{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Deniz Gencaga", "hasEmail": "mailto:dgencaga@gmail.com"}, "description": "In adaptive signal processing, joint process estimation plays\r\nan important role in various estimation problems. It is well\r\nknown that a joint process estimator consists of two struc-\r\ntures, namely the orthogonalizer and the regression filter. In\r\nliterature, orthogonalization step is performed either by or-\r\nthogonal transformations or by linear predictors. While the\r\northogonal transformations do not preserve entropy; the\r\npredictors, such as the lattice, do preserve it. However, the\r\nsteady-state performance of such linear predictors is not as\r\ngood as those of the orthogonal transformations. Lattice\r\nfilters do not perform perfect orthogonalization when they\r\noperate as gradient-based adaptive predictors. In this work,\r\nadaptive escalator predictor is proposed to be used as the\r\northogonalizer of the joint process estimator. The proposed\r\nmethod preserves the entropy and achieves perfect orthogo-\r\nnalization at all times. Moreover it has good steady-state\r\nperformance compared to those structures utilizing gradient\r\nadaptive lattice filters.", "identifier": "DASHLINK_213", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/213/", "modified": "2025-07-17", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "On The Performance Comparison of Gradient Type Joint-Process Est"}