针对快速分解后向投影算法(Fast Factorized Back Projection,FFBP)运算量大,实时处理较难的问题,提出了一种基于FPGA的数据分块并行处理的多阶段流水与同阶段并行的硬件架构设计。该设计将数据分块储存到ULTRARAM(URAM)组,使得在一个...针对快速分解后向投影算法(Fast Factorized Back Projection,FFBP)运算量大,实时处理较难的问题,提出了一种基于FPGA的数据分块并行处理的多阶段流水与同阶段并行的硬件架构设计。该设计将数据分块储存到ULTRARAM(URAM)组,使得在一个时钟周期完成数据地址索引,并对数据进行加权求和,得到插值结果;该设计采用多阶段流水与同阶段并行的硬件架构,充分利用算法并行特性,极大地减少了处理时间;在FPGA处理平台架构下,实现了基于单块板卡的数据分块并行的FFBP算法。经实验验证,当系统处理时钟频率为125 MHz,在147.05 ms时间内实现了1 024×512个采样点的32位单精度浮点复数据成像处理,验证了系统的有效性。展开更多
时域合成孔径成像算法可以更好地适应多子阵造成的方位向采样不均匀问题,并且具有存储空间小、并行处理方便的优点。但精确时域算法运算量非常大,快速分块反向传播投影(Fast Factorized Back Projection,FFBP)成像算法则可以大大降低成...时域合成孔径成像算法可以更好地适应多子阵造成的方位向采样不均匀问题,并且具有存储空间小、并行处理方便的优点。但精确时域算法运算量非常大,快速分块反向传播投影(Fast Factorized Back Projection,FFBP)成像算法则可以大大降低成像计算量。详细分析了FFBP声程误差的距离效应、孔径合并策略和图像分裂策略、成像的计算量等关键问题,并给出了仿真和实测数据成像结果。通过对仿真和实测成像结果的分析表明:FFBP算法可以提高计算效率,适用于实时合成孔径声纳成像系统。展开更多
This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model an...This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model and (CFBP) network model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets. This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing targets” is a 1 × 506 matrix of median values of owner-occupied homes in $1000’s. The result in this paper concludes that which one of the two networks appears to be a better indicator of the output data to target data network structure than maximizing predict. The CFBP network which is the best result from the Output_network for all samples are found from the equation output = 0.95 * Target + 1.2. The regression value is approximately 1, (R = 0.964). That means the Output_network is matching to the target data set (Median value of owner-occupied homes in $1000’s), and the percent correctly predict in the simulation sample is 96%.展开更多
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
文摘时域合成孔径成像算法可以更好地适应多子阵造成的方位向采样不均匀问题,并且具有存储空间小、并行处理方便的优点。但精确时域算法运算量非常大,快速分块反向传播投影(Fast Factorized Back Projection,FFBP)成像算法则可以大大降低成像计算量。详细分析了FFBP声程误差的距离效应、孔径合并策略和图像分裂策略、成像的计算量等关键问题,并给出了仿真和实测数据成像结果。通过对仿真和实测成像结果的分析表明:FFBP算法可以提高计算效率,适用于实时合成孔径声纳成像系统。
文摘This paper aims to demonstrate the importance and possible value of housing predictive power which provides independent real estate market forecasts on home prices by using data mining tasks. A (FFBP) network model and (CFBP) network model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets. This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing targets” is a 1 × 506 matrix of median values of owner-occupied homes in $1000’s. The result in this paper concludes that which one of the two networks appears to be a better indicator of the output data to target data network structure than maximizing predict. The CFBP network which is the best result from the Output_network for all samples are found from the equation output = 0.95 * Target + 1.2. The regression value is approximately 1, (R = 0.964). That means the Output_network is matching to the target data set (Median value of owner-occupied homes in $1000’s), and the percent correctly predict in the simulation sample is 96%.
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。
