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- Zhao Tan, Arye Nehorai, Merouane Debbah, Keke Hu, Sundeep Prabhakar, Christian Steffens
- 2014

- Zhao Tan, Peng Yang, Arye Nehorai
- IEEE Transactions on Signal Processing
- 2014

In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of… (More)

- Zhao Tan, Arye Nehorai
- IEEE Signal Processing Letters
- 2014

In this letter, we consider the problem of direction of arrival estimation using sparsity enforced reconstruction methods. Co-prime arrays with M + N sensors are utilized to increase the degrees of the freedom from O(M + N) to O(MN). The key to the success of sparse-based direction of arrival estimation is that every target must fall on the predefined grid.… (More)

- Zhao Tan, Yonina C. Eldar, Amir Beck, Arye Nehorai
- IEEE Transactions on Signal Processing
- 2014

We consider algorithms and recovery guarantees for the analysis sparse model in which the signal is sparse with respect to a highly coherent frame. We consider the use of a monotone version of the fast iterative shrinkage-thresholding algorithm (MFISTA) to solve the analysis sparse recovery problem. Since the proximal operator in MFISTA does not have a… (More)

- Zhao Tan, Yonina C. Eldar, Arye Nehorai
- IEEE Transactions on Signal Processing
- 2014

We consider the problem of direction of arrival (DOA) estimation using a recently proposed structure of nonuniform linear arrays, referred to as co-prime arrays. By exploiting the second order statistical information of the received signals, co-prime arrays exhibit O(MN) degrees of freedom with only M+N sensors. A sparsity-based recovery algorithm is… (More)

- Zhao Tan, Peng Yang, Arye Nehorai
- IEEE Trans. Smart Grid
- 2014

—In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or… (More)

- Zhao Tan, Peng Yang, Arye Nehorai
- ArXiv
- 2013

In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in problems such as radar signal processing… (More)

- Zhao Tan, Peng Yang, Arye Nehorai
- 2013 5th IEEE International Workshop on…
- 2013

In traditional compressed sensing theory, the dictionary matrix is given a priori, while in real applications this matrix suffers from random noise and fluctuations. This paper considers a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in radar related applications and… (More)

- Zhao Tan, Peng Yang, Arye Nehorai
- 2013 5th IEEE International Workshop on…
- 2013

In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in… (More)

- Zhao Tan, Arye Nehorai, Yonina C. Eldar
- 2014 IEEE 8th Sensor Array and Multichannel…
- 2014

We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of co-prime arrays. A continuous sparse recovery method is implemented in order to increase resolution. We show that in the noiseless case one can theoretically detect up to MN/2 sources with only 2M+N sensors via continuous sparse recovery. The noise… (More)