{"title":"灰色粒子滤波(GPF)在机动自主水下航行器(AUV)深度自估计中的应用","authors":"Ting Li, Dexin Zhao, Zhiping Huang, Shaojing Su","doi":"10.1109/IHMSC.2013.50","DOIUrl":null,"url":null,"abstract":"This paper presents a grey particle filter (GPF) that incorporates the grey prediction algorithm into the particle filter (PF). The GPF self-estimates the depth of maneuvering autonomous underwater vehicle (AUV) using the data measured by the depth sensor equipped in the AUV under the condition that the prior maneuvering information is unknown and the measurement noise is time-varying. The principle of the GPF is that the particles are sampled by grey prediction algorithm and the likelihood probabilities of the grey particles are calculated by wavelet transform in real time, which only uses the historical measurement without establishing prior dynamic models. Therefore, the GPF can effectively alleviate the sample degeneracy problem which is common in the multiple model particle filter (MMFP). The performance of the MMPF and GPF are both evaluated through the experimental data. The results show that GPF has the better estimation accuracy than the MMPF.","PeriodicalId":222375,"journal":{"name":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Grey Particle Filter (GPF) for Self-Estimating Depth of Maneuvering Autonomous Underwater Vehicle (AUV)\",\"authors\":\"Ting Li, Dexin Zhao, Zhiping Huang, Shaojing Su\",\"doi\":\"10.1109/IHMSC.2013.50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a grey particle filter (GPF) that incorporates the grey prediction algorithm into the particle filter (PF). The GPF self-estimates the depth of maneuvering autonomous underwater vehicle (AUV) using the data measured by the depth sensor equipped in the AUV under the condition that the prior maneuvering information is unknown and the measurement noise is time-varying. The principle of the GPF is that the particles are sampled by grey prediction algorithm and the likelihood probabilities of the grey particles are calculated by wavelet transform in real time, which only uses the historical measurement without establishing prior dynamic models. Therefore, the GPF can effectively alleviate the sample degeneracy problem which is common in the multiple model particle filter (MMFP). The performance of the MMPF and GPF are both evaluated through the experimental data. The results show that GPF has the better estimation accuracy than the MMPF.\",\"PeriodicalId\":222375,\"journal\":{\"name\":\"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2013.50\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2013.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grey Particle Filter (GPF) for Self-Estimating Depth of Maneuvering Autonomous Underwater Vehicle (AUV)
This paper presents a grey particle filter (GPF) that incorporates the grey prediction algorithm into the particle filter (PF). The GPF self-estimates the depth of maneuvering autonomous underwater vehicle (AUV) using the data measured by the depth sensor equipped in the AUV under the condition that the prior maneuvering information is unknown and the measurement noise is time-varying. The principle of the GPF is that the particles are sampled by grey prediction algorithm and the likelihood probabilities of the grey particles are calculated by wavelet transform in real time, which only uses the historical measurement without establishing prior dynamic models. Therefore, the GPF can effectively alleviate the sample degeneracy problem which is common in the multiple model particle filter (MMFP). The performance of the MMPF and GPF are both evaluated through the experimental data. The results show that GPF has the better estimation accuracy than the MMPF.