Radar waveform design based on optimization theory:
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Stevenage
Institution of Engineering & Technology, IET
[2020]
|
| Schriftenreihe: | Radar, Sonar and Navigation Ser
|
| Online-Zugang: | DE-706 DE-29 |
| Beschreibung: | Intro -- Contents -- About the editors -- Foreword -- Notation -- 1. On recent advances of binary sequence designs and their applications | Ronghao Lin and Jian Li -- 1.1 Introduction -- 1.2 Algebraic methods -- 1.2.1 Barker sequences -- 1.2.2 Legendre sequences -- 1.2.3 m-Sequences -- 1.2.4 Gold sequences -- 1.2.5 Almost perfect autocorrelation sequences -- 1.2.6 Summary -- 1.3 Computation algorithms -- 1.3.1 Iterative twisted approximation -- 1.3.2 CD algorithm -- 1.3.3 CAN(PeCAN) family of algorithms -- 1.3.4 Summary -- 1.4 Conclusions -- References -- 2. Quadratic optimization for unimodular sequence synthesis and applications | Guolong Cui, Xianxiang Yu, Goffredo Foglia, Yongwei Huang, and Jian Li -- 2.1 Introduction -- 2.2 Problem formulation -- 2.3 Iterative algorithms for both the continuous and discrete phase cases -- 2.3.1 Iterative algorithm for continuous phase case -- 2.3.2 Iterative algorithm for discrete phase case -- 2.3.3 Power method-like approaches for both the continuous and discrete phase cases -- 2.4 Numerical examples -- 2.4.1 Code design to optimize radar detection performance -- 2.4.2 Spectrally compatible waveform design -- 2.5 Conclusions -- Acknowledgments -- References -- 3. A computational design of phase-only (possibly binary) sequences for radar systems | Mohammad Alaee-Kerahroodi, Augusto Aubry, Mohammad Mahdi Naghsh, Antonio De Maio, and Mahmoud Modarres-Hashemi -- 3.1 Introduction -- 3.1.1 Background and previous works -- 3.1.2 Contribution and organization -- 3.2 Problem formulation -- 3.3 CD code optimization -- 3.3.1 Continuous phase code design -- 3.3.2 Discrete phase code design -- 3.4 Numerical examples -- 3.4.1 Sequence design with good PSL -- 3.4.2 Sequence design with good ISL -- 3.4.3 Pareto-optimized solution and designing binary sequences -- 3.5 Conclusions -- Appendix A: Proof of Lemma 3.1 Appendix B: Derivation of the feasibility set -- Appendix C: Proof of Lemma 3.2 -- References -- 4. Constrained radar code design for spectrally congested environments via quadratic optimization | Marco Piezzo, Yongwei Huang, Augusto Aubry, and Antonio De Maio -- 4.1 Introduction -- 4.2 System model -- 4.3 Figures of merit and constraints -- 4.3.1 Detection probability -- 4.3.2 Energy and similarity constraints -- 4.3.3 Spectral compatibility constraint -- 4.3.4 Bandwidth priority constraint -- 4.4 QCQP's solution methods via rank-one matrix decomposition -- 4.5 Radarwaveformdesign in a spectrally crowded environment under similarity and spectral coexistence constraints -- 4.5.1 Code design optimization problem -- 4.5.2 Performance analysis -- 4.6 Radar waveform design in a spectrally crowded environment under similarity, energy modulation, and spectral coexistence constraints -- 4.6.1 Code design optimization problem -- 4.6.2 Performance analysis -- 4.7 Radar waveform design under similarity, bandwidth priority, and spectral coexistence constraints -- 4.7.1 Code design optimization problem -- 4.7.2 Performance analysis -- 4.8 Conclusions -- A.1 Proof of Theorem 4.1 -- A.2 Proof of Theorem 4.2 -- A.3 Proof of Theorem 4.3 -- A.4 Proof of Proposition 4.1: SDP relaxation tightness for (4.36) -- References -- 5. Robust transmit code and receive filter design for extended targets detection in clutter | Seyyed Mohammad Karbasi, Augusto Aubry, Antonio De Maio, Mohammad Hassan Bastani, and Alfonso Farina -- 5.1 Introduction -- 5.2 Target and signal model -- 5.2.1 Target model -- 5.2.2 Signal model -- 5.3 Problem formulation -- 5.3.1 Filter matrix optimization -- 5.3.2 Code matrix optimization -- 5.4 Filter and code synthesis -- 5.4.1 Filter synthesis -- 5.4.2 Code synthesis -- 5.5 Special case of practical importance: spherical uncertainty set 5.6 Numerical results -- 5.6.1 TAA uncertainty set size analysis -- 5.6.2 TAA uncertainty set for different target types -- 5.6.3 Spherical uncertainty set -- 5.7 Conclusions -- Appendix A: Proof of Lemma 5.1 -- Appendix B: Proof of Proposition 5.1 -- References -- 6. Optimizing radar transceiver for Doppler processing via non-convex programming | Augusto Aubry, Mohammad Mahdi Naghsh, Ehsan Raei, Mohammad Alaee-Kerahroodi, and Bhavani Shankar Mysore -- 6.1 Introduction -- 6.2 Radar system operation -- 6.2.1 Transmit waveform -- 6.2.2 Receiver processing and signal model -- 6.2.3 Clutter and signal independent disturbance characterization -- 6.2.4 Performance metric for Doppler processing -- 6.3 Problem formulation and design issues -- 6.3.1 Constraints and optimization problem -- 6.3.2 Filter bank optimization: solution to problemPw (n) -- 6.3.3 Radar code optimization: solution to problemPs (n) -- 6.3.4 Transmit-receive system design: optimization procedure -- 6.4 Performance