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## Sparse Sensing for Estimation with Correlated Observations

### Citations

1570 |
Fundamentals of Statistical Signal Processing, Estimation Theory
- Kay
- 1993
(Show Context)
Citation Context ...the problem, and (b) the subset of sensors that yields a lower CRB also generally yields a lower estimation error as well. The covariance of an unbiased estimate θ̂ satisfies the following inequality =-=[16]-=- E{(θ − θ̂)(θ − θ̂)T } ≥ C(w, θ) = F−1(w, θ), where C(w, θ) is the CRB matrix and the inverse of the CRB matrix, i.e., F (w, θ) = E {( ∂ ln p(y; θ) ∂θ )( ∂ ln p(y; θ) ∂θ )T} ∈ RN×N is the Fisher infor... |

1363 | Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
- Sturm
- 1999
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Citation Context ...cified maximum number of iterations. In each iteration, we solve a convex program (9), more specifically, a semidefinite program. This can be solved using any of the off-the-shelf solvers like SeDuMi =-=[17]-=- or YALMIP [18]. V. NUMERICAL EXAMPLE We apply the developed theory to sensor placement for the source localization setup illustrated in Fig. 2(a). In applications related to field estimation, (active... |

430 |
YALMIP: A toolbox for modeling and optimization
- Löfberg
- 2004
(Show Context)
Citation Context ...number of iterations. In each iteration, we solve a convex program (9), more specifically, a semidefinite program. This can be solved using any of the off-the-shelf solvers like SeDuMi [17] or YALMIP =-=[18]-=-. V. NUMERICAL EXAMPLE We apply the developed theory to sensor placement for the source localization setup illustrated in Fig. 2(a). In applications related to field estimation, (active/passive) radar... |

340 | Near-Optimal sensor placements in Gaussian Processes: Theory, efficient August 23, 2012 DRAFT algorithms and empirical
- KRAUSE, SINGH, et al.
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Citation Context ...bability or positioning accuracy. In recent years, sensor selection and management has received a significant amount of attention for various signal processing problems such as control and estimation =-=[1]-=-–[10] and detection [11]–[13]. In [1] and [2], the sensor selection problem for linear parameter estimation with uncorrelated Gaussian noise was solved via greedy submodular maximization and This work... |

95 | Sensor selection via convex optimization
- Joshi, Boyd
- 2009
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Citation Context ...ears, sensor selection and management has received a significant amount of attention for various signal processing problems such as control and estimation [1]–[10] and detection [11]–[13]. In [1] and =-=[2]-=-, the sensor selection problem for linear parameter estimation with uncorrelated Gaussian noise was solved via greedy submodular maximization and This work was supported in part by STW under the FASTC... |

14 |
Sensor selection for event detection in wireless sensor networks
- Bajovic, Sinopoli, et al.
- 2011
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Citation Context ... accuracy. In recent years, sensor selection and management has received a significant amount of attention for various signal processing problems such as control and estimation [1]–[10] and detection =-=[11]-=-–[13]. In [1] and [2], the sensor selection problem for linear parameter estimation with uncorrelated Gaussian noise was solved via greedy submodular maximization and This work was supported in part b... |

12 | Sparsity-promoting sensor selection for non-linear measurement models
- Chepuri, Leus
- 2013
(Show Context)
Citation Context ...reedy submodular maximization and This work was supported in part by STW under the FASTCOM project (10551) and in part by NWO-STW under the VICI program (10382). convex optimization, respectively. In =-=[3]-=-, this problem was generalized to nonlinear measurement models with arbitrary yet conditionally independent data distributions, where convex optimization methods were employed to determine the best su... |

12 | Algorithms for leader selection in stochastically forced consensus networks - Lin, Fardad, et al. - 2014 |

8 | Sparsity-promoting extended Kalman filtering for target tracking in wireless sensor networks - Masazade, Fardad, et al. - 2012 |

4 | Sparsity-promoting adaptive sensor selection for non-linear filtering - Chepuri, Leus - 2014 |

3 | Sparsity-enforcing sensor selection for DOA estimation
- Roy, Chepuri, et al.
- 2013
(Show Context)
Citation Context ...lity or positioning accuracy. In recent years, sensor selection and management has received a significant amount of attention for various signal processing problems such as control and estimation [1]–=-=[10]-=- and detection [11]–[13]. In [1] and [2], the sensor selection problem for linear parameter estimation with uncorrelated Gaussian noise was solved via greedy submodular maximization and This work was ... |

2 | Optimal periodic sensor scheduling in networks of dynamical systems - Liu, Fardad, et al. - 2014 |

2 | Sparse sensing for distributed Gaussian detection - Chepuri, Leus - 2015 |

2 | Sparsity-aware sensor selection for correlated noise
- Rad, Simonetto, et al.
- 2014
(Show Context)
Citation Context ... the Fisher information matrix (FIM) under sparse sensing is no more preserved. This makes the sensor selection problem with correlated observations even more challenging. Existing works, e.g., [14], =-=[15]-=- focus on linear models in colored Gaussian noise, but with an approximate performance metric. That is, they rely on an approximate expression for the mean squared error, where the noise from the sens... |

1 | Continuous sensor placement - Chepuri, Leus - 2015 |

1 | Correlation-aware sparsity-enforcing sensor placement for spatio-temporal field estimation - Roy, Leus - 2015 |

1 | Sparse sensing for distributed detection
- Chepuri, Leus
- 2015
(Show Context)
Citation Context ...racy. In recent years, sensor selection and management has received a significant amount of attention for various signal processing problems such as control and estimation [1]–[10] and detection [11]–=-=[13]-=-. In [1] and [2], the sensor selection problem for linear parameter estimation with uncorrelated Gaussian noise was solved via greedy submodular maximization and This work was supported in part by STW... |

1 |
Sensor selection with correlated noise
- Rigtorp
- 2010
(Show Context)
Citation Context ...3]) of the Fisher information matrix (FIM) under sparse sensing is no more preserved. This makes the sensor selection problem with correlated observations even more challenging. Existing works, e.g., =-=[14]-=-, [15] focus on linear models in colored Gaussian noise, but with an approximate performance metric. That is, they rely on an approximate expression for the mean squared error, where the noise from th... |