| AUV (Autonomous Underwater Vehicle) is a hot research point of ocean engineering and plays an extremely important role in both military and civilian fields. Navigation technology is the key of AUV autonomous navigation, while the characteristics of AUV such as the long working time, complex environments, fewer sources of information, high confidentiality requirement bring a great challenge to the achievement of stable and accurate navigation.Bayes filtering is a integrated navigation algorithm to fuse multiple sensor observations. The probability description of water bottom image features is robust, in the cases of the unstructured seabed topography with the lack of points, lines and lower-quality imaging data. In this paper, based on sonar system of AUV, we discuss the integrated navigation method of simultaneous localization and bottom feature detection using Bayes filtering as a core technology, with focusing on the detection of underwater features as key navigation information.The method has four main ways of getting the observation data, namely through the GPS to get the initial location information before AUV diving, through the sonar imaging technology to obtain the seabed topography feature information between adjacent pulse intervals, through the doppler velocity log (DVL) to obtain the AUV speed information, through the electronic compass and intelligent gro compass to obtain the AUV attitude information. We employ Bayes filtering to integrate these four types of observation data to achieve the purpose of high-precision navigation.At the same time, in terms of the non-Gaussian characteristics of the observed data, we investigate the AUV navigation technology based on the particle filter (PF). In addition, navigation technology for multiple AUVs is also discussed.In this paper, we perform full theoretical analysis, simulation and at lake experiment through AUV loading sonar system for the above approaches, which prove the effectiveness of the algorithms. |
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