{"title":"基于自回归模型的时延估计","authors":"M. Pallas, N. Martin, J. Martin","doi":"10.1109/ICASSP.1987.1169733","DOIUrl":null,"url":null,"abstract":"From an active underwater acoustics experiment, we intend to estimate the time delays of the multipath propagation, in the case where the time differences of arrival are not large enough to be treated by classical methods. After estimating the propagation filter transfer function, we apply the autoregressive modelization to these frequential data. We deduce the delays values from the poles locations of the AR model. Simulations of the processing are presented. Finally, the method is applied to real data.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Time delay estimation by autoregressive modelization\",\"authors\":\"M. Pallas, N. Martin, J. Martin\",\"doi\":\"10.1109/ICASSP.1987.1169733\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"From an active underwater acoustics experiment, we intend to estimate the time delays of the multipath propagation, in the case where the time differences of arrival are not large enough to be treated by classical methods. After estimating the propagation filter transfer function, we apply the autoregressive modelization to these frequential data. We deduce the delays values from the poles locations of the AR model. Simulations of the processing are presented. Finally, the method is applied to real data.\",\"PeriodicalId\":140810,\"journal\":{\"name\":\"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1987-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.1987.1169733\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1987.1169733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Time delay estimation by autoregressive modelization
From an active underwater acoustics experiment, we intend to estimate the time delays of the multipath propagation, in the case where the time differences of arrival are not large enough to be treated by classical methods. After estimating the propagation filter transfer function, we apply the autoregressive modelization to these frequential data. We deduce the delays values from the poles locations of the AR model. Simulations of the processing are presented. Finally, the method is applied to real data.