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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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First Claim