Compressed Sensing & Sparse Filtering
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2014.
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| Series: | Signals and Communication Technology,
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| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Introduction to Compressed Sensing and Sparse Filtering
- The Geometry of Compressed Sensing
- Sparse Signal Recovery with Exponential-Family Noise
- Nuclear Norm Optimization and its Application to Observation Model Specification
- Nonnegative Tensor Decomposition
- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks
- Sparse Nonlinear MIMO Filtering and Identification
- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation
- Compressive System Identification
- Distributed Approximation and Tracking using Selective Gossip
- Recursive Reconstruction of Sparse Signal Sequences
- Estimation of Time-Varying Sparse Signals in Sensor Networks
- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging
- Structured Sparse Bayesian Modelling for Audio Restoration
- Sparse Representations for Speech Recognition.
