Adaptive Signal Processing: Next Generation Solutions
Adaptive Signal Processing: Next Generation Solutions
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DOI：
10.1002/9780470575758
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发表时间：
2010-03
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前言。贡献者。第1章复值自适应信号处理。1.1绪论。1.2绪论。1.3复域优化。1.4广泛线性自适应滤波。1.5多层感知器非线性自适应滤波。1.6复独立分量分析。1.7总结。1.8确认。1.9问题。参考文献。第二章复值随机向量的鲁棒估计技术。2.1简介。2.2复随机向量的统计特征。2.3复椭圆对称（CES）分布。2.4比较估计器的工具。2.5散点和伪散点矩阵。2.6阵列处理示例。2.7基于M -估计器的MVDR波束形成器。2.8稳健的ICA。2.9结论2.10问题参考文献。第3章Turbo均衡。3.1简介。3.2上下文。3.3通信链。3.4 Turbo译码：概述。3.5前后向算法。3.6简化算法：干扰消除。3.7容量分析。3.8 Turbo盲均衡。3.9收敛。3.10多通道和多用户设置。3.11结束语。3.12问题。参考文献。第4章信号处理的子空间跟踪。4.1简介。4.2线性代数回顾。4.3观察模型和问题陈述。4.4初步示例：Oja神经元。4.5子空间跟踪。4.6特征向量跟踪。4.7收敛和性能分析问题。4.8示例说明。4.9结语。4.10问题。参考文献。第5章粒子滤波。5.1简介。5.2使用粒子滤波的动机。5.3基本思想。5.4建议分布和重采样的选择。5.5一些粒子滤波方法。5.6处理常数参数。5.7 Rao blackwell化。5.8预测。5.9平滑。5.10收敛问题。5.11计算问题和硬件实现。5.12确认。5.13练习。参考文献。第6章解决模式分类问题的非线性顺序状态估计。6.1简介。6.2反向传播和支持向量机器学习算法：回顾。6.3使用非线性顺序状态估计的mlp监督训练框架。6.4扩展卡尔曼滤波。6.5扩展卡尔曼滤波算法与反向传播和支持向量机器学习算法的实验比较。6.6结语。6.7问题。参考文献。第7章电话语音的带宽扩展。7.1引言。7.2本章的组织。7.3非基于模型的带宽扩展算法。7.4基础知识。7.5基于模型的带宽扩展算法。7.6带宽扩展算法的评价。7.7结论。7.8问题。参考文献。索引。
Preface. Contributors. Chapter 1 Complex-Valued Adaptive Signal Processing. 1.1 Introduction. 1.2 Preliminaries. 1.3 Optimization in the Complex Domain. 1.4 Widely Linear Adaptive Filtering. 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons. 1.6 Complex Independent Component Analysis. 1.7 Summary. 1.8 Acknowledgment. 1.9 Problems. References. Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors. 2.1 Introduction. 2.2 Statistical Characterization of Complex Random Vectors. 2.3 Complex Elliptically Symmetric (CES) Distributions. 2.4 Tools to Compare Estimators. 2.5 Scatter and Pseudo-Scatter Matrices. 2.6 Array Processing Examples. 2.7 MVDR Beamformers Based on M -Estimators. 2.8 Robust ICA. 2.9 Conclusion. 2.10 Problems. References. Chapter 3 Turbo Equalization. 3.1 Introduction. 3.2 Context. 3.3 Communication Chain. 3.4 Turbo Decoder: Overview. 3.5 Forward-Backward Algorithm. 3.6 Simplified Algorithm: Interference Canceler. 3.7 Capacity Analysis. 3.8 Blind Turbo Equalization. 3.9 Convergence. 3.10 Multichannel and Multiuser Settings. 3.11 Concluding Remarks. 3.12 Problems. References. Chapter 4 Subspace Tracking for Signal Processing. 4.1 Introduction. 4.2 Linear Algebra Review. 4.3 Observation Model and Problem Statement. 4.4 Preliminary Example: Oja s Neuron. 4.5 Subspace Tracking. 4.6 Eigenvectors Tracking. 4.7 Convergence and Performance Analysis Issues. 4.8 Illustrative Examples. 4.9 Concluding Remarks. 4.10 Problems. References. Chapter 5 Particle Filtering. 5.1 Introduction. 5.2 Motivation for Use of Particle Filtering. 5.3 The Basic Idea. 5.4 The Choice of Proposal Distribution and Resampling. 5.5 Some Particle Filtering Methods. 5.6 Handling Constant Parameters. 5.7 Rao Blackwellization. 5.8 Prediction. 5.9 Smoothing. 5.10 Convergence Issues. 5.11 Computational Issues and Hardware Implementation. 5.12 Acknowledgments. 5.13 Exercises. References. Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems. 6.1 Introduction. 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review. 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation. 6.4 The Extended Kalman Filter. 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms. 6.6 Concluding Remarks. 6.7 Problems. References. Chapter 7 Bandwidth Extension of Telephony Speech. 7.1 Introduction. 7.2 Organization of the Chapter. 7.3 Nonmodel-Based Algorithms for Bandwidth Extension. 7.4 Basics. 7.5 Model-Based Algorithms for Bandwidth Extension. 7.6 Evaluation of Bandwidth Extension Algorithms. 7.7 Conclusion. 7.8 Problems. References. Index.