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Файл:Diss / 28.pdf
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- •Abstract
- •Keywords
- •Contents
- •Introduction
- •Background on Array Processing
- •2.1 INTRODUCTION
- •2.1.1 Propagation Delays in Uniform Linear Arrays
- •2.1.2 Narrowband Approximation
- •2.1.3 Matrix Equation for Array Data
- •2.1.4 Eigenstructure of the Spatial Covariance Matrix
- •2.2 ANTENNA BEAMFORMING BASICS
- •2.2.1 The Conventional Beamformer
- •2.2.2 The Minimum Variance Distortionless Response Beamformer
- •3.1 CLASSICAL METHODS FOR DIRECTION OF ARRIVAL ESTIMATION
- •3.1.1 Delay-and-Sum Method
- •3.1.2 Capon’s Minimum Variance Distortionless Response Method
- •3.2 SUBSPACE METHODS FOR DOA ESTIMATION
- •3.2.1 Multiple Signal Classification Algorithm
- •3.2.2 Orthogonal Vector Methods
- •3.2.3 The Root MUSIC Algorithm
- •3.2.4 The Minimum Norm Method
- •3.2.5 Estimation of Signal Parameters via Rotational Invariance Techniques
- •3.2.6 Linear Prediction
- •3.2.7 The Unitary ESPRIT for Linear Arrays
- •3.2.8 QR ESPRIT
- •3.2.9 Beamspace DOA Estimation
- •3.2.10 The DFT Beamspace ESPRIT
- •3.2.11 The Multiple Invariance ESPRIT
- •3.2.12 Unitary ESPRIT for Planar Arrays
- •3.2.13 Maximum Likelihood Methods
- •3.2.13.1 The Alternating Projection Algorithm for ML DOA Estimation
- •4.1 ADAPTIVE SIMULATION EXAMPLE
- •Appendix
- •Signal Generator
- •The MUSIC Algorithm
- •The ESPRIT Algorithm
- •MVDR Method and the Classical Beamformer
- •Code to Simulate the MUSIC, the ESPRIT, the MVDR, the Min-Norm, and the Classical DOA Algorithms
- •References
- •Additional References
- •List of Symbols
- •List of Acronyms
- •Author Biography
Background on Array Processing 15
2.2.2 The Minimum Variance Distortionless Response Beamformer
The minimum variance distortionless response (MVDR) [2] beamformer is designed by minimizing the output power of the beamformer while constraining the gain to be one in the direction of interest. This problem can be stated as follows:
min E[ y*y] subject to wHa(θ) = W( θ) = 1.
h
The weights of the MVDR [2] are given by
wMVDR = |
|
Rxx− 1a(θ) |
, |
|
H |
− 1 |
a(θ) |
||
|
a |
(θ)Rxx |
|
|
(2.39)
(2.40)
where a(θ) is the steering vector corresponding to the desired signal and w is the vector of complex weights. This beamformer represents a significant improvement over the conventional beamformer because, for a given DOA, it minimizes the power from unwanted directions.
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