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公开(公告)号：US08912951B2
公开(公告)日：2014-12-16
申请号：US13648091
申请日：2012-10-09
Applicant: Raytheon Company
Inventor： Timothy Campbell , Theagenis J. Abatzoglou
CPC classification number: G01S7/292 , G01S13/526
Abstract: A system and method for discrimination and identification of a target including: receiving a radar return signal including target information and clutter information; determining a two-fold forward or forward-backward data matrix from the received signal, using a multi-dimensional folding (MDF) process; computing singular values of the two-fold forward or forward-backward data matrix; using the computed singular values to determine a noise power level of the radar return signal; determining the number of scatterers in the radar return signal according to a predetermined threshold value above the noise power; estimating complex Doppler and azimuth frequencies of each scatterer from the determined number of scatterers using the MDF process; determining dispersive scatterers and non-dispersive scatterers using the estimated Doppler and azimuth complex frequencies of each scatterer; and distinguishing the target information from the clutter information, according to the determined dispersive scatterers and non-dispersive scatterers.
Abstract translation: 一种用于辨别和识别目标的系统和方法，包括：接收包括目标信息和杂波信息的雷达返回信号; 使用多维折叠（MDF）处理从接收到的信号确定二倍前向或前向数据矩阵; 计算双向前向或前向 - 后向数据矩阵的奇异值; 使用计算的奇异值来确定雷达返回信号的噪声功率电平; 根据高于噪声功率的预定阈值确定雷达返回信号中散射体的数量; 使用MDF过程从确定的散射体数量估计每个散射体的复数多普勒和方位角频率; 使用估计的每个散射体的多普勒和方位复合频率来确定色散散射体和非分散散射体; 并根据确定的散射散射体和非分散散射体将目标信息与杂波信息区分开来。
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公开(公告)号：US20140097980A1
公开(公告)日：2014-04-10
申请号：US13648091
申请日：2012-10-09
Applicant: RAYTHEON COMPANY
Inventor： Timothy Campbell , Theagenis J. Abatzoglou
IPC: G01S13/04
CPC classification number: G01S7/292 , G01S13/526
Abstract: A system and method for discrimination and identification of a target including: receiving a radar return signal including target information and clutter information; determining a two-fold forward or forward-backward data matrix from the received signal, using a multi-dimensional folding (MDF) process; computing singular values of the two-fold forward or forward-backward data matrix; using the computed singular values to determine a noise power level of the radar return signal; determining the number of scatterers in the radar return signal according to a predetermined threshold value above the noise power; estimating complex Doppler and azimuth frequencies of each scatterer from the determined number of scatterers using the MDF process; determining dispersive scatterers and non-dispersive scatterers using the estimated Doppler and azimuth complex frequencies of each scatterer; and distinguishing the target information from the clutter information, according to the determined dispersive scatterers and non-dispersive scatterers,
Abstract translation: 一种用于辨别和识别目标的系统和方法，包括：接收包括目标信息和杂波信息的雷达返回信号; 使用多维折叠（MDF）处理从接收到的信号确定二倍前向或前向数据矩阵; 计算双向前向或前向 - 后向数据矩阵的奇异值; 使用计算的奇异值来确定雷达返回信号的噪声功率电平; 根据高于噪声功率的预定阈值确定雷达返回信号中散射体的数量; 使用MDF过程从确定的散射体数量估计每个散射体的复数多普勒和方位角频率; 使用估计的每个散射体的多普勒和方位复合频率来确定色散散射体和非分散散射体; 并根据所确定的色散散射体和非分散散射体区分目标信息与杂波信息，
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公开(公告)号：US10304001B2
公开(公告)日：2019-05-28
申请号：US14817685
申请日：2015-08-04
Applicant: Raytheon Company
Inventor： Timothy Campbell , David S. Douglas , Ryan Quiller
IPC: G06N7/00
Abstract: A target estimator that properly conditions measurement variates in the case of a series of sensor measurements collected against a target, a system model that captures visible and hidden stochastic information including but not limited to target state, target identity, and sensor measurements and that marginalizes measurement failure and a dynamic mixed quadrature expression facilitating real-time implementation of the estimator are presented.
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公开(公告)号：US20170039478A1
公开(公告)日：2017-02-09
申请号：US14817685
申请日：2015-08-04
Applicant: Raytheon Company
Inventor： Timothy Campbell , David S. Douglas , Ryan Quiller
CPC classification number: G06N7/005
Abstract: A target estimator that properly conditions measurement variates in the case of a series of sensor measurements collected against a target, a system model that captures visible and hidden stochastic information including but not limited to target state, target identity, and sensor measurements and that marginalizes measurement failure and a dynamic mixed quadrature expression facilitating real-time implementation of the estimator are presented.
Abstract translation: 在针对目标收集的一系列传感器测量的情况下适当地调节测量变量的目标估计器，捕获可见和隐藏的随机信息的系统模型，包括但不限于目标状态，目标身份和传感器测量，并且边缘化测量 介绍了故障和动态混合正交表达，促进了估计器的实时实现。
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