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Knowledge-Aided Methods in Estimation Theory and Adaptive Filtering

Project description

Estimation theory is a key enabler in many of today‘s electronic products, devices, and industrial equipment. It provides the basis for efficient data estimation in communication systems, accurate characterization of systems based on measurements, estimation of parameters, signals and spectra, signal tracking, or noise cancellation, to name just a few.

Due to the ever-increasing complexity and the more demanding applications of modern electronic systems, optimal or near-to-optimal performance of estimation methods is often required. To achieve such an optimal performance, every available information about the underlying system model should be incorporated by the estimators.Ultimately, additional model knowledge is present in many applications. Possible examples of additional model knowledge as well as their optimal incorporation into the estimation process were investigated within the scope of the Ph.D. dissertation conducted at the ISP from 2014 to 2018 by Oliver Lang.

In his dissertation, novel classical estimation concepts as well as interesting advances in Bayesian estimation were analyzed in-depth. Finally, novel adaptive filters were developed, also allowing the incorporation of additional model knowledge. All concepts are derived analytically, and the properties of the estimators and filters are studied in great detail. Furthermore, a number of application examples are given, and numerical Monte Carlo simulations are used to test the algorithms.

Ph.D. Project Facts


Mar. 2014 - Feb. 2018


6) Lang O., Onic A., Steindl M., Huemer M., "Constrained Best Linear and Widely Linear Unbiased Estimation," in Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2018), IEEE, Seite(n) 1748-1752, 2018 

5) Lang O., Elvira V., Huemer M., "Estimation of Real Valued Impulse Responses based on Noisy Magnitude and Phase Measurements," in Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers, IEEE, Seite(n) 772-777, 2017

4) Huemer M., Lang O., Hofbauer C., "Component-Wise Conditionally Unbiased Widely Linear MMSE Estimation," in Signal Processing, Volume 133, Page(s) 227-239, 2017. Open Access, opens an external URL in a new window.

3) Lang O., Huemer M., Hofbauer C., "On the Log-Likelihood Ratio Evaluation of CWCU Linear and Widely Linear MMSE Data Estimators," in Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers, IEEE, 2016.

2) Lang O., Huemer M., "CWCU LMMSE Estimation Under Linear Model Assumptions", in Lecture Notes in Computer Science (LNCS): Computer Aided Systems Theory - EUROCAST 2015, Volume 9520, Page(s) 537-545, Springer International Publishing, 2015, PDF, opens a file

1) Huemer M., Lang O., "On Component-Wise Conditionally Unbiased Linear Bayesian Estimation," in Proceedings of the ASILOMAR Conference on Signals, Systems, and Computers, Page(s) 879-885, 2014.