analysis -- 6.4.1 Monotonicity of the proposed method and the impact of similarity constraint -- 6.4.2 Impact of colored interference -- 6.4.3 Effect of target Doppler shift interval -- 6.4.4 Impact of receive filter bank size -- 6.4.5 Impact of sequence length on performance -- 6.4.6 Performance comparison -- 6.5 Conclusions -- Appendix A: Proof of Proposition 6.1 -- Appendix B: Proof of Proposition 6.2 -- Appendix C: Proof of Lemma 6.1 -- References -- 7. Radar waveform design via the majorization-minimization framework | Linlong Wu and Daniel P. Palomar -- 7.1 Introduction -- 7.2 Preliminaries: the MM method -- 7.2.1 The vanilla MM method -- 7.2.2 Convergence analysis -- 7.2.3 Acceleration schemes -- 7.2.4 Extension to the maximin case -- 7.3 Joint design of transmit waveform and receive filter -- 7.3.1 System model and problem formulation 7.3.2 MM-based method for joint design with multiple constraints -- 7.3.3 Numerical experiments -- 7.4 Robust joint design for the worst-case SINR maximization -- 7.4.1 Problem formulation -- 7.4.2 MM-based method for robust joint design -- 7.4.3 Numerical experiments -- 7.5 Conclusion -- Appendix A: Proof of Lemma 7.1 -- Appendix B: Proof of Lemma 7.4 -- Appendix C: Proof of Lemma 7.5 -- Acknowledgment -- References -- 8. Lagrange programming neural network for radar waveform design | Junli Liang, Yang Jing, Hing Cheung So, Chi Sing Leung, Jian Li, and Alfonso Farina -- 8.1 Introduction -- 8.2 Basics of LPNN -- 8.2.1 Problem statement -- 8.2.2 Lagrange programming neural network -- 8.3 LPNN for waveform design with spectral constraints -- 8.3.1 Problem statement -- 8.3.2 Algorithm development -- 8.3.3 LPNN stability analysis -- 8.4 LPNN for designing waveform with low PSL -- 8.4.1 Problem statement -- 8.4.2 Algorithm description -- 8.4.3 LPNN stability analysis -- 8.4.4 Summary of proposed algorithm -- 8.5 Numerical examples -- 8.5.1 Experiment 1: Flat spectrum waveform design -- 8.5.2 Experiment 2: Spectrally constrained waveform design for radar -- 8.5.3 Experiment 3: Region of interest around main lobe -- 8.5.4 Experiment 4: Region of interest on one side of main lobe -- 8.5.5 Experiment 5: Low-sidelobe autocorrelation level -- 8.6 Conclusions -- A.1 Positive definiteness of Hessian matrix of (8.48) -- A.2 Solution to (8.58) -- A.3 Adaptive selection scheme of C0 -- A.3.1 On positive definiteness of ∇2 θθLx̄ -- A.3.2 On positive definiteness of Z0 -- A.3.3 On positive definiteness of Hessian matrix H of (8.49) -- References -- 9. Cognitive local ambiguity function shaping with spectral coexistence and experiments | Guolong Cui, Jing Yang, Xiangxiang Yu, and Lingjiang Kong -- 9.1 Introduction -- 9.2 Problem formulation 9.2.1 Weighted integrated sidelobe level -- 9.2.2 Spectral coexistence -- 9.2.3 Optimization problem -- 9.3 Iterative sequential quadratic optimization algorithm -- 9.4 Numerical results -- 9.4.1 Simulation results -- 9.4.2 Experimental results -- 9.5 Conclusions -- Appendix A: Proof of Proposition 9.1 -- Appendix B: Proof of (9.17) -- Appendix C: Proof of Proposition 9.2 -- Acknowledgments -- References -- 10. Relative entropy-based waveform design for MIMO radar | Bo Tang and Jun Tang -- 10.1 Introduction -- 10.2 Signal model and problem formulation -- 10.2.1 Signal model -- 10.2.2 Problem formulation -- 10.3 Two-stage algorithm design -- 10.3.1 Synthesis of energy-constrained waveforms -- 10.3.2 Convergence and computational complexity analysis -- 10.3.3 Extension to the synthesis of constant-moduluswaveforms -- 10.3.4 Extension to the synthesis of similarity-constrained waveforms -- 10.4 One-stage algorithm design -- 10.4.1 Minorizing Part A -- 10.4.2 Minorizing Part B -- 10.4.3 Minorizing Part C -- 10.4.4 The minorized problem at the (k + 1)th iteration -- 10.4.5 Convergence and computational complexity analysis -- 10.4.6 Extension to include other constraints -- 10.4.7 Accelerated schemes for the one-stage methods -- 10.5 Numerical examples -- 10.6 Concluding remarks -- Appendix A: Proof of (10.19) -- Appendix B: Proof of Lemma 10.1 -- Appendix C: Anintroduction to minorization-maximization -- Acknowledgment -- References -- Index |
| Beschreibung: | 1 Online-Ressource (347 Seiten) |
| ISBN: | 9781785619441 9781523133499 |
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| 245 | 1 | 0 | |a Radar waveform design based on optimization theory |c edited by Guolong Cui, Antonio De Maio, Alfonso Farina and Jian Li |
| 264 | 1 | |a Stevenage |b Institution of Engineering & Technology, IET |c [2020] | |
| 300 | |a 1 Online-Ressource (347 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Radar, Sonar and Navigation Ser | |
