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- Dave Zachariah, Martin Sundin, Magnus Jansson, Saikat Chatterjee
- IEEE Signal Processing Letters
- 2012

For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, as… (More)

- Nafiseh Shariati, Dave Zachariah, Mats Bengtsson
- 2014 IEEE International Conference on Acoustics…
- 2014

In this paper, we propose a robust transmit beampattern design for multiple-input multiple-output (MIMO) radar systems. The objective considered here is minimization of the beampattern sidelobes, subject to constraints on the transmit power where the waveform co-variance matrix is the optimization variable. Motivated by the fact that the steering vectors… (More)

- Petre Stoica, Dave Zachariah, Jian Li
- Digital Signal Processing
- 2014

- John-Olof Nilsson, Dave Zachariah, Isaac Skog, Peter Händel
- EURASIP J. Adv. Sig. Proc.
- 2013

—The implementation challenges of cooperative local-ization by dual foot-mounted inertial sensors and inter-agent ranging are discussed and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning… (More)

- Dave Zachariah, Saikat Chatterjee, Magnus Jansson
- IEEE Transactions on Signal Processing
- 2012

For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able to incorporate sequential predictions, thereby providing better compressive sensing recovery performance, but not at the… (More)

- Dave Zachariah, Isaac Skog, Magnus Jansson, Peter Händel
- IEEE Signal Processing Letters
- 2012

We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Since finding the conditional mean requires multidimensional… (More)

- Satyam Dwivedi, Dave Zachariah, Alessio De Angelis, Peter Händel
- IEEE Communications Letters
- 2013

- Dennis Sundman, Dave Zachariah, Saikat Chatterjee, Mikael Skoglund
- 2013 IEEE International Conference on Acoustics…
- 2013

In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an… (More)

- Isaac Skog, John-Olof Nilsson, Dave Zachariah, Peter Händel
- IPIN
- 2012

—A method is proposed to fuse the information from two navigation systems whose relative position is unknown, but where there exists an upper limit on how far apart the two systems can be. The proposed information fusion method is applied to a scenario in which a pedestrian is equipped with two foot-mounted zero-velocity-aided inertial navigation systems;… (More)

- Dave Zachariah, Petter Wirfält, Magnus Jansson, Saikat Chatterjee
- Signal Processing
- 2013

—For line spectrum estimation, we derive the maximum a posteriori probability estimator where prior knowledge of frequencies is modeled probabilistically. Since the spectrum is periodic, an appropriate distribution is the circular von Mises distribution that can parameterize the entire range of prior certainty of the frequencies. An efficient alternating… (More)