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Adaptive time-varying clutter suppression algorithm based on TAVFF using IR-UWB radar
Author(s): Haifan Liu; Zhaocheng Yang; Runhan Bao; Mengxia Chen
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Paper Abstract

Clutter suppression, especially in time-varying environments is a hindrance that must be solved for radar systems applied to unmanned vehicles. However, exponential moving average (EMA) method, a common background subtraction technique, does not handle such a situation very well because the fixed parameter constrains the updating of the estimated clutter. In this paper, we propose a novel adaptive clutter suppression algorithm to adjust the parameter of EMA method under the background of time-varying clutter. The main idea is to adopt a low-complexity time-averaged variable forgetting factor (TAVFF) mechanism. The proposed algorithm is assessed with data recording measured background clutter and a simulated moving target. The simulation results demonstrate our proposed algorithm has achieved both fast convergence and good steady-state performance.

Paper Details

Date Published: 31 December 2019
PDF: 9 pages
Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138402 (31 December 2019); doi: 10.1117/12.2558415
Show Author Affiliations
Haifan Liu, Shenzhen Univ. (China)
Zhaocheng Yang, Shenzhen Univ. (China)
Runhan Bao, Shenzhen Univ. (China)
Mengxia Chen, Shenzhen Univ. (China)


Published in SPIE Proceedings Vol. 11384:
Eleventh International Conference on Signal Processing Systems
Kezhi Mao, Editor(s)

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