| 500 | |a Intro -- Contents -- About the editors -- Foreword -- Notation -- 1. On recent advances of binary sequence designs and their applications | Ronghao Lin and Jian Li -- 1.1 Introduction -- 1.2 Algebraic methods -- 1.2.1 Barker sequences -- 1.2.2 Legendre sequences -- 1.2.3 m-Sequences -- 1.2.4 Gold sequences -- 1.2.5 Almost perfect autocorrelation sequences -- 1.2.6 Summary -- 1.3 Computation algorithms -- 1.3.1 Iterative twisted approximation -- 1.3.2 CD algorithm -- 1.3.3 CAN(PeCAN) family of algorithms -- 1.3.4 Summary -- 1.4 Conclusions -- References -- 2. Quadratic optimization for unimodular sequence synthesis and applications | Guolong Cui, Xianxiang Yu, Goffredo Foglia, Yongwei Huang, and Jian Li -- 2.1 Introduction -- 2.2 Problem formulation -- 2.3 Iterative algorithms for both the continuous and discrete phase cases -- 2.3.1 Iterative algorithm for continuous phase case -- 2.3.2 Iterative algorithm for discrete phase case -- 2.3.3 Power method-like approaches for both the continuous and discrete phase cases -- 2.4 Numerical examples -- 2.4.1 Code design to optimize radar detection performance -- 2.4.2 Spectrally compatible waveform design -- 2.5 Conclusions -- Acknowledgments -- References -- 3. A computational design of phase-only (possibly binary) sequences for radar systems | Mohammad Alaee-Kerahroodi, Augusto Aubry, Mohammad Mahdi Naghsh, Antonio De Maio, and Mahmoud Modarres-Hashemi -- 3.1 Introduction -- 3.1.1 Background and previous works -- 3.1.2 Contribution and organization -- 3.2 Problem formulation -- 3.3 CD code optimization -- 3.3.1 Continuous phase code design -- 3.3.2 Discrete phase code design -- 3.4 Numerical examples -- 3.4.1 Sequence design with good PSL -- 3.4.2 Sequence design with good ISL -- 3.4.3 Pareto-optimized solution and designing binary sequences -- 3.5 Conclusions -- Appendix A: Proof of Lemma 3.1 | ||
| 500 | |a Appendix B: Derivation of the feasibility set -- Appendix C: Proof of Lemma 3.2 -- References -- 4. Constrained radar code design for spectrally congested environments via quadratic optimization | Marco Piezzo, Yongwei Huang, Augusto Aubry, and Antonio De Maio -- 4.1 Introduction -- 4.2 System model -- 4.3 Figures of merit and constraints -- 4.3.1 Detection probability -- 4.3.2 Energy and similarity constraints -- 4.3.3 Spectral compatibility constraint -- 4.3.4 Bandwidth priority constraint -- 4.4 QCQP's solution methods via rank-one matrix decomposition -- 4.5 Radarwaveformdesign in a spectrally crowded environment under similarity and spectral coexistence constraints -- 4.5.1 Code design optimization problem -- 4.5.2 Performance analysis -- 4.6 Radar waveform design in a spectrally crowded environment under similarity, energy modulation, and spectral coexistence constraints -- 4.6.1 Code design optimization problem -- 4.6.2 Performance analysis -- 4.7 Radar waveform design under similarity, bandwidth priority, and spectral coexistence constraints -- 4.7.1 Code design optimization problem -- 4.7.2 Performance analysis -- 4.8 Conclusions -- A.1 Proof of Theorem 4.1 -- A.2 Proof of Theorem 4.2 -- A.3 Proof of Theorem 4.3 -- A.4 Proof of Proposition 4.1: SDP relaxation tightness for (4.36) -- References -- 5. Robust transmit code and receive filter design for extended targets detection in clutter | Seyyed Mohammad Karbasi, Augusto Aubry, Antonio De Maio, Mohammad Hassan Bastani, and Alfonso Farina -- 5.1 Introduction -- 5.2 Target and signal model -- 5.2.1 Target model -- 5.2.2 Signal model -- 5.3 Problem formulation -- 5.3.1 Filter matrix optimization -- 5.3.2 Code matrix optimization -- 5.4 Filter and code synthesis -- 5.4.1 Filter synthesis -- 5.4.2 Code synthesis -- 5.5 Special case of practical importance: spherical uncertainty set | ||
| 500 | |a 5.6 Numerical results -- 5.6.1 TAA uncertainty set size analysis -- 5.6.2 TAA uncertainty set for different target types -- 5.6.3 Spherical uncertainty set -- 5.7 Conclusions -- Appendix A: Proof of Lemma 5.1 -- Appendix B: Proof of Proposition 5.1 -- References -- 6. Optimizing radar transceiver for Doppler processing via non-convex programming | Augusto Aubry, Mohammad Mahdi Naghsh, Ehsan Raei, Mohammad Alaee-Kerahroodi, and Bhavani Shankar Mysore -- 6.1 Introduction -- 6.2 Radar system operation -- 6.2.1 Transmit waveform -- 6.2.2 Receiver processing and signal model -- 6.2.3 Clutter and signal independent disturbance characterization -- 6.2.4 Performance metric for Doppler processing -- 6.3 Problem formulation and design issues -- 6.3.1 Constraints and optimization problem -- 6.3.2 Filter bank optimization: solution to problemPw (n) -- 6.3.3 Radar code optimization: solution to problemPs (n) -- 6.3.4 Transmit-receive system design: optimization procedure -- 6.4 Performance analysis -- 6.4.1 Monotonicity of the proposed method and the impact of similarity constraint -- 6.4.2 Impact of colored interference -- 6.4.3 Effect of target Doppler shift interval -- 6.4.4 Impact of receive filter bank size -- 6.4.5 Impact of sequence length on performance -- 6.4.6 Performance comparison -- 6.5 Conclusions -- Appendix A: Proof of Proposition 6.1 -- Appendix B: Proof of Proposition 6.2 -- Appendix C: Proof of Lemma 6.1 -- References -- 7. Radar waveform design via the majorization-minimization framework | Linlong Wu and Daniel P. Palomar -- 7.1 Introduction -- 7.2 Preliminaries: the MM method -- 7.2.1 The vanilla MM method -- 7.2.2 Convergence analysis -- 7.2.3 Acceleration schemes -- 7.2.4 Extension to the maximin case -- 7.3 Joint design of transmit waveform and receive filter -- 7.3.1 System model and problem formulation | ||
