Hybrid SVM and HMM based navigation context awareness models for overwater and underwater mixed scene

doi:10.11947/j.AGCS.2023.20220013

Abstract

Abstract: Navigation context awareness is not only an important feature of intelligent PNT (positioning, navigation and timing), but also the basis for realizing multi-scene seamless navigation and positioning. This paper focuses on the overwater and underwater scene, which is divided into three kinds of subdivide scenes: overwater, shallow water and deep water according to the change of GNSS signal characteristics, using support vector machine (SVM) for scene classification and recognition. On this basis, hidden Markov model (HMM) is introduced to express navigation scene switching to further improve the reliability of context awareness. This paper constructs two kinds of context awareness models based on result combination (SVM-HMM1) and probability combination (SVM-HMM2). The recognition accuracy of SVM-HMM1 and SVM-HMM2 are 91.36% and 95.11%, respectively. Compared with HMM and SVM, the combined models are more stable in result classification and recognition, and the accuracies are improved by about 0.95%~8.46%.

Key words: intelligent PNT, navigation context awareness, overwater and underwater scenes, support vector machine, hidden Markov model

CLC Number:

P228

ZHU Feng, LUO Kegan, CHEN Weijie, LIU Wanke, ZHANG Xiaohong. Hybrid SVM and HMM based navigation context awareness models for overwater and underwater mixed scene[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5): 738-747.

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