An effective approach in solving the sea clutter spectrum extraction problem is studied in the paper.Different from the conventional signal to noise ratio（SNR）method based on Doppler frequency or range domain inform...An effective approach in solving the sea clutter spectrum extraction problem is studied in the paper.Different from the conventional signal to noise ratio（SNR）method based on Doppler frequency or range domain information,a method is developed to characterize the differences between the sea echo and those interferences are by signal to interference plus noise ratio（SINR）which jointly utilizing the range,Doppler frequency and azimuth domain information.Furthermore,these differences can be adaptable to adverse conditions by forming the necessary boundaries and constraints in searching of the maximum SINR,which greatly promotes the extraction of sea clutter spectrum.The real high frequency surface wave radar（HFSWR）data demonstrate that the proposed method is less influenced by those interferences and can effectively extract the sea clutter spectrum even under the adverse conditions.Furthermore,it has been shown as an effective method for ship detection and sea state remote sensing of HFSWR.展开更多
针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地...针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地提出了基于YOLOv5识别模型的HFSWR杂波和干扰识别分类方法。该方法旨在帮助研究人员在海量实验数据中快速筛选出符合其科学研究需求的数据集,从而提高研究效率和数据准确性。在具体实施过程中,通过采用批量实测距离-多普勒(Range-Doppler,RD)谱数据对所提出模型进行训练和分析,使该方法能够在频域范围内对杂波和干扰进行有效识别。本研究以该识别分类算法为核心,进一步基于Python语言设计了一款地波雷达智能杂波和干扰识别分类软件。经过严格的批量实测数据测试验证,该软件能够满足设计需求,具有良好的可靠性,极大地提高了研究人员筛选有效实测数据的工作效率,为科学研究工作提供了有力的技术支撑。展开更多
基金Supported by the National Natural Science Foundation of China(61501131,61171180)National Marine Technology Program for Public Welfare(201505002)Fundamental Research Funds for the Central Universities(HIT.MKSTISP.2016 26)
文摘An effective approach in solving the sea clutter spectrum extraction problem is studied in the paper.Different from the conventional signal to noise ratio（SNR）method based on Doppler frequency or range domain information,a method is developed to characterize the differences between the sea echo and those interferences are by signal to interference plus noise ratio（SINR）which jointly utilizing the range,Doppler frequency and azimuth domain information.Furthermore,these differences can be adaptable to adverse conditions by forming the necessary boundaries and constraints in searching of the maximum SINR,which greatly promotes the extraction of sea clutter spectrum.The real high frequency surface wave radar（HFSWR）data demonstrate that the proposed method is less influenced by those interferences and can effectively extract the sea clutter spectrum even under the adverse conditions.Furthermore,it has been shown as an effective method for ship detection and sea state remote sensing of HFSWR.
文摘针对高频地波雷达(High frequency surface wave radar,HFSWR)在探测中产生的回波数据,传统的人工识别和分类方法存在工作量大、效率低和主观性强等问题,本研究在分析一阶海杂波、电离层杂波和射频干扰的回波数据特性的基础上,创新性地提出了基于YOLOv5识别模型的HFSWR杂波和干扰识别分类方法。该方法旨在帮助研究人员在海量实验数据中快速筛选出符合其科学研究需求的数据集,从而提高研究效率和数据准确性。在具体实施过程中,通过采用批量实测距离-多普勒(Range-Doppler,RD)谱数据对所提出模型进行训练和分析,使该方法能够在频域范围内对杂波和干扰进行有效识别。本研究以该识别分类算法为核心,进一步基于Python语言设计了一款地波雷达智能杂波和干扰识别分类软件。经过严格的批量实测数据测试验证,该软件能够满足设计需求,具有良好的可靠性,极大地提高了研究人员筛选有效实测数据的工作效率,为科学研究工作提供了有力的技术支撑。
