论文信息 - Multi-task hidden Markov model for radar automatic target recognition

Multi-task hidden Markov model for radar automatic target recognition

A new radar high-resolution range profile (HRRP) target recognition method is presented via a multi-task learning truncated stick-breaking hidden Markov model (MTL TSB-HMM) with spectrogram feature of HRRP. Experimental results show this method has good recognition and rejection performance.

[1] Lei Shi,et al. Radar HRRP Statistical Recognition With Local Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning , 2011, IEEE Trans. Signal Process..

[2] Rich Caruana,et al. Multitask Learning , 1997, Machine-mediated learning.

[3] Lawrence Carin,et al. Hidden Markov Models With Stick-Breaking Priors , 2009, IEEE Transactions on Signal Processing.

[4] Tzu-Tsung Wong,et al. Generalized Dirichlet distribution in Bayesian analysis , 1998, Appl. Math. Comput..

[5] Lei Shi,et al. Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning , 2011 .

[6] Zheng Bao,et al. Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[7] Mengdao Xing,et al. Radar HRRP target recognition based on higher order spectra , 2005, IEEE Transactions on Signal Processing.

[8] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.