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Impact of incomplete and inaccurate data models on high resolution parameter estimation in multidimensional channel sounding

 
: Landmann, M.; Käske, M.; Thomä, R.S.

:

IEEE Transactions on Antennas and Propagation 60 (2012), Nr.2 Part 1, S.557-573
ISSN: 0018-926X
ISSN: 0096-1973
Englisch
Zeitschriftenaufsatz
Fraunhofer IIS ()

Abstract
Multidimensional channel sounding aims to estimate the geometrical structure of multi-path wave propagation in terms of directions of arrival/departure, Doppler shift, time delay, and complex polarimetric path weights. Maximum likelihood parameter estimation based upon an underlying data model is used to achieve high-resolution of the path parameters and, thus, renders possible an antenna independent channel characterization. However, any mismatch of the underlying data model to physical reality imposes limits to accuracy and reliability of the estimation. To cope with the limited resolution capability of the setup we are using a propagation data model that does not only contain discrete deterministic components but also a non-resolvable stochastic part. Joint estimation of both components considerably enhances the estimation quality and finally allows the interpretation as specular and diffuse contribution of multi-path propagation respectively. However, besides of noi se influence, the achievable resolution is further limited by the accuracy of the data model that describes the measurement setup. Since the antenna characteristics are very susceptible to calibration and modeling errors, the directional estimates are most error-prone. We refer to the antenna array calibration procedure and discuss common pitfalls in highresolution multi-path direction estimation that are related to inaccurate and/or incomplete device data model. Depending on the type of the antenna array (linear, circular) this will inherently produce biased and artificially spread angular estimates. Only with precise knowledge of the model errors the stochastic part can be identified as diffuse propagation component vs. modeling error.

: http://publica.fraunhofer.de/dokumente/N-206928.html