{"title":"有限离散信号的正交模式分解","authors":"Ning Li, Lezhi Li","doi":"arxiv-2409.07242","DOIUrl":null,"url":null,"abstract":"In this paper, an orthogonal mode decomposition method is proposed to\ndecompose ffnite length real signals on both the real and imaginary axes of the\ncomplex plane. The interpolation function space of ffnite length discrete\nsignal is constructed, and the relationship between the dimensionality of the\ninterpolation function space and its subspaces and the band width of the\ninterpolation function is analyzed. It is proved that the intrinsic mode is\nactually the narrow band signal whose intrinsic instantaneous frequency is\nalways positive (or always negative). Thus, the eigenmode decomposition problem\nis transformed into the orthogonal projection problem of interpolation function\nspace to its low frequency subspace or narrow band subspace. Different from the\nexisting mode decomposition methods, the orthogonal modal decomposition is a\nlocal time-frequency domain algorithm. Each operation extracts a speciffc mode.\nThe global decomposition results obtained under the precise deffnition of\neigenmodes have uniqueness and orthogonality. The computational complexity of\nthe orthogonal mode decomposition method is also much smaller than that of the\nexisting mode decomposition methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Orthogonal Mode Decomposition for Finite Discrete Signals\",\"authors\":\"Ning Li, Lezhi Li\",\"doi\":\"arxiv-2409.07242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an orthogonal mode decomposition method is proposed to\\ndecompose ffnite length real signals on both the real and imaginary axes of the\\ncomplex plane. The interpolation function space of ffnite length discrete\\nsignal is constructed, and the relationship between the dimensionality of the\\ninterpolation function space and its subspaces and the band width of the\\ninterpolation function is analyzed. It is proved that the intrinsic mode is\\nactually the narrow band signal whose intrinsic instantaneous frequency is\\nalways positive (or always negative). Thus, the eigenmode decomposition problem\\nis transformed into the orthogonal projection problem of interpolation function\\nspace to its low frequency subspace or narrow band subspace. Different from the\\nexisting mode decomposition methods, the orthogonal modal decomposition is a\\nlocal time-frequency domain algorithm. Each operation extracts a speciffc mode.\\nThe global decomposition results obtained under the precise deffnition of\\neigenmodes have uniqueness and orthogonality. The computational complexity of\\nthe orthogonal mode decomposition method is also much smaller than that of the\\nexisting mode decomposition methods.\",\"PeriodicalId\":501175,\"journal\":{\"name\":\"arXiv - EE - Systems and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Systems and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07242\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Orthogonal Mode Decomposition for Finite Discrete Signals
In this paper, an orthogonal mode decomposition method is proposed to
decompose ffnite length real signals on both the real and imaginary axes of the
complex plane. The interpolation function space of ffnite length discrete
signal is constructed, and the relationship between the dimensionality of the
interpolation function space and its subspaces and the band width of the
interpolation function is analyzed. It is proved that the intrinsic mode is
actually the narrow band signal whose intrinsic instantaneous frequency is
always positive (or always negative). Thus, the eigenmode decomposition problem
is transformed into the orthogonal projection problem of interpolation function
space to its low frequency subspace or narrow band subspace. Different from the
existing mode decomposition methods, the orthogonal modal decomposition is a
local time-frequency domain algorithm. Each operation extracts a speciffc mode.
The global decomposition results obtained under the precise deffnition of
eigenmodes have uniqueness and orthogonality. The computational complexity of
the orthogonal mode decomposition method is also much smaller than that of the
existing mode decomposition methods.