A New Learning Method for Continuous Hidden Markov Models for Subsurface Landmine Detection in Ground Penetrating Radar

作者：Zhang Xuping^*; Bolton Jeremy; Gader Paul

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3): 813-819.

DOI：10.1109/JSTARS.2014.2305981

• Gibbs sampling
• ground penetrating radar (GPR) imagery
• Hidden Markov Model (HMM)
• Markov Chain Monte Carlo (MCMC) sampling
• multivariate Gaussian mixture

A new learning algorithm based on Gibbs sampling to learn the parameters of continuous Hidden Markov Models (HMMs) with multivariate Gaussian mixtures is presented. The proposed sampling algorithm outperformed the standard expectation- maximization (EM) algorithm and a minimum classification error algorithm when applied to a synthetic data set. The proposed algorithm outperforms the state of the art when applied to landmine detection using ground penetrating radar (GPR) data.

• 出版日期2014-3