7,397 research outputs found

    Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation

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    Multi-mode antennas are an alternative to classical antenna arrays, and hence a promising emerging sensor technology for a vast variety of applications in the areas of array signal processing and digital communications. An unsolved problem is to describe the radiation pattern of multi-mode antennas in closed analytic form based on calibration measurements or on electromagnetic field (EMF) simulation data. As a solution, we investigate two modeling methods: One is based on the array interpolation technique (AIT), the other one on wavefield modeling (WM). Both methods are able to accurately interpolate quantized EMF data of a given multi-mode antenna, in our case a planar four-port antenna developed for the 6-8.5 GHz range. Since the modeling methods inherently depend on parameter sets, we investigate the influence of the parameter choice on the accuracy of both models. Furthermore, we evaluate the impact of modeling errors for coherent maximum-likelihood direction-of-arrival (DoA) estimation given different model parameters. Numerical results are presented for a single polarization component. Simulations reveal that the estimation bias introduced by model errors is subject to the chosen model parameters. Finally, we provide optimized sets of AIT and WM parameters for the multi-mode antenna under investigation. With these parameter sets, EMF data samples can be reproduced in interpolated form with high angular resolution

    Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

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    Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

    Parametric high resolution techniques for radio astronomical imaging

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    The increased sensitivity of future radio telescopes will result in requirements for higher dynamic range within the image as well as better resolution and immunity to interference. In this paper we propose a new matrix formulation of the imaging equation in the cases of non co-planar arrays and polarimetric measurements. Then we improve our parametric imaging techniques in terms of resolution and estimation accuracy. This is done by enhancing both the MVDR parametric imaging, introducing alternative dirty images and by introducing better power estimates based on least squares, with positive semi-definite constraints. We also discuss the use of robust Capon beamforming and semi-definite programming for solving the self-calibration problem. Additionally we provide statistical analysis of the bias of the MVDR beamformer for the case of moving array, which serves as a first step in analyzing iterative approaches such as CLEAN and the techniques proposed in this paper. Finally we demonstrate a full deconvolution process based on the parametric imaging techniques and show its improved resolution and sensitivity compared to the CLEAN method.Comment: To appear in IEEE Journal of Selected Topics in Signal Processing, Special issue on Signal Processing for Astronomy and space research. 30 page

    Calibration Challenges for Future Radio Telescopes

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    Instruments for radio astronomical observations have come a long way. While the first telescopes were based on very large dishes and 2-antenna interferometers, current instruments consist of dozens of steerable dishes, whereas future instruments will be even larger distributed sensor arrays with a hierarchy of phased array elements. For such arrays to provide meaningful output (images), accurate calibration is of critical importance. Calibration must solve for the unknown antenna gains and phases, as well as the unknown atmospheric and ionospheric disturbances. Future telescopes will have a large number of elements and a large field of view. In this case the parameters are strongly direction dependent, resulting in a large number of unknown parameters even if appropriately constrained physical or phenomenological descriptions are used. This makes calibration a daunting parameter estimation task, that is reviewed from a signal processing perspective in this article.Comment: 12 pages, 7 figures, 20 subfigures The title quoted in the meta-data is the title after release / final editing

    High-resolution ab initio three-dimensional X-ray diffraction microscopy

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    Coherent X-ray diffraction microscopy is a method of imaging non-periodic isolated objects at resolutions only limited, in principle, by the largest scattering angles recorded. We demonstrate X-ray diffraction imaging with high resolution in all three dimensions, as determined by a quantitative analysis of the reconstructed volume images. These images are retrieved from the 3D diffraction data using no a priori knowledge about the shape or composition of the object, which has never before been demonstrated on a non-periodic object. We also construct 2D images of thick objects with infinite depth of focus (without loss of transverse spatial resolution). These methods can be used to image biological and materials science samples at high resolution using X-ray undulator radiation, and establishes the techniques to be used in atomic-resolution ultrafast imaging at X-ray free-electron laser sources.Comment: 22 pages, 11 figures, submitte

    An Investigation of Energy Transmission Due to Flexural Wave Propagation in Lightweight, Built-Up Structures

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    A technique to measure flexural structure-borne noise intensity is investigated. Two accelerometers serve as transducers in this cross-spectral technique. The structure-borne sound power is obtained by two different techniques and compared. In the first method, a contour integral of intensity is performed from the values provided by the two-accelerometer intensity technique. In the second method, input power is calculated directly from the output of force and acceleration transducers. A plate and two beams were the subjects of the sound power comparisons. Excitation for the structures was either band-limited white noise or a deterministic signal similar to a swept sine. The two-accelerometer method was found to be sharply limited by near field and transducer spacing limitations. In addition, for the lightweight structures investigated, it was found that the probe inertia can have a significant influence on the power input to the structure. In addition to the experimental investigation of structure-borne sound energy, an extensive study of the point harmonically forced, point-damped beam boundary value problem was performed to gain insight into measurements of this nature. The intensity formulations were also incorporated into the finite element method. Intensity mappings were obtained analytically via finite element modeling of simple structures

    Computational Analysis of Functional Imaging in the Primary Auditory Cortex

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    Functional imaging can reveal detailed organizational structure in cerebral cortical areas, but neuronal response features and local neural interconnectivity can influence the resulting images, possibly limiting the inferences that can be drawn about neural function. Historically, discerning the fundamental principles of organizational structure in the auditory cortex of multiple species has been somewhat challenging with functional imaging as the studies have failed to reproduce results seen in electrophysiology. One difference might result from the way most functional imaging studies record the summed activity of multiple neurons. To test this effect, virtual mapping experiments were run in order to gauge the ability of functional imaging to accurately estimate underlying maps. The experiments suggest that spatial averaging improves the ability to estimate maps with low spatial frequencies or with large amounts of cortical variability, at the cost of decreasing the spatial resolution of the images. Despite the decrease in resolution, the results suggest that current functional imaging studies may be able to depict maps with high spatial frequencies better than electrophysiology can; therefore, the difficulties in recapitulating electrophysiology experiments with imaging may stem from underlying neural circuitry. One possible reason may be the relative distribution of response selectivity throughout the population of auditory cortex neurons. A small percent of neurons have a response type that exhibits a receptive field size that increases with higher stimulus intensities, but they are likely to contribute disproportionately to the activity detected in functional images, especially if intense sounds are used for stimulation. To evaluate the potential influence of neuronal subpopulations upon functional images of the primary auditory cortex, a model array representing cortical neurons was probed with virtual imaging experiments under various assumptions about the local circuit organization. As expected, different neuronal subpopulations were activated preferentially under different stimulus conditions. In fact, stimulus protocols that can preferentially excite one subpopulation of neurons over the others have the potential to improve the effective resolution of functional auditory cortical images. These experimental results also make predictions about auditory cortex organization that can be tested with refined functional imaging experiments
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