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

被引：1
|
作者：
Ehrnsperger, Matthias G. [1 ]
Noll, Maximilian [1 ]
Punzet, Stefan [1 ]
Siart, Uwe [1 ]
Eibert, Thomas F. [1 ]
机构：
[1] Tech Univ Munich, Dept Elect & Comp Engn, Arcisstr 21, D-80333 Munich, Germany
来源：
ADVANCES IN RADIO SCIENCE | 2021年 / 19卷
关键词：
SUBTRACTION; REMOVAL; NOISE;
D O I：
10.5194/ars-19-71-2021
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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).
引用
收藏
页码：71 / 77
页数：7
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