The Big Data Processing of HF Sky-Wave Radar Sea Echo for Detection of Sea Moving Targets

Qianzhao Lei; Zhensen Wu; Lixin Guo; Junmei Fan; Senlin Geng

doi:10.4018/ijitwe.2017100104

The Big Data Processing of HF Sky-Wave Radar Sea Echo for Detection of Sea Moving Targets

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2017100104 ◽

2017 ◽

Vol 12 (4) ◽

pp. 56-71 ◽

Cited By ~ 1

Author(s):

Qianzhao Lei ◽

Zhensen Wu ◽

Lixin Guo ◽

Junmei Fan ◽

Senlin Geng

Keyword(s):

Big Data ◽

Data Processing ◽

Radar Data ◽

Sea Surface ◽

False Alarms ◽

Successful Implementation ◽

Moving Target ◽

Decision Threshold ◽

Constant False Alarm Rate ◽

Wave Radar

A high-frequency (HF) sky-wave radar always monitoring large area of sea surface, for detecting sea surface moving objects, there must be big data waiting to be processed. A set of data processing methods were proposed, the successful implementation of HF sky-wave radar on the sea moving target detection. By setting the HF sky-wave radar parameters, after the initial data processing, the gotten HF sky-wave radar data were saved. Then a new HF sky-wave radar data processing method was provided, this method was the so-called three-step detection method (TSTM) which based on the constant false alarm rate (CFAR) technique. By using TSTM, setting the decision threshold G, with false alarms being ruled out, a moving target was detected out at last, its speed was calculated. The results also proved that TSTM could effectively reduce the sea clutter, and greatly lessen the echo-broadening and double-image caused by ionosphere contamination.

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Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Remote Sensing ◽

10.3390/rs11101190 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1190

Author(s):

Wenjie Shen ◽

Wen Hong ◽

Bing Han ◽

Yanping Wang ◽

Yun Lin

Keyword(s):

False Alarm ◽

Target Detection ◽

Background Subtraction ◽

Image Sequence ◽

False Alarms ◽

Moving Target ◽

Constant False Alarm Rate ◽

Moving Targets ◽

Moving Target Detection ◽

Original Algorithm

Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

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Changing the mindset for precision medicine: from incentivized biobanking models to genomic data

Genetics Research ◽

10.1017/s0016672319000077 ◽

2019 ◽

Vol 101 ◽

Cited By ~ 1

Author(s):

Daniel Tigard

Keyword(s):

Health Promotion ◽

Big Data ◽

Data Processing ◽

Organ Donation ◽

Precision Medicine ◽

Genomic Data ◽

Successful Implementation ◽

Significant Shift ◽

Ethical Challenges ◽

Big Data Processing

Abstract The emerging paradigm in contemporary healthcare, precision medicine, is widely seen as a revolutionary approach to both clinical treatment and overall health promotion. Precision models are making use of the most up-to-date technological advancements – such as genomics and ‘big data’ processing – in an effort to tailor healthcare to each individual. Yet the list of hurdles to successful implementation of precision medicine is no secret. Among the challenges, it was recently suggested in this journal that we must change the ‘mindset’ of patients, practitioners and the wider public (McGonigle, 2016). And while precision medicine indeed demands a significant shift, we must not understate the extent of the overhaul required. In particular, I argue, against McGonigle's suggestion, that the ethical challenges regarding participant contributions cannot be tackled by relying upon existing models of incentivized blood banking or organ donation. Instead, the success of precision medicine requires a wholescale change in mindset.

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X-Band Wave Radar for Coastal Upwelling Detection off the Southern Coast of Sicily

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0049.1 ◽

2017 ◽

Vol 34 (1) ◽

pp. 21-31 ◽

Cited By ~ 9

Author(s):

F. Raffa ◽

G. Ludeno ◽

B. Patti ◽

F. Soldovieri ◽

S. Mazzola ◽

...

Keyword(s):

Coastal Upwelling ◽

Surface Current ◽

Radar Data ◽

Sea Surface ◽

Open Sea ◽

Independent Information ◽

X Band ◽

Wave Radar ◽

Radar Data Processing

AbstractThe main aim of this work is to show the potentialities of an X-band radar system about the detection of the coastal upwelling phenomenon. This is made possible by means of the estimation of the sea surface current, which moves from the coastal areas toward the open sea for wind-induced upwelling events. The study presents the results of the X-band radar data processing for a system installed at Capo Granitola (southwestern Sicily). In particular, the radar data acquired in three different periods—namely, 5–6 November 2013, 8–9 February 2015, and 6 March 2015—indicated upwelling events. The occurrence of these events was confirmed by independent information derived from in situ wind data provided by a meteorological station and the analysis of the satellite-derived sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration.

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