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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.


A lightweight clutter suppression algorithm for passive bistatic radar

Author(s):  Yong WU, Luo ZUO, Dongliang PENG, Zhikun CHEN

Affiliation(s):  Zhejiang Institute of Communications, Hangzhou 311112, China; more

Corresponding email(s):   wuyong@zjvtit.edu.cn, zuoluo@xidian.edu.cn, dlpeng@hdu.edu.cn, czk@hdu.edu.cn

Key Words:  Passive bistatic radar, Clutter suppression, Extensive cancellation algorithm, Computational complexity, Space complexity

Yong WU, Luo ZUO, Dongliang PENG, Zhikun CHEN. A lightweight clutter suppression algorithm for passive bistatic radar[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yong WU, Luo ZUO, Dongliang PENG, Zhikun CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
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%T A lightweight clutter suppression algorithm for passive bistatic radar
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%A Zhikun CHEN
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300859

T1 - A lightweight clutter suppression algorithm for passive bistatic radar
A1 - Yong WU
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A1 - Dongliang PENG
A1 - Zhikun CHEN
J0 - Journal of Zhejiang University Science C
VL - -1
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Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300859

In passive bistatic radar, the computational efficiency of clutter suppression algorithms remains low, due to continuous increases in bandwidth for potential illuminators of opportunity and the use of multi-source detection frameworks. Accordingly, we propose a lightweight version of the extensive cancellation algorithm (ECA), which achieves clutter suppression performance comparable to ECA while reducing the computational and space complexity by at least one order of magnitude. This is achieved through innovative adjustments to the reference signal subspace matrix within the ECA framework, resulting in a redefined approach to the computation of the autocorrelation and cross-correlation matrices. This novel modification significantly simplifies the computational aspects. Furthermore, we introduce a dimension-expanding technique that streamlines clutter estimation. Overall, the proposed method replaces the computationally intensive aspects of the original ECA with fast Fourier transform (FFT) and inverse FFT operations, and eliminates the construction of the memory-intensive signal subspace. Comparing the proposed method with the ECA and its batched version, the central advantages are more streamlined implementation and minimal storage requirements, all without compromising performance. The efficacy of this approach is demonstrated through both simulations and field experimental results.

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