双站前视低频超宽带(UWB)SAR兼具双站前视的复杂成像构型和低频UWB的强距离方位耦合两个特点,因此极大地增加了实现高精度成像处理的难度。针对这个问题,该文提出一种基于快速因式分解后向投影(FFBP)算法的双站前视低频UWB SAR成像处理...双站前视低频超宽带(UWB)SAR兼具双站前视的复杂成像构型和低频UWB的强距离方位耦合两个特点,因此极大地增加了实现高精度成像处理的难度。针对这个问题,该文提出一种基于快速因式分解后向投影(FFBP)算法的双站前视低频UWB SAR成像处理方法。首先,基于双站前视低频UWB SAR的成像几何构型和信号模型,给出了双站前视低频UWB SAR原始BP算法成像的原理和流程。其次,在上述基础上,推导了双站前视低频UWB SAR FFBP算法成像处理的精确相位误差形式,并分析了相位误差对成像处理的影响,据此建立了双站前视低频UWB SAR FFBP成像处理中的子孔径和子区域划分原则。接下来,给出了双站前视低频UWB SAR FFBP算法成像处理流程,并对比分析了BP算法和FFBP算法的成像效率。最后,利用仿真实验证明了文中所作理论分析的正确性和所提方法的有效性。展开更多
后向投影(Back Projection,BP)算法具有精确聚焦、完美运动补偿等优点,适合于机载超宽带合成孔径雷达(Ultra Wide Band Synthetic Aperture Radar,UWB SAR)成像,但是巨大的计算量限制了它的实际应用。子块快速因子分解后向投影算法(Sub-...后向投影(Back Projection,BP)算法具有精确聚焦、完美运动补偿等优点,适合于机载超宽带合成孔径雷达(Ultra Wide Band Synthetic Aperture Radar,UWB SAR)成像,但是巨大的计算量限制了它的实际应用。子块快速因子分解后向投影算法(Sub-Image Fast Factorized Back Projection,SIFFBP)算法大幅度减小了BP算法的计算量,提高了BP算法的实用性。本文通过分析SIFFBP算法区域划分的约束条件,提出了一种基于最优区域划分的改进算法,解决了传统SIFFBP算法在小波束积累角时加速性能下降的问题。当波束积累角小于60度或成像区域长宽相差较大时,改进算法进一步减小了计算量。仿真和实测SAR数据的成像结果验证了改进算法的性能。展开更多
Frequent Pattern mining plays an essential role in data mining. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especia...Frequent Pattern mining plays an essential role in data mining. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.In this study, we introduce a novel frequent pattern growth (FP-growth)method, which is efficient and scalable for mining both long and short frequent patterns without candidate generation. And build a new project frequent pattern growth (PFP-tree)algorithm on this study, which not only heirs all the advantages in the FP-growth method, but also avoids it's bottleneck in database size dependence. So increase algorithm's scalability efficiently.展开更多
文摘双站前视低频超宽带(UWB)SAR兼具双站前视的复杂成像构型和低频UWB的强距离方位耦合两个特点,因此极大地增加了实现高精度成像处理的难度。针对这个问题,该文提出一种基于快速因式分解后向投影(FFBP)算法的双站前视低频UWB SAR成像处理方法。首先,基于双站前视低频UWB SAR的成像几何构型和信号模型,给出了双站前视低频UWB SAR原始BP算法成像的原理和流程。其次,在上述基础上,推导了双站前视低频UWB SAR FFBP算法成像处理的精确相位误差形式,并分析了相位误差对成像处理的影响,据此建立了双站前视低频UWB SAR FFBP成像处理中的子孔径和子区域划分原则。接下来,给出了双站前视低频UWB SAR FFBP算法成像处理流程,并对比分析了BP算法和FFBP算法的成像效率。最后,利用仿真实验证明了文中所作理论分析的正确性和所提方法的有效性。
文摘后向投影(Back Projection,BP)算法具有精确聚焦、完美运动补偿等优点,适合于机载超宽带合成孔径雷达(Ultra Wide Band Synthetic Aperture Radar,UWB SAR)成像,但是巨大的计算量限制了它的实际应用。子块快速因子分解后向投影算法(Sub-Image Fast Factorized Back Projection,SIFFBP)算法大幅度减小了BP算法的计算量,提高了BP算法的实用性。本文通过分析SIFFBP算法区域划分的约束条件,提出了一种基于最优区域划分的改进算法,解决了传统SIFFBP算法在小波束积累角时加速性能下降的问题。当波束积累角小于60度或成像区域长宽相差较大时,改进算法进一步减小了计算量。仿真和实测SAR数据的成像结果验证了改进算法的性能。
文摘Frequent Pattern mining plays an essential role in data mining. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.In this study, we introduce a novel frequent pattern growth (FP-growth)method, which is efficient and scalable for mining both long and short frequent patterns without candidate generation. And build a new project frequent pattern growth (PFP-tree)algorithm on this study, which not only heirs all the advantages in the FP-growth method, but also avoids it's bottleneck in database size dependence. So increase algorithm's scalability efficiently.