| 500 | |a 7.3.2 MM-based method for joint design with multiple constraints -- 7.3.3 Numerical experiments -- 7.4 Robust joint design for the worst-case SINR maximization -- 7.4.1 Problem formulation -- 7.4.2 MM-based method for robust joint design -- 7.4.3 Numerical experiments -- 7.5 Conclusion -- Appendix A: Proof of Lemma 7.1 -- Appendix B: Proof of Lemma 7.4 -- Appendix C: Proof of Lemma 7.5 -- Acknowledgment -- References -- 8. Lagrange programming neural network for radar waveform design | Junli Liang, Yang Jing, Hing Cheung So, Chi Sing Leung, Jian Li, and Alfonso Farina -- 8.1 Introduction -- 8.2 Basics of LPNN -- 8.2.1 Problem statement -- 8.2.2 Lagrange programming neural network -- 8.3 LPNN for waveform design with spectral constraints -- 8.3.1 Problem statement -- 8.3.2 Algorithm development -- 8.3.3 LPNN stability analysis -- 8.4 LPNN for designing waveform with low PSL -- 8.4.1 Problem statement -- 8.4.2 Algorithm description -- 8.4.3 LPNN stability analysis -- 8.4.4 Summary of proposed algorithm -- 8.5 Numerical examples -- 8.5.1 Experiment 1: Flat spectrum waveform design -- 8.5.2 Experiment 2: Spectrally constrained waveform design for radar -- 8.5.3 Experiment 3: Region of interest around main lobe -- 8.5.4 Experiment 4: Region of interest on one side of main lobe -- 8.5.5 Experiment 5: Low-sidelobe autocorrelation level -- 8.6 Conclusions -- A.1 Positive definiteness of Hessian matrix of (8.48) -- A.2 Solution to (8.58) -- A.3 Adaptive selection scheme of C0 -- A.3.1 On positive definiteness of ∇2 θθLx̄ -- A.3.2 On positive definiteness of Z0 -- A.3.3 On positive definiteness of Hessian matrix H of (8.49) -- References -- 9. Cognitive local ambiguity function shaping with spectral coexistence and experiments | Guolong Cui, Jing Yang, Xiangxiang Yu, and Lingjiang Kong -- 9.1 Introduction -- 9.2 Problem formulation | ||
| 500 | |a 9.2.1 Weighted integrated sidelobe level -- 9.2.2 Spectral coexistence -- 9.2.3 Optimization problem -- 9.3 Iterative sequential quadratic optimization algorithm -- 9.4 Numerical results -- 9.4.1 Simulation results -- 9.4.2 Experimental results -- 9.5 Conclusions -- Appendix A: Proof of Proposition 9.1 -- Appendix B: Proof of (9.17) -- Appendix C: Proof of Proposition 9.2 -- Acknowledgments -- References -- 10. Relative entropy-based waveform design for MIMO radar | Bo Tang and Jun Tang -- 10.1 Introduction -- 10.2 Signal model and problem formulation -- 10.2.1 Signal model -- 10.2.2 Problem formulation -- 10.3 Two-stage algorithm design -- 10.3.1 Synthesis of energy-constrained waveforms -- 10.3.2 Convergence and computational complexity analysis -- 10.3.3 Extension to the synthesis of constant-moduluswaveforms -- 10.3.4 Extension to the synthesis of similarity-constrained waveforms -- 10.4 One-stage algorithm design -- 10.4.1 Minorizing Part A -- 10.4.2 Minorizing Part B -- 10.4.3 Minorizing Part C -- 10.4.4 The minorized problem at the (k + 1)th iteration -- 10.4.5 Convergence and computational complexity analysis -- 10.4.6 Extension to include other constraints -- 10.4.7 Accelerated schemes for the one-stage methods -- 10.5 Numerical examples -- 10.6 Concluding remarks -- Appendix A: Proof of (10.19) -- Appendix B: Proof of Lemma 10.1 -- Appendix C: Anintroduction to minorization-maximization -- Acknowledgment -- References -- Index | ||
| 700 | 1 | |a Cui, Guolong |4 edt | |
| 700 | 1 | |a De Maio, Antonio |4 edt | |
| 700 | 1 | |a Farina, Alfonso |4 edt | |
| 700 | 1 | |a Li, Jian |4 edt | |
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| 966 | e | |u https://doi.org/10.1049/SBRA533E |l DE-29 |p ZDB-100-IET |x Verlag |3 Volltext | |
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On recent advances of binary sequence designs and their applications | Ronghao Lin and Jian Li -- 1.1 Introduction -- 1.2 Algebraic methods -- 1.2.1 Barker sequences -- 1.2.2 Legendre sequences -- 1.2.3 m-Sequences -- 1.2.4 Gold sequences -- 1.2.5 Almost perfect autocorrelation sequences -- 1.2.6 Summary -- 1.3 Computation algorithms -- 1.3.1 Iterative twisted approximation -- 1.3.2 CD algorithm -- 1.3.3 CAN(PeCAN) family of algorithms -- 1.3.4 Summary -- 1.4 Conclusions -- References -- 2. Quadratic optimization for unimodular sequence synthesis and applications | Guolong Cui, Xianxiang Yu, Goffredo Foglia, Yongwei Huang, and Jian Li -- 2.1 Introduction -- 2.2 Problem formulation -- 2.3 Iterative algorithms for both the continuous and discrete phase cases -- 2.3.1 Iterative algorithm for continuous phase case -- 2.3.2 Iterative algorithm for discrete phase case -- 2.3.3 Power method-like approaches for both the continuous and discrete phase cases -- 2.4 Numerical examples -- 2.4.1 Code