Abstract
Abstract. 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 24 GHz. With this background and clutter removal method, it is possible to increase the signal-to-clutter-ratio (SCR) by 4.9 dB compared to standard PCA with mean removal (MR).
Highlights
Radar systems have become very small in size due to vast technological developments
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 algorithm dynamically adjusts the number of eigenimages that are utilised for the processing of the signal
Summary
Radar systems have become very small in size due to vast technological developments. They are found in various user electronics applications such as cellphones and smartwatches, as well as in the automotive sector where they are utilised, e.g., for advanced driver assistance systems. Taking the available literature into consideration, our investigations focus on clutter and background removal by employing eigenvalue decomposition (EVD) in combination with principal component analysis (PCA) as basis. We investigate how the reconstruction of a measured signal can be completed dynamically to remove background and clutter, and maintaining a sufficient detection probability for lowvelocity objects. This dynamic principal component analysis (dPCA) reconstruction approach is firstly implemented and evaluated in a simulative environment. Is dPCA able, in combination with a suitable mean removal method, to detect low-velocity and temporarily static targets in bright clutter environments?
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