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A Novel Research in Low Altitude Acoustic Target Recognition Based on HMM

A Novel Research in Low Altitude Acoustic Target Recognition Based on HMM
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Author(s): Hui Liu (National University of Defense Science and Technology, China), Wei Wang (Center for Assessment and Demonstration, Research Academy of Military Science, China) and Chuang Wen Wang (PLA 61336, China)
Copyright: 2021
Volume: 12
Issue: 2
Pages: 12
Source title: International Journal of Multimedia Data Engineering and Management (IJMDEM)
Editor(s)-in-Chief: Chengcui Zhang (University of Alabama at Birmingham, USA) and Shu-Ching Chen (Florida International University, USA)
DOI: 10.4018/IJMDEM.2021040102


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This paper introduces an improved HMM (hidden Markov model) for low altitude acoustic target recognition. To overcome the limitation of the classical CDHMM (continuous density hidden Markov model) training algorithm and the generalization ability deficiency of existing discriminative learning methods, a new discriminative training method for estimating the CDHMM in acoustic target recognition is proposed based on the principle of maximizing the minimum relative separation margin. According to the definition of the relative margin, the new training criterion can be equation as a standard constrained minimax optimization problem. Then, the optimization problem can be solved by a GPD (generalized probabilistic descent) algorithm. The experimental results show that the performance of the algorithm is significantly improved compared with the former training method, which can effectively improve the recognition ability of the acoustic target recognition system.

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