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Enhanced target detection using fractional Fourier transform features with threshold-modified normalization
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  • XingYu Jiang,
  • Ningbo Liu,
  • Hao Ding,
  • Yunlong Dong,
  • Jian Guan,
  • Tong Liu
XingYu Jiang
Naval Aviation University
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Ningbo Liu
Naval Aviation University Institute of Information Fusion

Corresponding Author:[email protected]

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Hao Ding
Naval Aviation University Institute of Information Fusion
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Yunlong Dong
Naval Aviation University Institute of Information Fusion
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Jian Guan
Naval Aviation University
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Tong Liu
Shandong University School of Journalism and Communication
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Abstract

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non-normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, we propose a target detection method that utilizes these three adjusted features. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri-feature based detector.
26 Apr 2024Submitted to Electronics Letters
30 Apr 2024Submission Checks Completed
30 Apr 2024Assigned to Editor
30 Apr 2024Review(s) Completed, Editorial Evaluation Pending
15 May 20241st Revision Received
16 May 2024Review(s) Completed, Editorial Evaluation Pending
29 May 2024Editorial Decision: Accept