Abstract
A new multi-aspect target detection method is presented based on the infinite hidden Markov model (iHMM). The scattering of waves from multiple targets is modeled as an iHMM with the number of underlying states treated as infinite, from which a full posterior distribution on the number of states associated with the targets is inferred and the target-dependent states are learned collectively. A set of Dirichlet processes (DPs) are used to define the rows of the HMM transition matrix and these DPs are linked and shared via a hierarchical Dirichlet process (HDP). Learning and inference for the iHMM are based on an effective Gibbs sampler. The framework is demonstrated using measured acoustic scattering data.
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