Radar Array Processing (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
317
Utgivningsdatum
2011-12-21
Upplaga
Softcover reprint of the original 1st ed. 1993
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Ottersten, B. (contributions)/Haykin, Simon S. (contributions)/Ho, T.V. (contributions)/Litva, J. (contributions)/McWhirter, J. G. (contributions)/Nehorai, A. (contributions)/Nickel, U. (contributions)/Ottersten, B. (contributions)/Haykin, Simon S. (contributions)/Ho, T.V. (contributions)/Litva, J.
Illustrationer
XII, 317 p.
Dimensioner
235 x 155 x 17 mm
Vikt
506 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783642773495
Radar Array Processing (häftad)

Radar Array Processing

Häftad Engelska, 2011-12-21
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Radar Array Processing presents modern techniques and methods for processingradar signals received by an array of antenna elements. With the recent rapid growth of the technology of hardware for digital signal processing, itis now possible to apply this to radar signals and thus to enlist the full power of sophisticated computational algorithms. Topics covered in detail here include: super-resolution methods of array signal processing as applied to radar, adaptive beam forming for radar, and radar imaging. This book will be of interest to researchers and studentsin the radar community and also in related fields such as sonar, seismology, acoustics and radio astronomy.
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Innehållsförteckning

1. Overview.- I Detection and Estimation.- 2. Radar Detection Using Array Processing.- 2.1 Observation Model.- 2.2 Coherent Radar Detection.- 2.2.1 Signal and Noise Model.- 2.2.2 Detection of Targets with Known Directions.- 2.2.3 Detection of Targets with Unknown Directions.- 2.3 Noncoherent Radar Detection.- 2.3.1 Signal and Noise Model.- 2.3.2 Detection of Targets with Known Directions.- 2.3.3 Detection of Targets with Unknown Directions: Deterministic Signal.- 2.3.4 Detection of Targets with Unknown Directions: Gaussian Signal.- 2.4 Passive Radar Detection.- 2.4.1 Signal and Noise Model.- 2.4.2 Detection of Emitters with Known Directions.- 2.4.3 Detection of Emitters with Unknown Directions: Deterministic Signal.- 2.4.4 Detection of Emitters with Unknown Directions: Gaussian Signal.- 2.5 Discussion.- References.- Additional References.- 3. Radar Target Parameter Estimation with Array Antennas.- 3.1 Radar Parameter Estimation Problem.- 3.1.1 Range and Angle Estimation.- 3.1.2 Frequency and Power Estimation.- 3.2 Angle Estimation.- 3.2.1 Monopulse Estimation (Single Target Estimation).- 3.2.2 Covariance Matrix Estimation.- 3.2.3 Linear Prediction Methods.- 3.2.4 Capon-Pisarenko-Type Methods.- 3.2.5 Signal Subspace Methods.- 3.2.6 Parametric Target Model Fitting.- 3.2.7 Aspects of Implementation.- 3.3 Frequency Estimation.- 3.3.1 Doppler Filter Bank.- 3.3.2 Superresolution Methods.- 3.4 Range, Amplitude and Power Estimation.- 3.4.1 Conventional Range Estimation.- 3.4.2 Superresolution in Range.- 3.4.3 Amplitude and Power Estimation.- 3.5 Summary.- References.- 4. Exact and Large Sample Maximum Likelihood Techniques for Parameter Estimation and Detection in Array Processing.- 4.1 Background.- 4.2 Chapter Outline.- 4.3 Sensor Array Processing.- 4.3.1 Narrowband Data Model.- 4.3.2 Parametric Data Model.- 4.3.3 Assumptions and Problem Formulation.- 4.3.4 Parameter Identifiability.- 4.4 Exact Maximum Likelihood Estimation.- 4.4.1 Stochastic Maximum Likelihood Method.- 4.4.2 Deterministic Maximum Likelihood Method.- 4.4.3 Bounds of Estimation Accuracy.- 4.4.4 Asymptotic Properties of Maximum Likelihood Estimates.- 4.4.5 Order Relations.- 4.5 Large Sample Maximum Likelihood Approximations.- 4.5.1 Subspace Based Approach.- 4.5.2 Relation Between Subspace Formulations.- 4.5.3 Relation to Maximum Likelihood Estimation.- 4.6 Calculating the Estimates.- 4.6.1 Newton-Type Search Algorithms.- 4.6.2 Gradients and Approximate Hessians.- 4.6.3 Uniform Linear Arrays.- 4.6.4 Practical Aspects.- 4.7 Detection of Coherent/Noncoherent Signals.- 4.7.1 Generalized Likelihood Ratio Test Based Detection.- 4.7.2 Subspace Based Detection.- 4.8 Numerical Examples and Simulations.- 4.9 Conclusions.- Appendix 4.A Differentiation of the Projection Matrix.- Appendix 4.B Asymptotic Distribution of the Weighted Subspace Fitting Criterion.- References.- II Systolic Arrays.- 5. Systolic Adaptive Beamforming.- 5.1 Adaptive Antenna Arrays.- 5.2 Systolic and Wavefront Arrays.- 5.3 Canonical Problem.- 5.3.1 Canonical Configuration.- 5.3.2 Least-Squares Formulation.- 5.4 QR Decomposition by Givens Rotations.- 5.4.1 QR Decomposition.- 5.4.2 Givens Rotations.- 5.4.3 Systolic Array Implementation.- 5.4.4 Square-Root-Free Algorithm.- 5.4.5 Sensitivity to Arithmetic Precision.- 5.5 Direct Residual Extraction.- 5.5.1 Definition of Residuals.- 5.5.2 Properties of Rotation Matrix Q?(n).- 5.5.3 A Posteriori Residual Extraction.- 5.5.4 A Priori Residual Extraction.- 5.6 Weight Freezing and Flushing.- 5.6.1 Basic Concept.- 5.6.2 Frozen Networks.- 5.6.3 Serial Weight Flushing.- 5.6.4 Further Insights.- 5.7 Linear Constraint Pre-Processor.- 5.7.1 Single Constraint Pre-Processor.- 5.7.2 Multiple Constraint Pre-Processor.- 5.7.3 Generalized Sidelobe Canceller.- 5.8 Minimum Variance Distortionless Response Beamforming.- 5.8.1 Schreiber's Algorithm.- 5.8.2 SystoUc Array Implementation.- 5.8.3 Square-Root-Free Minimum Variance Distortionless Response Algorithm.- 5.9 Adaptive Antenna Pr