Title :
Multitarget-multisensor ML and PHD: Some asymptotics
Author :
Braca, Paolo ; Marano, Stefano ; Matta, Vincenzo ; Willett, Peter
Author_Institution :
NATO Undersea Res. Centre, La Spezia, Italy
Abstract :
Multi-object estimation transforms unlabeled (and possible false and missing) observations to estimates that are also unlabeled. Two estimation strategies are here studied. The first one is a two-step procedure: detection of the number of objects followed by estimation of their locations. The second one appeals to Random Finite Set (RFS) theory, and is based on the Probability Hypothesis Density (PHD). This paper proves the notion that both are asymptotically efficient, thus achieving the same performance of a clairvoyant (in number of objects) scheme.
Keywords :
maximum likelihood estimation; set theory; signal processing; PHD; RFS theory; asymptotics; maximum likelihood estimation; multi-object estimation; multitarget-multisensor ML; probability hypothesis density; random finite set theory; Covariance matrix; Logic gates; Maximum likelihood detection; Maximum likelihood estimation; Sensors; Vectors; PHD; RFS; multi-object estimation; probability hypothesis density; random finite sets; unlabeled estimation;
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
