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强烈的海杂波干扰以及目标起伏严重制约了高频雷达的目标检测与跟踪性能,针对这一不足,提出一种基于多频雷达的数据融合与跟踪算法.通过加权最近邻关联来融合多频数据;通过无味卡尔曼滤波输出跟踪结果.在中国东海舟山海域进行了为期10d的数据采集实验用于验证系统性能.研究了多频雷达数据特点,给出了合适的距离、速度和方位的融合门限及权重设置方法,建立了从检测到跟踪整套处理流程,并提出了用于检验多频工作性能的评价指标.评价指标包括目标在线时间、航迹分裂数目、跟踪区域和定位误差.研究结果表明:通过数据融合和跟踪滤波显著延长了目标在线时间,提高了目标检测概率并减小了定位误差和跟踪中出现的航迹分裂数量,增强了跟踪稳健性.
The strong sea clutter interference and target fluctuation seriously restrict the target detection and tracking performance of high-frequency radar. Aiming at this problem, a data fusion and tracking algorithm based on multi-frequency radar is proposed. The weighted nearest neighbor correlation Data were output by the unscented Kalman filter.The data acquisition experiments were carried out for a period of 10 days in Zhoushan waters of East China Sea to verify the system performance.The characteristics of multi-frequency radar data were studied and the suitable fusion of distance, velocity and azimuth Threshold and weight setting method, set up a complete set of process from detection to tracking, and put forward the evaluation index for testing the performance of multi-frequency.The evaluation index includes the target online time, the number of track splits, tracking area and positioning error.The results The results show that the target online time is significantly prolonged by data fusion and tracking filtering, the probability of target detection is improved, the positioning error and the number of track splits in tracking are reduced, and the tracking robustness is enhanced.