Browsing by Subject "Measurement matrix"

Now showing 1 - 3 of 3

Open Access
Adaptive measurement matrix design for compressed DoA estimation with sensor arrays
(IEEE, 2015) Özer, Berk; Lavrenko, A.; Gezici, Sinan; Römer, F.; Galdo, G. D.; Arıkan, Orhan
In this work we consider the problem of measurement matrix design for compressed 3-D Direction of Arrival (DoA) estimation using a sensor array with analog combiner. Since generic measurement matrix designs often do not yield optimal estimation performance, we propose a novel design technique based on the minimization of the Cramér-Rao Lower Bound (CRLB). We develop specific approaches for adaptive measurement design for two applications: detection of the newly appearing targets and tracking of the previously detected targets. Numerical results suggest that the developed designs allow to provide the near optimal performance in terms of the CRLB.
Open Access
Image feature extraction using compressive sensing
(Springer, 2014) Eleyan, A.; Köse, Kıvanç; Çetin, A. Enis
In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.
Open Access
Sparsity order estimation for single snapshot compressed sensing
(IEEE, 2015-11) Romer, F.; Lavrenko, A.; Del Galdo, G.; Hotz, T.; Arıkan, Orhan; Thoma, R. S.
In this paper we discuss the estimation of the spar-sity order for a Compressed Sensing scenario where only a single snapshot is available. We demonstrate that a specific design of the sensing matrix based on Khatri-Rao products enables us to transform this problem into the estimation of a matrix rank in the presence of additive noise. Thereby, we can apply existing model order selection algorithms to determine the sparsity order. The matrix is a rearranged version of the observation vector which can be constructed by concatenating a series of non-overlapping or overlapping blocks of the original observation vector. In both cases, a Khatri-Rao structured measurement matrix is required with the main difference that in the latter case, one of the factors must be a Vandermonde matrix. We discuss the choice of the parameters and show that an increasing amount of block overlap improves the sparsity order estimation but it increases the coherence of the sensing matrix. We also explain briefly that the proposed measurement matrix design introduces certain multilinear structures into the observations which enables us to apply tensor-based signal processing, e.g., for enhanced denoising or improved sparsity order estimation. © 2014 IEEE.