design to optimize radar detection performance -- 2.4.2 Spectrally compatible waveform design -- 2.5 Conclusions -- Acknowledgments -- References -- 3. A computational design of phase-only (possibly binary) sequences for radar systems | Mohammad Alaee-Kerahroodi, Augusto Aubry, Mohammad Mahdi Naghsh, Antonio De Maio, and Mahmoud Modarres-Hashemi -- 3.1 Introduction -- 3.1.1 Background and previous works -- 3.1.2 Contribution and organization -- 3.2 Problem formulation -- 3.3 CD code optimization -- 3.3.1 Continuous phase code design -- 3.3.2 Discrete phase code design -- 3.4 Numerical examples -- 3.4.1 Sequence design with good PSL -- 3.4.2 Sequence design with good ISL -- 3.4.3 Pareto-optimized solution and designing binary sequences -- 3.5 Conclusions -- Appendix A: Proof of Lemma 3.1</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Appendix B: Derivation of the feasibility set -- Appendix C: Proof of Lemma 3.2 -- References -- 4. Constrained radar code design for spectrally congested environments via quadratic optimization | Marco Piezzo, Yongwei Huang, Augusto Aubry, and Antonio De Maio -- 4.1 Introduction -- 4.2 System model -- 4.3 Figures of merit and constraints -- 4.3.1 Detection probability -- 4.3.2 Energy and similarity constraints -- 4.3.3 Spectral compatibility constraint -- 4.3.4 Bandwidth priority constraint -- 4.4 QCQP's solution methods via rank-one matrix decomposition -- 4.5 Radarwaveformdesign in a spectrally crowded environment under similarity and spectral coexistence constraints -- 4.5.1 Code design optimization problem -- 4.5.2 Performance analysis -- 4.6 Radar waveform design in a spectrally crowded environment under similarity, energy modulation, and spectral coexistence constraints -- 4.6.1 Code design optimization problem -- 4.6.2 Performance analysis -- 4.7 Radar waveform design under similarity, bandwidth priority, and spectral coexistence constraints -- 4.7.1 Code design optimization problem -- 4.7.2 Performance analysis -- 4.8 Conclusions -- A.1 Proof of Theorem 4.1 -- A.2 Proof of Theorem 4.2 -- A.3 Proof of Theorem 4.3 -- A.4 Proof of Proposition 4.1: SDP relaxation tightness for (4.36) -- References -- 5. Robust transmit code and receive filter design for extended targets detection in clutter | Seyyed Mohammad Karbasi, Augusto Aubry, Antonio De Maio, Mohammad Hassan Bastani, and Alfonso Farina -- 5.1 Introduction -- 5.2 Target and signal model -- 5.2.1 Target model -- 5.2.2 Signal model -- 5.3 Problem formulation -- 5.3.1 Filter matrix optimization -- 5.3.2 Code matrix optimization -- 5.4 Filter and code synthesis -- 5.4.1 Filter synthesis -- 5.4.2 Code synthesis -- 5.5 Special case of practical importance: spherical uncertainty set</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">5.6 Numerical results -- 5.6.1 TAA uncertainty set size analysis -- 5.6.2 TAA uncertainty set for different target types -- 5.6.3 Spherical uncertainty set -- 5.7 Conclusions -- Appendix A: Proof of Lemma 5.1 -- Appendix B: Proof of Proposition 5.1 -- References -- 6. Optimizing radar transceiver for Doppler processing via non-convex programming | Augusto Aubry, Mohammad Mahdi Naghsh, Ehsan Raei, Mohammad Alaee-Kerahroodi, and Bhavani Shankar Mysore -- 6.1 Introduction -- 6.2 Radar system operation -- 6.2.1 Transmit waveform -- 6.2.2 Receiver processing and signal model -- 6.2.3 Clutter and signal independent disturbance characterization -- 6.2.4 Performance metric for Doppler processing -- 6.3 Problem formulation and design issues -- 6.3.1 Constraints and optimization problem -- 6.3.2 Filter bank optimization: solution to problemPw (n) -- 6.3.3 Radar code optimization: solution to problemPs (n) -- 6.3.4 Transmit-receive system design: optimization procedure -- 6.4 Performance analysis -- 6.4.1 Monotonicity of the proposed method and the impact of similarity constraint -- 6.4.2 Impact of colored interference -- 6.4.3 Effect of target Doppler shift interval -- 6.4.4 Impact of receive filter bank size -- 6.4.5 Impact of sequence length on performance -- 6.4.6 Performance comparison -- 6.5 Conclusions -- Appendix A: Proof of Proposition 6.1 -- Appendix B: Proof of Proposition 6.2 -- Appendix C: Proof of Lemma 6.1 -- References -- 7. Radar waveform design via the majorization-minimization framework | Linlong Wu and Daniel P. Palomar -- 7.1 Introduction -- 7.2 Preliminaries: the MM method -- 7.2.1 The vanilla MM method -- 7.2.2 Convergence analysis -- 7.2.3 Acceleration schemes -- 7.2.4 Extension to the maximin case -- 7.3 Joint design of transmit waveform and receive filter -- 7.3.1 System model and problem formulation</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">7.3.2 MM-based method for joint design with multiple constraints -- 7.3.3 Numerical experiments -- 7.4 Robust joint design for the worst-case SINR maximization -- 7.4.1 Problem formulation -- 7.4.2 MM-based method for robust joint design -- 7.4.3 Numerical experiments -- 7.5 Conclusion -- Appendix A: Proof of Lemma 7.1 -- Appendix