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联合SVM和HMM的水上/水下导航场景感知模型构建

Ce hui xue bao, 2023-05, Vol.52 (5), p.738-747

May 2023. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © Wanfang Data Co. Ltd. All Rights Reserved. ;ISSN: 1001-1595 ;EISSN: 1001-1595 ;DOI: 10.11947/j.AGCS.2023.20220013

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  • Title:
    联合SVM和HMM的水上/水下导航场景感知模型构建
  • Author: 朱锋 ; 罗科干 ; 陈惟杰 ; 刘万科 ; 张小红
  • Subjects: Accuracy ; Classification ; Context ; Deep water ; Markov chains ; Navigation ; Recognition ; Shallow water ; Support vector machines ; Underwater
  • Is Part Of: Ce hui xue bao, 2023-05, Vol.52 (5), p.738-747
  • Description: 导航场景感知是智能化PNT的重要特征, 更是实现多场景无缝导航定位的基础。本文聚焦水上/水下导航场景, 考虑电磁波的衰减程度差异将其细分为水上、浅水、深水3类场景, 利用支持向量机(support vector machine, SVM)进行场景分类与识别, 在此基础上, 引入隐马尔可夫模型(hidden Markov model, HMM)表达导航场景切换, 进一步提升场景识别可靠性。本文分别构建了基于结果联合(SVM-HMM1)及基于概率联合(SVM-HMM2)的水上/水下导航场景感知模型。实测分析表明, 两种模型能够实现高精度场景感知, SVM-HMM1与SVM-HMM2识别准确率分别为91.36%与95.11%;与单一的HMM和SVM模型相比, 联合模型在结果分类与识别上更为稳定, 准确率提升约为0.95%~8.46%。
  • Publisher: Beijing: Surveying and Mapping Press
  • Language: Chinese;English
  • Identifier: ISSN: 1001-1595
    EISSN: 1001-1595
    DOI: 10.11947/j.AGCS.2023.20220013
  • Source: Alma/SFX Local Collection
    ProQuest Central
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