{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Deniz Gencaga", "hasEmail": "mailto:dgencaga@gmail.com"}, "description": "In the last decade alpha-stable distributions have become a\r\nstandard model for impulsive data. Especially the linear\r\nsymmetric alpha-stable processes have found applications in\r\nvarious fields. When the process parameters are time-\r\ninvariant, various techniques are available for estimation.\r\nHowever, time-invariance is an important restriction given\r\nthat in many communications applications channels are\r\ntime-varying. For such processes, we propose a relatively\r\nnew technique, based on particle filters which obtained great\r\nsuccess in tracking applications involving non-Gaussian\r\nsignals and nonlinear systems. Since particle filtering is a\r\nsequential method, it enables us to track the time-varying\r\nautoregression coefficients of the alpha-stable processes.\r\nThe method is tested both for abruptly and slowly changing\r\nautoregressive parameters of signals, where the driving\r\nnoises are symmetric-alpha-stable processes and is observed\r\nto perform very well. Moreover, the method can easily be\r\nextended to skewed alpha-stable distributions.", "identifier": "DASHLINK_214", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/214/", "modified": "2025-07-17", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Estimation of Time-Varying Autoregressive Symmetric Alpha Stable"}