B: Proof of Lemma 7.4 -- Appendix C: Proof of Lemma 7.5 -- Acknowledgment -- References -- 8. Lagrange programming neural network for radar waveform design | Junli Liang, Yang Jing, Hing Cheung So, Chi Sing Leung, Jian Li, and Alfonso Farina -- 8.1 Introduction -- 8.2 Basics of LPNN -- 8.2.1 Problem statement -- 8.2.2 Lagrange programming neural network -- 8.3 LPNN for waveform design with spectral constraints -- 8.3.1 Problem statement -- 8.3.2 Algorithm development -- 8.3.3 LPNN stability analysis -- 8.4 LPNN for designing waveform with low PSL -- 8.4.1 Problem statement -- 8.4.2 Algorithm description -- 8.4.3 LPNN stability analysis -- 8.4.4 Summary of proposed algorithm -- 8.5 Numerical examples -- 8.5.1 Experiment 1: Flat spectrum waveform design -- 8.5.2 Experiment 2: Spectrally constrained waveform design for radar -- 8.5.3 Experiment 3: Region of interest around main lobe -- 8.5.4 Experiment 4: Region of interest on one side of main lobe -- 8.5.5 Experiment 5: Low-sidelobe autocorrelation level -- 8.6 Conclusions -- A.1 Positive definiteness of Hessian matrix of (8.48) -- A.2 Solution to (8.58) -- A.3 Adaptive selection scheme of C0 -- A.3.1 On positive definiteness of ∇2 θθLx̄ -- A.3.2 On positive definiteness of Z0 -- A.3.3 On positive definiteness of Hessian matrix H of (8.49) -- References -- 9. Cognitive local ambiguity function shaping with spectral coexistence and experiments | Guolong Cui, Jing Yang, Xiangxiang Yu, and Lingjiang Kong -- 9.1 Introduction -- 9.2 Problem formulation</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">9.2.1 Weighted integrated sidelobe level -- 9.2.2 Spectral coexistence -- 9.2.3 Optimization problem -- 9.3 Iterative sequential quadratic optimization algorithm -- 9.4 Numerical results -- 9.4.1 Simulation results -- 9.4.2 Experimental results -- 9.5 Conclusions -- Appendix A: Proof of Proposition 9.1 -- Appendix B: Proof of (9.17) -- Appendix C: Proof of Proposition 9.2 -- Acknowledgments -- References -- 10. Relative entropy-based waveform design for MIMO radar | Bo Tang and Jun Tang -- 10.1 Introduction -- 10.2 Signal model and problem formulation -- 10.2.1 Signal model -- 10.2.2 Problem formulation -- 10.3 Two-stage algorithm design -- 10.3.1 Synthesis of energy-constrained waveforms -- 10.3.2 Convergence and computational complexity analysis -- 10.3.3 Extension to the synthesis of constant-moduluswaveforms -- 10.3.4 Extension to the synthesis of similarity-constrained waveforms -- 10.4 One-stage algorithm design -- 10.4.1 Minorizing Part A -- 10.4.2 Minorizing Part B -- 10.4.3 Minorizing Part C -- 10.4.4 The minorized problem at the (k + 1)th iteration -- 10.4.5 Convergence and computational complexity analysis -- 10.4.6 Extension to include other constraints -- 10.4.7 Accelerated schemes for the one-stage methods -- 10.5 Numerical examples -- 10.6 Concluding remarks -- Appendix A: Proof of (10.19) -- Appendix B: Proof of Lemma 10.1 -- Appendix C: Anintroduction to minorization-maximization -- Acknowledgment -- References -- Index</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cui, Guolong</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">De Maio, Antonio</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Farina, Alfonso</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jian</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-78561-943-4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield><subfield code="a">ZDB-100-IET</subfield><subfield code="a">ZDB-10-ARA</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032424939</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1049/SBRA533E</subfield><subfield code="l">DE-706</subfield><subfield code="p">ZDB-100-IET</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1049/SBRA533E</subfield><subfield code="l">DE-29</subfield><subfield code="p">ZDB-100-IET</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047017406 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:58:22Z |
| indexdate | 2024-07-31T01:11:37Z |
| institution | BVB |
| isbn | 9781785619441 9781523133499 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032424939 |
| oclc_num | 1224014274 |
| open_access_boolean | |
| owner | DE-29 DE-706 |
| owner_facet | DE-29 DE-706 |
| physical | 1 Online-Ressource (347 Seiten) |
| psigel | ZDB-30-PQE ZDB-100-IET ZDB-10-ARA |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Institution of Engineering & Technology, IET |
| record_format | marc |
| series2 | Radar, Sonar and Navigation Ser |
