@INCOLLECTION{roese-koerner-etal_12,
  author = {Roese-Koerner, L. and Krasbutter, I. and Schuh, W.-D.},
  title = {A constrained quadratic programming technique for data-adaptive design
	of decorrelation filters},
  booktitle = {{VII. Hotine-Marussi-Symposium}, {IAG} Symposia},
  publisher = {Springer},
  year = {2012},
  editor = {Sneeuw, Nico and Nov\'{a}k, Pavel and Crespi, Mattia and Sans\`{o},
	Fernando},
  volume = {137},
  series = {Lecture Notes in Earth Sciences},
  pages = {165--170},
  address = {Berlin - Heidelberg},
  abstract = {Signals from sensors with high sampling rates are often highly correlated.
	For the decorrelation of such data, which is often applied for the
	efficient estimation of parametric data models, discrete filters
	have proven to be both highly flexible and numerically efficient.
	Standard filter techniques are, however, often not suitable for eliminating
	strong local fluctuations or trends present in the noise spectral
	density. Therefore we propose a constrained least-squares filter
	design method. The spectral features to be filtered out are specified
	through inequality constraints regarding the noise spectral density.
	To solve for the optimal filter parameters under such inequality
	constraints, we review and apply the Active Set Method, a quadratic
	programming technique. Results are validated by statistical tests.
	The proposed filter design algorithm is applied to GOCE gradiometer
	signals to analyze its numerical behaviour and efficiency for a realistic
	and complex application.},
  doi = {10.1007/978-3-642-22078-4\_25},
  keywords = {Active Set Method – Adjustment with inequality constraints – Decorrelation
	– Filter}
}