Dynamic Eigenimage Based Background and Clutter Suppression for Ultra Short-Range Radar

Matthias G. Ehrnsperger, Maximilian Noll, Stefan Punzet, Uwe Siart, Thomas F. Eibert

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background and clutter suppression techniques are important towards the successful application of radar in complex environments. We investigate eigenimage based methodologies such as principal component analysis (PCA) and apply it to frequency modulated continuous wave (FMCW) radar. The designed dynamic principal component analysis (dPCA) algorithm dynamically adjusts the number of eigenimages that are utilised for the processing of the signal. Furthermore, the algorithm adapts towards the number of objects in the field of view as well as the estimated distances. For the experimental evaluation, the dPCA algorithm is implemented in a multi-static FMCW radar prototype that operates in the K-band at 24GHz. With this background and clutter removal method, it is possible to increase the signal-to-clutter-ratio (SCR) by 4.9dB compared to standard PCA with mean removal (MR).

Original languageEnglish
Pages (from-to)71-77
Number of pages7
JournalAdvances in Radio Science
StatePublished - 17 Dec 2021


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