| spelling | Radar waveform design based on optimization theory edited by Guolong Cui, Antonio De Maio, Alfonso Farina and Jian Li Stevenage Institution of Engineering & Technology, IET [2020] 1 Online-Ressource (347 Seiten) txt rdacontent c rdamedia cr rdacarrier Radar, Sonar and Navigation Ser Intro -- Contents -- About the editors -- Foreword -- Notation -- 1. On recent advances of binary sequence designs and their applications | Ronghao Lin and Jian Li -- 1.1 Introduction -- 1.2 Algebraic methods -- 1.2.1 Barker sequences -- 1.2.2 Legendre sequences -- 1.2.3 m-Sequences -- 1.2.4 Gold sequences -- 1.2.5 Almost perfect autocorrelation sequences -- 1.2.6 Summary -- 1.3 Computation algorithms -- 1.3.1 Iterative twisted approximation -- 1.3.2 CD algorithm -- 1.3.3 CAN(PeCAN) family of algorithms -- 1.3.4 Summary -- 1.4 Conclusions -- References -- 2. Quadratic optimization for unimodular sequence synthesis and applications | Guolong Cui, Xianxiang Yu, Goffredo Foglia, Yongwei Huang, and Jian Li -- 2.1 Introduction -- 2.2 Problem formulation -- 2.3 Iterative algorithms for both the continuous and discrete phase cases -- 2.3.1 Iterative algorithm for continuous phase case -- 2.3.2 Iterative algorithm for discrete phase case -- 2.3.3 Power method-like approaches for both the continuous and discrete phase cases -- 2.4 Numerical examples -- 2.4.1 Code design to optimize radar detection performance -- 2.4.2 Spectrally compatible waveform design -- 2.5 Conclusions -- Acknowledgments -- References -- 3. A computational design of phase-only (possibly binary) sequences for radar systems | Mohammad Alaee-Kerahroodi, Augusto Aubry, Mohammad Mahdi Naghsh, Antonio De Maio, and Mahmoud Modarres-Hashemi -- 3.1 Introduction -- 3.1.1 Background and previous works -- 3.1.2 Contribution and organization -- 3.2 Problem formulation -- 3.3 CD code optimization -- 3.3.1 Continuous phase code design -- 3.3.2 Discrete phase code design -- 3.4 Numerical examples -- 3.4.1 Sequence design with good PSL -- 3.4.2 Sequence design with good ISL -- 3.4.3 Pareto-optimized solution and designing binary sequences -- 3.5 Conclusions -- Appendix A: Proof of Lemma 3.1 Appendix B: Derivation of the feasibility set -- Appendix C: Proof of Lemma 3.2 -- References -- 4. Constrained radar code design for spectrally congested environments via quadratic optimization | Marco Piezzo, Yongwei Huang, Augusto Aubry, and Antonio De Maio -- 4.1 Introduction -- 4.2 System model -- 4.3 Figures of merit and constraints -- 4.3.1 Detection probability -- 4.3.2 Energy and similarity constraints -- 4.3.3 Spectral compatibility constraint -- 4.3.4 Bandwidth priority constraint -- 4.4 QCQP's solution methods via rank-one matrix decomposition -- 4.5 Radarwaveformdesign in a spectrally crowded environment under similarity and spectral coexistence constraints -- 4.5.1 Code design optimization problem -- 4.5.2 Performance analysis -- 4.6 Radar waveform design in a spectrally crowded environment under similarity, energy modulation, and spectral coexistence constraints -- 4.6.1 Code design optimization problem -- 4.6.2 Performance analysis -- 4.7 Radar waveform design under similarity, bandwidth priority, and spectral coexistence constraints -- 4.7.1 Code design optimization problem -- 4.7.2 Performance analysis -- 4.8 Conclusions -- A.1 Proof of Theorem 4.1 -- A.2 Proof of Theorem 4.2 -- A.3 Proof of Theorem 4.3 -- A.4 Proof of Proposition 4.1: SDP relaxation tightness for (4.36) -- References -- 5. Robust transmit code and receive filter design for extended targets detection in clutter | Seyyed Mohammad Karbasi, Augusto Aubry, Antonio De Maio, Mohammad Hassan Bastani, and Alfonso Farina -- 5.1 Introduction -- 5.2 Target and signal model -- 5.2.1 Target model -- 5.2.2 Signal model -- 5.3 Problem formulation -- 5.3.1 Filter matrix optimization -- 5.3.2 Code matrix optimization -- 5.4 Filter and code synthesis -- 5.4.1 Filter synthesis -- 5.4.2 Code synthesis -- 5.5 Special case of practical importance: spherical uncertainty set 5.6 Numerical results -- 5.6.1 TAA uncertainty set size analysis -- 5.6.2 TAA uncertainty set for different target types -- 5.6.3 Spherical uncertainty set -- 5.7 Conclusions -- Appendix A: Proof of Lemma 5.1 -- Appendix B: Proof of Proposition 5.1 -- References -- 6. Optimizing radar transceiver for Doppler processing via non-convex programming | Augusto Aubry, Mohammad Mahdi Naghsh, Ehsan Raei, Mohammad Alaee-Kerahroodi, and Bhavani Shankar Mysore -- 6.1 Introduction -- 6.2 Radar system operation -- 6.2.1 Transmit waveform -- 6.2.2 Receiver processing and signal model -- 6.2.3 Clutter and signal independent disturbance characterization -- 6.2.4 Performance metric for Doppler processing -- 6.3 Problem formulation and design issues -- 6.3.1 Constraints and optimization problem -- 6.3.2 Filter bank optimization: solution to problemPw (n) -- 6.3.3 Radar code optimization: solution to problemPs (n) -- 6.3.4 Transmit-receive system design: optimization procedure -- 6.4 Performance analysis -- 6.4.1 Monotonicity of the proposed method and the impact of similarity constraint -- 6.4.2 Impact of colored interference -- 6.4.3 Effect of target Doppler shift interval -- 6.4.4 Impact of receive filter bank size -- 6.4.5 Impact of sequence length on performance -- 6.4.6 Performance comparison -- 6.5 Conclusions -- Appendix A: Proof of Proposition 6.1 -- Appendix B: Proof of Proposition 6.2 -- Appendix C: Proof of Lemma 6.1 -- References -- 7. Radar