Sea surface micromotion target detection and feature extraction method based on short-time fractional Fourier transform

A micro-moving target and feature extraction technology, which is applied in measurement devices, radio wave measurement systems, and radio wave reflection/re-radiation. , the effect of suppressing sea spikes

Active Publication Date: 2012-11-21
NAVAL AVIATION UNIV
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Problems solved by technology

[0008] Fractional Fourier Transform (FRFT) has good energy aggregation and detection performance for

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  • Sea surface micromotion target detection and feature extraction method based on short-time fractional Fourier transform
  • Sea surface micromotion target detection and feature extraction method based on short-time fractional Fourier transform
  • Sea surface micromotion target detection and feature extraction method based on short-time fractional Fourier transform

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Embodiment Construction

[0030] The following is attached with the manual figure 1 The present invention is described in further detail. Refer to the attached figure 1 , the processing flow of the present invention is divided into the following steps:

[0031] (1) sea peak identification;

[0032] (2) Sea clutter data screening;

[0033] (3) Micro-moving target detection in FRFT domain;

[0034] (4) Optimal FRFT domain filtering;

[0035] (5) Micro-motion feature estimation in STFRFT domain.

[0036] The following is attached with the manual figure 2 The present invention is described in further detail. Refer to the attached figure 2 , the specific embodiment of the present invention divides the following steps:

[0037] (1) Send the radar echo data in the same distance unit obtained after amplification and demodulation to the storage device 1 for preprocessing, and obtain the input signal sequence c(i) of N sampling points, i=1, 2 ,..., N.

[0038] (2) Compare the input signal sequence c...

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Abstract

The invention relates to a sea surface micromotion target detection and feature extraction method based on short-time fractional Fourier transform (STFRFT) and belongs to the technical field of radar signal processing and detection. The method comprises the following steps: 1) sea peak identification: dividing sea clutters into sea peak sequences and sea clutter background sequences free of sea peaks; 2) sea clutter data screening: selecting the sea clutter background sequences corresponding to the minimal mean power as data to be detected; 3) FRFT domain micromotion target detection: taking the FRFT domain signal amplitude values as detection statistical quantities and comparing the detection statistical quantities with the thresholds; 4) optimal FRFT domain filtering: extracting multi-component micromotion signals by using a narrow bandpass filter; and 5) setting an optimal time window length and estimating micromotion features in the STFRFT domain. The method can automatically adapt to suppress sea clutters to improve the signal-to-clutter ratio, can effectively isolate and extract the multi-component micromotion signals, provides a new approach to sea surface weak target detection and feature extraction, and is significant in promotion and application.

Description

1. Technical field [0001] The invention belongs to the technical field of radar signal processing and detection, in particular to the moving target detection technology of sea detection radar. 2. Background technology [0002] The detection technology of weak targets in sea clutter, especially "low (low grazing angle), slow (stationary or slow moving), small (small target size)" targets has always been a difficult problem in the field of radar signal processing, not only of theoretical importance Sex, and occupies a very important position in both military and civilian use. Traditional target detection methods are based on statistical theory, and sea clutter is regarded as a random process, but both the target model and the clutter model show a trend of diversification, especially the clutter distribution model, which is often untenable or not valid in complex environments. It is completely established, which makes the classical object detection methods unable to achieve th...

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Application Information

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IPC IPC(8): G01S7/292G01S13/88
Inventor 关键陈小龙何友黄勇王国庆宋杰刘宁波
Owner NAVAL AVIATION UNIV
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