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Advances in Radio Science An open-access journal of the U.R.S.I. Landesausschuss in der Bundesrepublik Deutschland e.V.
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Volume 10
Adv. Radio Sci., 10, 333-339, 2012
https://doi.org/10.5194/ars-10-333-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Radio Sci., 10, 333-339, 2012
https://doi.org/10.5194/ars-10-333-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Nov 2012

29 Nov 2012

FMCW sparse array imaging and restoration for microwave gauging

S. Kolb and R. Stolle S. Kolb and R. Stolle
  • University of Applied Sciences Augsburg, An der Hochschule 1, 86161 Augsburg, Germany

Abstract. The application of imaging radar to microwave level gauging represents a prospect of increasing the reliability of target detection. The aperture size of the used sensor determines the underlying azimuthal resolution. In consequence, when FMCW-based multistatic radar (FMCW: frequency modulated continuous wave) is used, the number of antennas dictates this essential property of an imaging system. The application of a sparse array leads to an improvement of the azimuthal resolution by keeping the number of array elements constant with the cost of increased side lobe level. Therefore, ambiguities occur within the imaging process. This problem can be modelled by a point spread function (PSF) which is common in image processing. Hence, an inverse system to the imaging system is needed to restore unique information of existing targets within the observed radar scenario.

In general, the process of imaging is of ill-conditioned nature and therefore appropriate algorithms have to be applied. The present paper first develops the degradation model, namely PSF, of an imaging system based on a uniform linear array in time domain. As a result, range and azimuth dimensions are interdependent and the process of imaging has to be reformulated in one dimension. Matrix-based approaches can be adopted in this way. The second part applies two computational methods to the given inverse problem, namely quadratic and non-quadratic regularization. Notably, the second one exhibits an ability to suppress ambiguities. This can be demonstrated with the results of both, simulations and measurements, and enables sparse array imaging to localize point targets more unambiguously.

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