traditional Approaches; 2.3 Robust Minimum Variance Beamforming Based on Worst-Case Performance Optimization 327 $a2.4 Numerical Examples2.5 Conclusions; Appendix 2.A: Proof of Lemma 1; Appendix 2.B: Proof of Lemma 2; Appendix 2.C: Proof of Lemma 3; Appendix 2.D: Proof of Lemma 4; Appendix 2.E: Proof of Lemma 5; References; 3 Robust Capon Beamforming; 3.1 Introduction; 3.2 Problem Formulation; 3.3 Standard Capon Beamforming; 3.4 Robust Capon Beamforming with Single Constraint; 3.5 Capon Beamforming with Norm Constraint; 3.6 Robust Capon Beamforming with Double Constraints; 3.7 Robust Capon Beamforming with Constant Beamwidth and Constant Powerwidth 327 $a3.8 Rank-Deficient Robust Capon Filter-Bank Spectral Estimator3.9 Adaptive Imaging for Forward-Looking Ground Penetrating Radar; 3.10 Summary; Acknowledgments; Appendix 3.A: Relationship between RCB and the Approach in [14]; Appendix 3.B: Calculating the Steering Vector; Appendix 3.C: Relationship between RCB and the Approach in [15]; Appendix 3.D: Analysis of Equation (3.72); Appendix 3.E: Rank-Deficient Capon Beamformer; Appendix 3.F: Conjugate Symmetry of the Forward-Backward FIR; Appendix 3.G: Formulations of NCCF and HDI; Appendix 3.H: Notations and Abbreviations; References 327 $a4 Diagonal Loading for Finite Sample Size Beamforming: An Asymptotic Approach4.1 Introduction and Historical Review; 4.2 Asymptotic Output SINR with Diagonal Loading; 4.3 Estimating the Asymptotically Optimum Loading Factor; 4.4 Characterization of the Asymptotically Optimum Loading Factor; 4.5 Summary and Conclusions; Acknowledgments; Appendix 4.A: Proof of Proposition 1; Appendix 4.B: Proof of Lemma 1; Appendix 4.C: Derivation of the Consistent Estimator; Appendix 4.D: Proof of Proposition 2; References; 5 Mean-Squared Error Beamforming for Signal Estimation: A Competitive Approach 327 $a5.1 Introduction5.2 Background and Problem Formulation; 5.3 Minimax MSE Beamforming for Known Steering Vector; 5.4 Random Steering Vector; 5.5 Practical Considerations; 5.6 Numerical Examples; 5.7 Summary; Acknowledgments; References; 6 Constant Modulus Beamforming; 6.1 Introduction; 6.2 The Constant Modulus Algorithm; 6.3 Prewhitening and Rank Reduction; 6.4 Multiuser CMA Techniques; 6.5 The Analytical CMA; 6.6 Adaptive Prewhitening; 6.7 Adaptive ACMA; 6.8 DOA Assisted Beamforming of Constant Modulus Signals; 6.9 Concluding Remarks; Acknowledgment; References; 7 Robust Wideband Beamforming 327 $a7.1 Introduction 330 $aThe latest research and developments in robust adaptive beamformingRecent work has made great strides toward devising robust adaptive beamformers that vastly improve signal strength against background noise and directional interference. This dynamic technology has diverse applications, including radar, sonar, acoustics, astronomy, seismology, communications, and medical imaging. There are also exciting emerging applications such as smart antennas for wireless communications, handheld ultrasound imaging systems, and directional hearing aids.Robust Adaptive Beamforming compiles t 410 0$aWiley Series in Telecommunications and Signal Processing 606 $aBeamforming 606 $aAdaptive antennas 606 $aAntenna radiation patterns 615 0$aBeamforming. 615 0$aAdaptive antennas. 615 0$aAntenna radiation patterns. 676 $a621.382/4 676 $a621.3824 701 $aLi$b Jian$0846202 701 $aStoica$b Petre$027522 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829830203321 996 $aRobust adaptive beamforming$93989345 997 $aUNINA