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Related publications (34)

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Estimation of Sparse MIMO Channels with Common Support

Martin Vetterli, Ali Hormati, Yann Barbotin

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are indiv ...

2012

ESTIMATING SPARSE MIMO CHANNELS HAVING COMMON SUPPORT

Martin Vetterli, Ali Hormati, Yann Barbotin

According to a first aspect of the present invention there is provided a method of estimating, jointly, a set of multipath channels having a common path support, the method comprising the steps of, estimating jointly the common path support of the set of m ...

USPTO2012

Spatio-Temporal Diffusion Strategies for Estimation and Detection Over Networks

Ali H. Sayed

We present diffusion algorithms for distributed estimation and detection over networks that endow all nodes with both spatial cooperation abilities and temporal processing abilities. Each node in the network is allowed to share information locally with its ...

IEEE2012

Diffusion LMS for source and process estimation in sensor networks

Ali H. Sayed

We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that ...

IEEE2012

Bits from Photons

Feng Yang

The trends in the design of image sensors are to build sensors with low noise, high sensitivity, high dynamic range, and small pixel size. How can we benefit from pixels with small size and high sensitivity? In this dissertation, we study a new image senso ...

EPFL2012

On the role of diversity in sparsity estimation

Michael Christoph Gastpar

A major challenge in sparsity pattern estimation is that small modes are difficult to detect in the presence of noise. This problem is alleviated if one can observe samples from multiple realizations of the nonzero values for the same sparsity pattern. We ...

IEEE2011

State estimation for large-scale partitioned systems: a moving horizon approach

Giancarlo Ferrari Trecate, Marcello Farina

This paper presents three novel Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting ...

2010

Distributed Moving Horizon Estimation for Linear Constrained Systems

Giancarlo Ferrari Trecate, Marcello Farina

This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensors to the correct state even when constrain ...

2010

Moving horizon partition-based state estimation of large-scale systems

Giancarlo Ferrari Trecate, Marcello Farina

2010

Estimation of phase derivative using adaptive window spectrogram

Pramod Rastogi, Sai Siva Gorthi

The paper introduces an adaptive-window-spectrogram-based method to directly estimate the phase derivative from a single fringe pattern. The proposed method relies on estimating the phase derivative using spectrogram peak detection for a set of different w ...

2010