Tracking Analysis of Recursive Stochastic Algorithms
{'Code': '05010800', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'John Cozzens', 'PO_EMAI': None, 'PO_PHON': None}
This work involves the analysis and application of constant step-               size adaptive recursive algorithms.  The PI is applying the weak                convergence theory pioneered by Kushner and incorporating                       prescaling to study the tracking behavior of constant step-size                 algorithms.  These algorithms will then be applied to the problem               of spectral line enhancement.  This in turn has practical                       application to the problem of detecting harmonic signals in                     noise, which occurs in communications, signal processing,                       geophysics, military applications, etc.  In the second year, he                 plans to generalize the adaptive recursive method and extend it                 to adaptive lattice filters which have been found to be quite                   useful in many signal processing applications.
{'FirstName': 'Bhaskar', 'LastName': 'Rao', 'PI_MID_INIT': 'D', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Bhaskar D Rao', 'EmailAddress': 'brao@ucsd.edu', 'NSF_ID': '000456047', 'StartDate': '01/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-San Diego', 'CityName': 'LA JOLLA', 'ZipCode': '920930021', 'PhoneNumber': '8585344896', 'StreetAddress': '9500 GILMAN DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '50', 'CONGRESS_DISTRICT_ORG': 'CA50', 'ORG_UEI_NUM': 'UYTTZT6G9DT1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, SAN DIEGO', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '4720', 'Text': 'SIGNAL PROCESSING SYS PROGRAM'}
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