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    • 1. 发明授权
    • Missile boost-ballistic estimator
    • 导弹加速弹道估计器
    • US07663528B1
    • 2010-02-16
    • US12051892
    • 2008-03-20
    • Kourken MalakianSabrina M. Chowdhury
    • Kourken MalakianSabrina M. Chowdhury
    • G01S7/42
    • G01S13/723
    • A hostile missile engager senses the missile and supplies kinematic data to an interceptor missile fire control processor, which predicts the target's future location with the aid of a powered/unpowered identifier. The identifier passes the kinematic data through filters having lags for powered and unpowered operation, to produce residuals and residual covariances. The probabilities of powered and unpowered motion are determined, and thresholded. If the probability of powered motion or unpowered motion exceeds its threshold, the motion is deemed known. If the target is deemed to be powered, a set of the three-dimensional kinematic features is applied to a nine-state Kalman filter to produce an optimal state. If the target missile is unpowered, a set of the three-dimensional kinematic features corresponding to the second lag is applied to a six-state Kalman filter to produce an optimal state for the unpowered motion. The optimal states control the interceptor toward the target.
    • 敌对导弹感应导弹,并向拦截弹导弹消防控制处理器提供运动学数据,该处理器借助有动力/无动力识别符来预测目标的未来位置。 标识符通过具有用于供电和无动力操作的滞后的过滤器来传递运动学数据,以产生残差和残差协方差。 确定供电和无动力运动的概率,并进行阈值化。 如果动力运动或无动力运动的概率超过其阈值,则认为该运动是已知的。 如果目标被认为是供电的,则将一组三维运动特征应用于九状态卡尔曼滤波器以产生最佳状态。 如果目标导弹没有动力,则将一组对应于第二滞后的三维运动特征应用于六态卡尔曼滤波器,以产生无动力运动的最佳状态。 最佳状态控制拦截器朝向目标。
    • 2. 发明授权
    • Probabilistic classifier
    • 概率分类器
    • US08085186B1
    • 2011-12-27
    • US12178290
    • 2008-07-23
    • Kourken MalakianChristopher J. DahmenSabrina M. Chowdhury
    • Kourken MalakianChristopher J. DahmenSabrina M. Chowdhury
    • G01S13/72
    • G01S13/723G01S7/295
    • A computer-implemented method for probabilistically classifying an occurrence of an event, a change in the state of a target, includes measuring feature data of the target simultaneously processing the measured feature data through first and second filters. The first filter is suited for a situation in which the target is in a first state and generates a first residual and a first residual covariance for the measured feature data. The second filter is suited for a situation in which the target is in a second state and generates a second residual and a second residual covariance for the feature data. By determining a probability of the occurrence of the event and the probability of the non-occurrence of the event and comparing the two probabilities with at least one threshold value the occurrence or non-occurrence of the event is determined.
    • 用于概率地分类事件的发生,目标状态的变化的计算机实现的方法包括测量目标的特征数据,同时通过第一和第二滤波器处理测量的特征数据。 第一个滤波器适用于目标处于第一状态并为测量的特征数据产生第一残差和第一残差协方差的情况。 第二滤波器适用于目标处于第二状态并为特征数据产生第二残差和第二残差协方差的情况。 通过确定事件发生的概率和不发生事件的概率,并将两个概率与至少一个阈值进行比较,确定事件的发生或不发生。
    • 3. 发明授权
    • Drop track time selection using systems approach
    • 使用系统方法拖放时间选择
    • US08035549B1
    • 2011-10-11
    • US12578241
    • 2009-10-13
    • Kourken MalakianStephen J. Salvatore
    • Kourken MalakianStephen J. Salvatore
    • G01S13/66
    • G01S13/723
    • A method for calculating a drop track time for a radar system includes receiving characteristics of the radar system as an input, determining in a computer process the characteristics of a target being tracked by the radar system, calculating in a computer process a target track drop time for the target based on the characteristics of the radar system and the target, determining in a computer process whether a value associated with the target being tracked has been updated within the target track drop time, and discontinuing the tracking of the target if a value associated with the target being tracked is not updated within the target track drop time.
    • 用于计算雷达系统的下落轨道时间的方法包括:接收雷达系统的特性作为输入,在计算机程序中确定由雷达系统跟踪的目标的特性,在计算机进程中计算目标轨道下落时间 基于雷达系统和目标的特征,针对目标,在计算机进程中确定与目标跟踪的目标相关联的值是否已经在目标轨迹下降时间内被更新,并且如果相关联的值相关联,则停止跟踪目标 跟踪的目标不在目标轨道下降时间内更新。