HIDDEN MARKOV MODEL FOR JAMMER BEHAVIOR PREDICTION

    公开(公告)号:US20170300825A1

    公开(公告)日:2017-10-19

    申请号:US15359778

    申请日:2016-11-23

    CPC classification number: G06N7/005 G06N20/00 H04K3/222

    Abstract: Jammer behavior modeling utilizes two-layer hidden Markov models (HMMs) for identifying an interferer's plurality of modes and accumulating statistics on transitions between the interferer's plurality of modes for use in improved jammer characterization. The two-layer hidden Markov model characterizes jammer behavior by estimating time-varying but repetitive (mode-cycling) jammer behavior, providing estimates of future states for use by a strategy optimizer. Steps include receiving input data from an interferer; determining if models exist for describing the interferer's behavior; determining if a new model is needed; building a first layer HMM for each state of the interferer; building a second layer HMM using an output from the first layer HMM; and outputting the results from the first and second layer HMMs to a strategy optimizer to identify an interferer's plurality of modes and accumulate statistics on transitions between the interferer's plurality of modes for use in jammer mode prediction.

    Data adaptive analog to digital converter
    2.
    发明授权
    Data adaptive analog to digital converter 有权
    数据自适应模数转换器

    公开(公告)号:US08866660B2

    公开(公告)日:2014-10-21

    申请号:US13868181

    申请日:2013-04-23

    CPC classification number: H03M1/12 H03M1/186

    Abstract: A system and method for mitigating Analog to Digital (A/D) clipping is disclosed. The mean and variance of analog input data are tracked and the bits of A/D are dynamically reassigned to keep the input signal within their range. The quantization levels of A/D are dynamically re-mapped to avoid changes in sensitivity of sensor system. The method is based on random walk statistic and keeps the sensitivity of the sensor system constant. Also the system and method provides a way to mitigate A/D clipping that avoids changing the sensitivity by dynamically re-mapping the quantization levels of the A/D, keeping the sensitivity of the system constant.

    Abstract translation: 公开了一种减轻模数(A / D)限幅的系统和方法。 跟踪模拟输入数据的均值和方差,动态重新分配A / D位,使输入信号保持在其范围内。 动态重新映射A / D的量化级别,以避免传感器系统灵敏度的变化。 该方法基于随机游走统计量,保持传感器系统的灵敏度不变。 此外,系统和方法还提供了一种减轻A / D限幅的方法,通过动态重新映射A / D的量化级别,保持系统的灵敏度不变,避免了灵敏度的变化。

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