A sparse signal reconstruction algorithm in wireless sensor networks

DOI10.1155/2016/9410873zbMath1400.94079OpenAlexW2463323188WikidataQ59141072 ScholiaQ59141072MaRDI QIDQ1793797

Jiuchao Feng, Zhi Zhao

Publication date: 12 October 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/9410873

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Filtering in stochastic control theory (93E11) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• Data-driven communication for state estimation with sensor networks
• Distributed Kalman-consensus filtering for sparse signal estimation
• One-Bit Compressive Sensing With Norm Estimation
• A Class of Stable Square-Root Nonlinear Information Filters
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms
• SPARLS: The Sparse RLS Algorithm
• Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support
• Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding
• The Kalman-Like Particle Filter: Optimal Estimation With Quantized Innovations/Measurements
• SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
• Cubature Kalman Filters
• Compressed sensing

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