Multifractal Correlation Analysis of Autoregressive Spectrum-Based Feature Learning for Target Detection Within Sea Clutter

Fan, Yifei; Tao, Mingliang; Su, Jia

doi:10.1109/tgrs.2021.3137466

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…The high‐dimensional feature space combined with multiple features of sea clutter can effectively combine the sea surface weak target detection problem with advanced AI technology to identify the target. Considering that radar echo can be focused into images, and CNN has outstanding feature extraction ability for the image, this paper will achieve target detection based on CNN, and such research is currently rare (Fan et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Marine radar monitoring IoT system and case study of target detection based on PPI images

Zhang

Guo

et al. 2023

Expert Systems

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To ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy.

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Section: Related Workmentioning

confidence: 99%

Marine radar monitoring IoT system and case study of target detection based on PPI images

Zhang

Guo

et al. 2023

Expert Systems

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“…The target echo often shows low‐observable characteristics under the influence of clutter background and complex motion within the clutter Doppler extent. This will increase the difficulty for radar to detect weak targets in clutter background (Fan et al., 2022; Yu et al., 2020). The radar echo system needs to extract and analyze the relevant information of the measured target from the received echo signal.…”

Section: Introductionmentioning

confidence: 99%

An ASC‐Based Unitary Matrix Pencil Method for Parameter Estimation of Group Target

Sun

et al. 2023

Radio Science

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The unitary matrix pencil (UMP) method has been successfully applied to antenna array optimization. In this paper, the UMP method is applied to the parameter estimation of target detection based on the attributed scattering center (ASC) model. This method can accurately estimate the number of arbitrary group targets in the space. First, the RCS is reconstructed based on the ASC model. Since the unitary matrix can be used to process the parameter estimation of the echo with “sum of attenuation exponents,” the unitary matrix is constructed by the samples from this reconstructed RCS. Moreover, the stronger scattering center can be characterized via the larger singular values, and the number of group targets can be obtained by solving the number of large singular values. Then, singular value decomposition is performed on this unitary matrix, and some larger singular values are screened by setting an appropriate threshold. The higher estimation accuracy can be obtained by fusing the echoes from multiple frequencies and angles. Besides, the estimation accuracies of this proposed method in this paper are discussed under different signal‐to‐noise ratios considering the noise in the actual scene. Simulation results show that this method can effectively and accurately estimate the number of group targets, and it shows strong robustness.

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A Novel Airborne Radar Sea Clutter Simulation Method Based on Full Link Factors

Jiang,

Fan,

Gong

et al. 2023

2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT)

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Multifractal Correlation Analysis of Autoregressive Spectrum-Based Feature Learning for Target Detection Within Sea Clutter

Cited by 7 publications

References 22 publications

Marine radar monitoring IoT system and case study of target detection based on PPI images

Marine radar monitoring IoT system and case study of target detection based on PPI images

An ASC‐Based Unitary Matrix Pencil Method for Parameter Estimation of Group Target

A Novel Airborne Radar Sea Clutter Simulation Method Based on Full Link Factors

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