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 and Senlin Geng
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Qianzhao Lei: School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China & School of Mathematics & Physics, Weinan Normal University, Weinan, China
Zhensen Wu: School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China
Lixin Guo: School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China
Junmei Fan: Chinese Research Institute of Radiowave Propagation, National Key Laboratory of Electromagnetic Environment, Qingdao, China
Senlin Geng: School of Mathematics & Physics, Weinan Normal University, Weinan, China

International Journal of Information Technology and Web Engineering (IJITWE), 2017, vol. 12, issue 4, 56-71

Abstract: 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.

Date: 2017
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