Robust Adaptive Beamforming (Hardcover)

Jian Li, Petre Stoica

Robust Adaptive Beamforming (Hardcover)

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The latest research and developments in robust adaptive beamforming

Recent 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 s1. Robust Minimum Variance Beamforming (R. Lorenz & S. Boyd).

2. Robust Adaptive beamforming Based on Worst-Case Performance Optimization (A. Gershman, et al.).

3. Robust Capon Beamforming (J. Li, et al.).

4. Diagonal Loading for Finite Sample Size Beamforming: An Asymptotic Approach (X. Mestre & M. Lagunas).

5. Mean-Squared Error Beamforming for Signal Estimation: A Competitive Approach (Y. Eldar & A. Nehorai).

6. Constant Modulus Beamforming (A. van der Veen & A. Leshem).

7. Robust Wideband Beamforming (E. Di Claudio & R. Parisi).

Index.

uch as smart antennas for wireless communications, handheld ultrasound imaging systems, and directional hearing aids.

Robust Adaptive Beamforming compiles the theories and work of leading researchers investigating various approaches in one comprehensive volume. Unlike previous efforts, these pioneering studies are based on theories that use an uncertainty set of the array steering vector. The researchers define their theories, explain their methodologies, and present their conclusions. Methods presented include:

  • Coupling the standard Capon beamformers with a spherical or ellipsoidal uncertainty set of the array steering vector
  • Diagonal loading for finite sample size beamforming
  • Mean-squared error beamforming for signal estimation
  • Constant modulus beamforming
  • Robust wideband beamforming using a steered adaptive beamformer to adapt the weight vector within a generalized sidelobe canceller formulation

Robust Adaptive Beamforming provides a truly up-to-date resource and reference for engineers, researchers, and graduate students in this promising, rapidly expanding field.

 

 

Table of Contents:

1. Robust Minimum Variance Beamforming (R. Lorenz & S. Boyd).

2. Robust Adaptive beamforming Based on Worst-Case Performance Optimization (A. Gershman, et al.).

3. Robust Capon Beamforming (J. Li, et al.).

4. Diagonal Loading for Finite Sample Size Beamforming: An Asymptotic Approach (X. Mestre & M. Lagunas).

5. Mean-Squared Error Beamforming for Signal Estimation: A Competitive Approach (Y. Eldar & A. Nehorai).

6. Constant Modulus Beamforming (A. van der Veen & A. Leshem).

7. Robust Wideband Beamforming (E. Di Claudio & R. Parisi).

Index.

商品描述(中文翻譯)

描述：
最新的強健自適應波束成形研究和發展
最近的工作在設計強健自適應波束成形器方面取得了巨大進展，大大提高了對背景噪音和方向干擾的信號強度。這項動態技術具有多種應用，包括雷達、聲納、聲學、天文學、地震學、通信和醫學成像。還有一些令人興奮的新興應用，例如強健最小變異波束成形（R. Lorenz和S. Boyd）。
2. 基於最壞情況性能優化的強健自適應波束成形（A. Gershman等）。
3. 強健Capon波束成形（J. Li等）。
4. 有限樣本大小波束成形的對角加載：一種漸近方法（X. Mestre和M. Lagunas）。
5. 用於信號估計的均方誤差波束成形：一種競爭方法（Y. Eldar和A. Nehorai）。
6. 常數模數波束成形（A. van der Veen和A. Leshem）。
7. 強健寬頻波束成形（E. Di Claudio和R. Parisi）。
索引。
例如用於無線通信的智能天線、手持式超聲波成像系統和定向聽力助聽器。
《強健自適應波束成形》將領先研究人員在各種方法上的理論和工作編纂成一本綜合性的專著。與以往的努力不同，這些開創性的研究基於使用陣列指向向量的不確定性集合的理論。研究人員定義了他們的理論，解釋了他們的方法論，並提出了他們的結論。介紹的方法包括：
將標準Capon波束成形器與球形或橢圓形陣列指向向量的不確定性集合相結合
有限樣本大小波束成形的對角加載
用於信號估計的均方誤差波束成形
常數模數波束成形
使用定向自適應波束成形器在廣義旁瓣抵消器公式中調整權重向量的強健寬頻波束成形
《強健自適應波束成形》為這個有前景且快速發展的領域的工程師、研究人員和研究生提供了一個真正最新的資源和參考。

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