现实中针对目标任务往往没有标签数据,如何在辅助源领域中学习到知识以应用目标任务成为了一个关键性问题。为此提出一种新方法,即领域困惑迁移网络(Domain Confusion Transfer Network,DCTN)。该方法首先在残差网络的基础上设计出领域...现实中针对目标任务往往没有标签数据,如何在辅助源领域中学习到知识以应用目标任务成为了一个关键性问题。为此提出一种新方法,即领域困惑迁移网络(Domain Confusion Transfer Network,DCTN)。该方法首先在残差网络的基础上设计出领域分布部分,然后在领域分布部分的输出端使用领域困惑损失函数以使得特征提取网络学习领域不变特征。通过实验证实,该方法可以有效地学习领域不变特征且能够获得良好的效果。展开更多
直接数据域(Direct data dom a in,DDD)方法利用子孔径平滑来从单个距离门中获得足够样本,但空域、时域孔径损失严重,使得空时自适应处理的地面动目标检测性能下降严重。针对该问题提出了一种不需要协方差矩阵估计和求逆的DDD方法,该方...直接数据域(Direct data dom a in,DDD)方法利用子孔径平滑来从单个距离门中获得足够样本,但空域、时域孔径损失严重,使得空时自适应处理的地面动目标检测性能下降严重。针对该问题提出了一种不需要协方差矩阵估计和求逆的DDD方法,该方法将多级维纳滤波器引入到DDD的最优权求解过程中,在低的空时孔径损失下仍然能够获得好的性能。某机载实测雷达数据的实验结果验证了该方法的有效性。展开更多
提出了一种联合空间、时间和距离的三维直接数据域(space-time-range direct data domain,STR-DDD)算法。与传统方法相比,该方法将目标周边距离单元的数据联合起来,充分利用目标距离向上的信息,在空域上能够对孤立干扰形成深的凹口,并...提出了一种联合空间、时间和距离的三维直接数据域(space-time-range direct data domain,STR-DDD)算法。与传统方法相比,该方法将目标周边距离单元的数据联合起来,充分利用目标距离向上的信息,在空域上能够对孤立干扰形成深的凹口,并且当目标导向矢量存在幅相误差时仍具有稳健性。在MCARM数据平台上的实验结果表明,该方法能够提高地面动目标检测(ground moving target indication,GMTI)性能。展开更多
A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group targe...A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group target, split models of group targets are established based on a sliding window feedback mechanism to determine the occurrence and classification of split maneuvering, which makes the tracked objects focus by group members effectively. The track of an outlier single target is reconstructed by the sequential least square method. At the same time, the relationship between the group members is expressed by the complex domain topological description method, which solves the problem of point-track association between the members. The Singer method is then used to update the tracks. Compared with classical multi-target tracking algorithms based on Multiple Hypothesis Tracking （MHT） and the Different Structure Joint Probabilistic Data Association （DS-JPDA） algorithm, the proposed algorithm has better tracking accuracy and stability, is robust against environmental clutter and has stable time-consumption under both classical radar conditions and partly resolvable conditions.展开更多
文摘现实中针对目标任务往往没有标签数据,如何在辅助源领域中学习到知识以应用目标任务成为了一个关键性问题。为此提出一种新方法,即领域困惑迁移网络(Domain Confusion Transfer Network,DCTN)。该方法首先在残差网络的基础上设计出领域分布部分,然后在领域分布部分的输出端使用领域困惑损失函数以使得特征提取网络学习领域不变特征。通过实验证实,该方法可以有效地学习领域不变特征且能够获得良好的效果。
文摘直接数据域(Direct data dom a in,DDD)方法利用子孔径平滑来从单个距离门中获得足够样本,但空域、时域孔径损失严重,使得空时自适应处理的地面动目标检测性能下降严重。针对该问题提出了一种不需要协方差矩阵估计和求逆的DDD方法,该方法将多级维纳滤波器引入到DDD的最优权求解过程中,在低的空时孔径损失下仍然能够获得好的性能。某机载实测雷达数据的实验结果验证了该方法的有效性。
文摘提出了一种联合空间、时间和距离的三维直接数据域(space-time-range direct data domain,STR-DDD)算法。与传统方法相比,该方法将目标周边距离单元的数据联合起来,充分利用目标距离向上的信息,在空域上能够对孤立干扰形成深的凹口,并且当目标导向矢量存在幅相误差时仍具有稳健性。在MCARM数据平台上的实验结果表明,该方法能够提高地面动目标检测(ground moving target indication,GMTI)性能。
基金co-supported by the National Natural Science Foundation of China(Nos.61471383,61531020,61471379 and 61102166)
文摘A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group target, split models of group targets are established based on a sliding window feedback mechanism to determine the occurrence and classification of split maneuvering, which makes the tracked objects focus by group members effectively. The track of an outlier single target is reconstructed by the sequential least square method. At the same time, the relationship between the group members is expressed by the complex domain topological description method, which solves the problem of point-track association between the members. The Singer method is then used to update the tracks. Compared with classical multi-target tracking algorithms based on Multiple Hypothesis Tracking （MHT） and the Different Structure Joint Probabilistic Data Association （DS-JPDA） algorithm, the proposed algorithm has better tracking accuracy and stability, is robust against environmental clutter and has stable time-consumption under both classical radar conditions and partly resolvable conditions.
