Sea Clutter Suppression Based on Selective Reconstruction of Features

Wen, Liwu; Zhong, Chao; Huang, Xuejun; Ding, Jian

doi:10.1109/apsar46974.2019.9048548

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Usually, the two algorithms can be used together, for example when dealing with sea clutter problems with severe interference. However, compared with sea clutter, river clutter is relatively simple, so only CFAR is used for velocity detection [97].…”

Section: Target Detection Methodsmentioning

confidence: 99%

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

Huang

Chen

Liu

et al. 2023

Water

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As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology.

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Section: Target Detection Methodsmentioning

confidence: 99%

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

Huang

Chen

Liu

et al. 2023

Water

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“…However, these methods relied predominantly on deep learning techniques for data preprocessing and overlooked the multidimensional characteristics of the target. On the other hand, alternative approaches incorporated the TBD framework for intra-frame detection, as illustrated in studies [13,14]. However, these methods may not have adequately considered nonhomogeneous backgrounds.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Track-before-Detect Method for Non-Cooperative Bistatic Radar Based on Deep Learning

et al. 2023

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Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method’s ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems.

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“…In order to improve the sea clutter suppression performance, more complex machine learning models have been applied to sea clutter suppression tasks [25]. The works [26] and [27] proposed the clutter suppression networks based on deep convolution autoencoders, and [28] proposed clutter suppression method based on deep convolutional neural networks and achieved good results. In the training phase of these methods, a large number of clutter sample and corresponding clutter-free sample pairs are often needed as training samples.…”

Section: Introductionmentioning

confidence: 99%

A Sea Clutter Suppression Method Based on Machine Learning Approach for Marine Surveillance Radar

Pei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Sea Clutter Suppression Based on Selective Reconstruction of Features

Cited by 11 publications

References 8 publications

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

Radar Technology for River Flow Monitoring: Assessment of the Current Status and Future Challenges

A Two-Stage Track-before-Detect Method for Non-Cooperative Bistatic Radar Based on Deep Learning

A Sea Clutter Suppression Method Based on Machine Learning Approach for Marine Surveillance Radar

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