中文摘要：

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.

英文摘要：

Target detection and tracking ability of high frequency surface wave radar（HFSWR）is limited by heavy sea clutter and target fluctuation.A data fusion and tracking filtering algorithm was proposed based on multi-frequency HFSWR to overcome these shortcomings.Multi-frequency data were fused by weighted nearest-neighbor algorithm,and fused results was filtered by unscented Kalman filter（UKF）.A 10 dexperiment was conducted in the East China Sea to verify the system performance.Thresholds and weights of fused parameters,including range,speed and orientation,were given on the basis of multi-frequency data features.A technological process from target detection to tracking was established,and indicators for evaluating target detection and tracking ability of multifrequency HFSWRs were proposed.System performance was defined in terms of time on target（ToT）,track fragmentation,tracking area and accuracy.Experimental results show that ToT and detection rate increase after data fusion.Tracking robustness is enhanced for the decreasement of location error and track fragmentation.