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.

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