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

fYear

2012

fDate

9-12 July 2012

Firstpage

2347

Lastpage

2353

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;

fLanguage

English

Publisher

ieee

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

Type

conf

Filename

6290590