Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering

Bi, Xiaowen; Guo, Shenglong; Yang, Yunxiu; Shu, Qin

doi:10.1109/tgrs.2022.3192893

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…However, this kind of algorithm is usually difficult to achieve the given false alarm rate in the low SCR (Signal-to-Clutter Ratio) scene under the sea clutter interference, which restricts the improvement of radar system detection performance. For the target detection in sea clutter, some researchers have proposed a series of methods based on statistical features [5][6][7][8][9][10][11] .…”

Section: Background and Related Workmentioning

confidence: 99%

“…They defined MSR as a specific linear spectral statistic indicating data correlation, and proved through experiments that MSR is an effective feature to distinguish target echo and sea clutter. Besides, the velocity changes of the target and sea clutter can also be used as detection features [8][9][10][11] . For example, Chen et al 8 studied the micro-Doppler effect and proposed a micro-Doppler signal extraction algorithm based on short-time fractional Fourier transform (STFRFT).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Although these methods based on simple mathematical calculations have high efficiency, its detection performance is relatively poor. With the development of machine learning, methods based on hand-crafted features have been proposed [5][6][7][8][9][10][11] . The commonly used paradigm is to design one or several features by time-frequency domain analysis, and utilized these features to classify sea clutter and maritime targets.…”

mentioning

confidence: 99%

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SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

Wang¹,

Li²

2023

Sci Rep

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Radar detection of maritime targets plays an important role in marine environment monitoring. For civil maritime detection in the areas of inshore coastal, pulse-compression radar is universally used owing to its low cost. The complex sea clutter in the practical application will greatly affect the received radar echoes. Due to the inability to accurately describe the differences in characteristics between sea clutter and maritime targets, the detection performance of methods based on mathematical derivation is not satisfactory in actual deployment. Recently, neural-based methods have made strides in many pattern recognition tasks, such as computer vision and natural language processing. The sophisticated deep neural models can be applied to different downstream tasks due to their powerful learning ability. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on deep learning. To better model the sequence correlation of radar echoes, we propose a Self-Adaption Local Augmented Long Short-Term Memory (SALA-LSTM) structure. The proposed SALA-LSTM integrates adaptive convolution into vanilla LSTM cells, which not only maintains the inherent overall sequence modeling ability of vanilla LSTM, but also strengthens its ability to perceive the correlation on a small scale in the local scope. Based on SALA-LSTM and other neural structures, we propose a radar target detection network. A measured dataset containing different typical scenarios is utilized to evaluate the detection probability and false alarm rate. The detection performance of our proposed network is superior to that of the existing methods.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

Wang¹,

Li²

2023

Sci Rep

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“…The second category is based on processing in the transform domain, including short-time Fourier transform (STFT), fractional Fourier transform (FRFT), wavelet transform (WT), etc. [2][3][4][5]. The third category is based on subspace decomposition theory, such as singular value decomposition (SVD) [6,7] and eigenvalue decomposition (EVD) [8].…”

Section: Introductionmentioning

confidence: 99%

Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization

Peng

Liu

2023

Applied Sciences

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In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by reconstructing and cancelling out the sea clutter. Since the fixed sparse base is not always completely applicable for the sparse representation of sea clutter, we propose a sparse base optimization algorithm based on transfer learning to convert the fixed sparse base into an adaptive one. Moreover, we introduce a sensing matrix optimization algorithm to decrease the cross-correlation coefficient between the measurement matrix and the sparse base matrix, which can enhance the signal reconstruction quality. Finally, we use the orthogonal matching pursuit algorithm to reconstruct the sea clutter and employ the reconstructed results to cancel and suppress the sea clutter. The simulation results demonstrate that the proposed method outperforms the traditional methods such as the root time-domain cancellation method and the singular value decomposition method (SVD). Therefore, the proposed method has great practical significance for the detection of bistatic sea surface targets.

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Coherent Detection of Weak Moving Targets in Compound-Gaussian Clutter Using Nonlinear Preprocessing System: Performance Measure and Implementation

Yan,

Wang,

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering

Cited by 5 publications

References 30 publications

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization

Coherent Detection of Weak Moving Targets in Compound-Gaussian Clutter Using Nonlinear Preprocessing System: Performance Measure and Implementation

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