Browsing by Author "Landmann, Markus"

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    Design and analysis of compressive antenna arrays for direction of arrival estimation
    (Elsevier, 2017) Ibrahim, Mohamed; Ramireddy, Venkatesh; Lavrenko, Anastasia; König, Jonas; Römer, Florian; Landmann, Markus; Grossmann, Marcus; Del Galdo, Giovanni; S. Thomä, Reiner
    In this paper we investigate the design of compressive antenna arrays for direction of arrival (DOA) estimation that aim to provide a larger aperture with a reduced hardware complexity by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction, and propose two design approaches. The first approach is based on the spatial correlation function which is a low-complexity scheme that in certain cases admits a closed-form solution. The second approach is based on minimizing the Cramer-Rao Bound (CRB) with the ´ constraint to limit the probability of false detection of paths to a pre-specified level. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels.
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    Polarimetric compressive sensing based DOA estimation
    (VDE, 2014) Roemer, Florian; Ibrahim, Mohamed; Alieiev, Roman; Landmann, Markus; S. Thomae, Reiner; Del Galdo, Giovanni
    In this paper, we discuss direction of arrival (DOA) estimation based on the full polarimetric array manifold using a Compressive Sensing (CS)-based formulation. We first show that the existing non-polarimetric CS-based description of the DOA estimation problem can be extended to the polarimetric setting, giving rise to an amplitude vector that possesses a structured sparsity. We explain how DOAs can be estimated from this vector for incoming waves of arbitrary polarization. We then discuss the “gridding” problem, i.e., the effect of DOAs that are not on the sampling grid which was chosen for the discretization of the array manifold. We propose an estimator of these grid offsets which extends earlier work to the polarimetric setting. Numerical results demonstrate that the proposed scheme can achieve a DOA estimation accuracy close to the Cramér-Rao Bound for arbitrarily polarized waves.