waveform design via the majorization-minimization framework | Linlong Wu and Daniel P. Palomar -- 7.1 Introduction -- 7.2 Preliminaries: the MM method -- 7.2.1 The vanilla MM method -- 7.2.2 Convergence analysis -- 7.2.3 Acceleration schemes -- 7.2.4 Extension to the maximin case -- 7.3 Joint design of transmit waveform and receive filter -- 7.3.1 System model and problem formulation 7.3.2 MM-based method for joint design with multiple constraints -- 7.3.3 Numerical experiments -- 7.4 Robust joint design for the worst-case SINR maximization -- 7.4.1 Problem formulation -- 7.4.2 MM-based method for robust joint design -- 7.4.3 Numerical experiments -- 7.5 Conclusion -- Appendix A: Proof of Lemma 7.1 -- Appendix B: Proof of Lemma 7.4 -- Appendix C: Proof of Lemma 7.5 -- Acknowledgment -- References -- 8. Lagrange programming neural network for radar waveform design | Junli Liang, Yang Jing, Hing Cheung So, Chi Sing Leung, Jian Li, and Alfonso Farina -- 8.1 Introduction -- 8.2 Basics of LPNN -- 8.2.1 Problem statement -- 8.2.2 Lagrange programming neural network -- 8.3 LPNN for waveform design with spectral constraints -- 8.3.1 Problem statement -- 8.3.2 Algorithm development -- 8.3.3 LPNN stability analysis -- 8.4 LPNN for designing waveform with low PSL -- 8.4.1 Problem statement -- 8.4.2 Algorithm description -- 8.4.3 LPNN stability analysis -- 8.4.4 Summary of proposed algorithm -- 8.5 Numerical examples -- 8.5.1 Experiment 1: Flat spectrum waveform design -- 8.5.2 Experiment 2: Spectrally constrained waveform design for radar -- 8.5.3 Experiment 3: Region of interest around main lobe -- 8.5.4 Experiment 4: Region of interest on one side of main lobe -- 8.5.5 Experiment 5: Low-sidelobe autocorrelation level -- 8.6 Conclusions -- A.1 Positive definiteness of Hessian matrix of (8.48) -- A.2 Solution to (8.58) -- A.3 Adaptive selection scheme of C0 -- A.3.1 On positive definiteness of ∇2 θθLx̄ -- A.3.2 On positive definiteness of Z0 -- A.3.3 On positive definiteness of Hessian matrix H of (8.49) -- References -- 9. Cognitive local ambiguity function shaping with spectral coexistence and experiments | Guolong Cui, Jing Yang, Xiangxiang Yu, and Lingjiang Kong -- 9.1 Introduction -- 9.2 Problem formulation 9.2.1 Weighted integrated sidelobe level -- 9.2.2 Spectral coexistence -- 9.2.3 Optimization problem -- 9.3 Iterative sequential quadratic optimization algorithm -- 9.4 Numerical results -- 9.4.1 Simulation results -- 9.4.2 Experimental results -- 9.5 Conclusions -- Appendix A: Proof of Proposition 9.1 -- Appendix B: Proof of (9.17) -- Appendix C: Proof of Proposition 9.2 -- Acknowledgments -- References -- 10. Relative entropy-based waveform design for MIMO radar | Bo Tang and Jun Tang -- 10.1 Introduction -- 10.2 Signal model and problem formulation -- 10.2.1 Signal model -- 10.2.2 Problem formulation -- 10.3 Two-stage algorithm design -- 10.3.1 Synthesis of energy-constrained waveforms -- 10.3.2 Convergence and computational complexity analysis -- 10.3.3 Extension to the synthesis of constant-moduluswaveforms -- 10.3.4 Extension to the synthesis of similarity-constrained waveforms -- 10.4 One-stage algorithm design -- 10.4.1 Minorizing Part A -- 10.4.2 Minorizing Part B -- 10.4.3 Minorizing Part C -- 10.4.4 The minorized problem at the (k + 1)th iteration -- 10.4.5 Convergence and computational complexity analysis -- 10.4.6 Extension to include other constraints -- 10.4.7 Accelerated schemes for the one-stage methods -- 10.5 Numerical examples -- 10.6 Concluding remarks -- Appendix A: Proof of (10.19) -- Appendix B: Proof of Lemma 10.1 -- Appendix C: Anintroduction to minorization-maximization -- Acknowledgment -- References -- Index Cui, Guolong edt De Maio, Antonio edt Farina, Alfonso edt Li, Jian edt Erscheint auch als Druck-Ausgabe 978-1-78561-943-4 |
| spellingShingle | Radar waveform design based on optimization theory |
| title | Radar waveform design based on optimization theory |
| title_auth | Radar waveform design based on optimization theory |
| title_exact_search | Radar waveform design based on optimization theory |
| title_exact_search_txtP | Radar waveform design based on optimization theory |
| title_full | Radar waveform design based on optimization theory edited by Guolong Cui, Antonio De Maio, Alfonso Farina and Jian Li |
| title_fullStr | Radar waveform design based on optimization theory edited by Guolong Cui, Antonio De Maio, Alfonso Farina and Jian Li |
| title_full_unstemmed | Radar waveform design based on optimization theory edited by Guolong Cui, Antonio De Maio, Alfonso Farina and Jian Li |
| title_short | Radar waveform design based on optimization theory |
| title_sort | radar waveform design based on optimization theory |
| work_keys_str_mv | AT cuiguolong radarwaveformdesignbasedonoptimizationtheory AT demaioantonio radarwaveformdesignbasedonoptimizationtheory AT farinaalfonso radarwaveformdesignbasedonoptimizationtheory AT lijian radarwaveformdesignbasedonoptimizationtheory |