Adaptive Estimation of Signals of Opportunity

dc.creatorKassas, Zaher M.
dc.creatorGhadiok, Vaibhav
dc.date.accessioned2018-01-25T18:11:35Z
dc.date.available2018-01-25T18:11:35Z
dc.date.issued2014-04
dc.description.abstractTo exploit unknown ambient radio frequency signals of opportunity (SOPs) for positioning and navigation, one must estimate their states along with a set of parameters that characterize the stability of their oscillators. SOPs can be modeled as stochastic dynamical systems driven by process noise. The statistics of such process noise is typically unknown to the receiver wanting to exploit the SOPs for positioning and navigation. Incorrect statistical models jeopardize the estimation optimality and may cause filter divergence. This necessitates the development of adaptive filters, which provide a significant improvementover fixed filters through the filter learning process. This paper develops two such adaptive filters: an innovationbased maximum likelihood filter and an interacting multiple model filter and compares their performance and complexity. Numerical and experimental results are presented demonstrating the superiority of these filters over fixed, mismatched filters.en_US
dc.description.departmentAerospace Engineeringen_US
dc.identifierdoi:10.15781/T28C9RM4V
dc.identifier.urihttp://hdl.handle.net/2152/63213
dc.language.isoengen_US
dc.relation.ispartofRadionavigation Laboratory Conference Proceedingsen_US
dc.rights.restrictionOpenen_US
dc.subjectKassasen_US
dc.subjectGhadioken_US
dc.subjectadaptive estimationen_US
dc.titleAdaptive Estimation of Signals of Opportunityen_US
dc.typeConference proceedingsen_US

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