Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unkn...Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.展开更多
针对多部空间姿态时刻变化的机载雷达,提出了一种全新的、无需依赖先验信息(如雷达位置和姿态)的空间配准策略,本策略涉及到实时配准参数解算以及融合点迹优化等多个关键环节。利用目标点迹数据建立雷达间的空间姿态关系,借助递归最小...针对多部空间姿态时刻变化的机载雷达,提出了一种全新的、无需依赖先验信息(如雷达位置和姿态)的空间配准策略,本策略涉及到实时配准参数解算以及融合点迹优化等多个关键环节。利用目标点迹数据建立雷达间的空间姿态关系,借助递归最小二乘法(recursive least squares,RLS)迭代求解旋转矩阵和平移向量,进而实现各雷达坐标系的实时配准。此外,引入了一种基于融合结果的目标轨迹级空间配准参数反向调节策略,通过构建配准误差模型并运用梯度下降法进行优化,有效降低了融合轨迹误差,提升了配准精度与跟踪质量。所提策略为雷达空间姿态的实时配准问题提供了一种全面且高效的解决方案,具有重大的理论价值与实际应用前景。展开更多
针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目...针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。展开更多
基金supported by the National Natural Science Foundation of China(61931015,62071335)the Science and Technology Program of Shenzhen(JCYJ20170818112037398)the Technological Innovation Project of Hubei Province of China(2019AAA061).
文摘Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.
文摘针对多部空间姿态时刻变化的机载雷达,提出了一种全新的、无需依赖先验信息(如雷达位置和姿态)的空间配准策略,本策略涉及到实时配准参数解算以及融合点迹优化等多个关键环节。利用目标点迹数据建立雷达间的空间姿态关系,借助递归最小二乘法(recursive least squares,RLS)迭代求解旋转矩阵和平移向量,进而实现各雷达坐标系的实时配准。此外,引入了一种基于融合结果的目标轨迹级空间配准参数反向调节策略,通过构建配准误差模型并运用梯度下降法进行优化,有效降低了融合轨迹误差,提升了配准精度与跟踪质量。所提策略为雷达空间姿态的实时配准问题提供了一种全面且高效的解决方案,具有重大的理论价值与实际应用前景。
文摘针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。
