Ocean Surface Current Extraction Scheme With High-Frequency Distributed Hybrid Sky-Surface Wave Radar System

Li, Miao; Lan, Zhang; Wu, Xiongbin; Yue, Xianchang; Emery, William J.; Yi, Xianzhou; Liu, Jianfei; Yang, Guobin

doi:10.1109/tgrs.2018.2834938

Cited by 23 publications

(24 citation statements)

References 39 publications

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“…[72,73]) or to assess physical and biological parameters (e.g. [32,74,75]). Imagery data also appear as a non-negligible input with associated remote sensing techniques (e.g.…”

Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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Maritime Spatial Planning (MSP) has become of key interest in many countries around the world in the last decade, leading to an increased need to inform the historical spatial and temporal footprint of maritime activities. If this knowledge was still recently considered a blind spot, recent developments of maritime surveillance systems (e.g. AIS, VTS, VMS) have allowed to fill some of these gaps by generating significant amounts of spatial data on ships at sea. In this paper, the use of these maritime surveillance data for planning purposes is explored through the lens of the international scientific literature. A first set of 2030 articles dealing with maritime surveillance data and collected through the Web of Science collection was explored to determine the type of data used, the maritime activities addressed and the main objective of each paper (technical developments, safety and security, environmental protection, MSP). This allowed to highlight the growing interest on maritime surveillance data in the scientific literature over the past decades, predominantly towards AIS data and shipping. Over the 2030 papers, only 63 dealt specifically with MSP. These were extracted and explored, allowing to highlight the potential of these data to feed in MSP processes, with a specific focus on fisheries. Nevertheless, the description of actual uses of surveillance-based information within MSP processes remained particularly rare. If this can be seen as a result of a science-policy delay, we suggest that it is mostly related to issues of accessibility, acceptance by economic stakeholders and appropriation by decision makers.

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“…[72,73]) or to assess physical and biological parameters (e.g. [32,74,75]). Imagery data also appear as a non-negligible input with associated remote sensing techniques (e.g.…”

Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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“…When ignoring the influences of ocean current and ionosphere, the Doppler frequency of first‐order Bragg peaks f B ± in HFHSSW radar is calculated by (referring Barrick, ; Li et al, )

f_{B \pm} = \pm \sqrt{\frac{g f_{0}}{2 πc}} {(1 + 2 cosβcosα + cos α^{2})}^{1 / 4}

where f 0 is the radar operating frequency, g is the acceleration of gravity, and c is the speed of light. The plus‐minus sign represents the positive and negative Bragg peaks, respectively.…”

Section: Model and Methodsmentioning

confidence: 99%

“…When ignoring the influences of ocean current and ionosphere, the Doppler frequency of first-order Bragg peaks f B± in HFHSSW radar is calculated by (referring Barrick, 1972;Li et al, 2018)…”

Section: Model Of Radar Echo Modulated By Ionospherementioning

confidence: 99%

“…HFHSSW radar has an advancement in large-scale environment surveillance and early warning. In the recent decade, a few studies about HFHSSW radar have been conducted on system simulation (e.g., Chen, Huang, & Gill, 2016;Chen et al, 2016aChen et al, , 2016bJiang et al, 2011;Jiao et al, 2007;Li et al, 2016;Walsh et al, 2014Walsh et al, , 2015, system designation and experiment (e.g., Cui et al, 2016;Zhao et al, 2013), application in target over-the-horizon detection (see, e.g., Riddolls, 2007Riddolls, , 2008Zhang et al, 2014), and ocean surface dynamic parameters inversion (see, e.g., Ji et al, 2017;Li et al, 2017Li et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

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Correction of Ionospheric Distortion on HF Hybrid Sky‐Surface Wave Radar Calibrated by Direct Wave

et al. 2019

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The performance of the high-frequency hybrid sky-surface wave radar is often degraded due to the distortion of radar echoes induced by time-varying ionosphere. Here a modified S-transform method based on the direct wave for ionosphere decontamination of high-frequency hybrid sky-surface wave radar is presented. First, we extract the time-varying-ionosphere-distorted phase of the echoes from the direct wave of high-frequency hybrid sky-surface wave radar by using the modified S-transform. Then this phase function is used to decontaminate the ionosphere distortion of radar echo at each range bins. Simulations and experimental data are conducted to validate the performance of the proposed method on decontaminating the distortion. The experimental data are acquired by Wuhan Universitys ocean state measuring and analyzing radar. A mass of radar data is processed and analyzed, demonstrating that the decontamination effect is statistically significant. The method is not only effective to remove the Doppler shift of direct wave and Bragg peaks caused by ionosphere but also able to narrow the broadened direct wave and Bragg peaks. Meanwhile, the SNR is improved in most range bins; the background noise is reduced detectably, sometimes significantly; and some isolated radar echoes near direct wave or Bragg peaks are recovered or enhanced. These results indicate that the proposed decontamination algorithm works well.

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“…High-frequency surface wave radar (HFSWR) transmits high-frequency electromagnetic waves and receives the backscatter echo based on the mechanism of coastal surface diffraction propagation [1]. By analyzing the received echo, continuous monitoring over a wide range can be achieved, which includes vessels [2] and the low altitude flying objects over the sea [3] as well as the ocean dynamics parameters [4][5][6]. It is widely used and has become the primary technical means in the field of maritime-state monitoring and target vessel detection.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

Zhang

You

et al. 2018

Remote Sensing

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High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.such as the sea clutter, the ionospheric clutter and the radio frequency interference (RFI) severely limit the detection ability of HFSWR. Hence, the detection and suppression of the clutter [7] and the interference [8,9] is essential to guarantee the performance of HFSWR, the automatic and accurate detection in particular is a prerequisite for the suppression of the clutter. Although the suppression of clutter and interference can be implemented without prior detection, both theoretical and simulation results show that the signal to noise ratio (SNR) of the target also can be obviously reduced after implementing clutter/interference suppression [10]. Hence, it is of great importance to detect whether there is existence of clutter/interference before initiating clutter/interference suppression for reservation of the signal energy and improvement of computation efficiency.The most common ways to identify and detect the clutter and the interference based on Range-Doppler (RD) spectral image are usually divided into two parts: The first step is to extract features from the images; then use the traditional image segmentation techniques to achieve the goal or use the machine learning which is currently the cutting-edge technology in the field of identifying and detecting objects. Chen et al. [11] used the corresponding analysis and cluster analysis methods to classify interfe...

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Ocean Surface Current Extraction Scheme With High-Frequency Distributed Hybrid Sky-Surface Wave Radar System

Cited by 23 publications

References 39 publications

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Correction of Ionospheric Distortion on HF Hybrid Sky‐Surface Wave Radar Calibrated by Direct Wave

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

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