Sensor Selection via Convex Optimization

By Siddharth Joshi, Stephen P. Boyd. IEEE Transactions on Signal Processing, 2009

1,276 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/d742cbcce8f083804c97202529f139c017b634a6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Steven L. Brunton
2. J. Nathan Kutz
3. Bingni W. Brunton
4. Krithika Manohar
5. Stephen P. Boyd
6. Danny Bickson
7. Antonio Ortega
8. A. Ihler

In Computer Networks and Communications

1. Steven L. Brunton
2. J. Nathan Kutz
3. Seung-Jean Kim
4. Stephen P. Boyd
5. Antonio Ortega
6. Akshay Gadde
7. Aamir Anis
8. Siddhartha Banerjee

In Management Science and Operations Research

1. Antonio Ortega
2. Haris Vikalo
3. Akshay Gadde
4. Manohar Shamaiah
5. Siddhartha Banerjee
6. Aamir Anis
7. J. Nathan Kutz
8. Steven L. Brunton

Citations in the corpus

• Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies (2015, methodology): “Previous methods for sampling set selection in graphs can be classified into two types, namely spectral-domain methods and vertex-domain methods, which are summarized below.”
• Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, methodology): “QR pivoting sensors perform comparably to sensors obtained via the convex optimization (Convex opt) method [2], and both are close to the optimal proper orthogonal decomposition approximation using the full state.”
• 1 Trend Filtering (2009, background): “In signal processing, the idea of 1 regularization comes up in several contexts, including basis pursuit (denoising) [19, 20], image decomposition [31, 88], signal recovery from incomplete measurements [17, 16, 26, 27, 96], sensor selection [55], fault identification [108], and wavelet thresholding ”
• Fault Identification Via Nonparametric Belief Propagation (2009, methodology): “Despite this, the sum-product algorithm has been frequently applied to MAP estimation problems, as it often exhibits better convergence behavior than max-product [39].”
• Greedy sensor selection: Leveraging submodularity (2010, methodology): “Following [1], we assume that the fusion center knows the measurement vectors ai.”
• A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications (2020, neutral): “estimator for parameter estimation [74].”