Parameter estimation from heterogeneous/multimodal data sets

Inbar Fijalkow (1) , Elad Heiman (2) , Hagit Messer (2)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 TAU - Tel Aviv University

Optimal parameter estimation requires simultaneous processing of all available measurements. The complexity of this task may become too large when measurements from two or more multimodal sensor networks are avaliable. In such cases, fusion of estimates obtained from each data set separately may be practical. In this paper, we derive the optimal linear combination of the possibly non-linear estimators, and propose sub-optimal weightings. We analyze the asymptotic performance gain of the first sub-optimal approach with respect to the individual optimal estimates. The theoretical results are supported by simulations.

Mots clés: Estimation theory; Fisher information matrix; multimodal sensors; heterogeneous sensor networks; Fusion

Domaines: Traitement du signal et de l'image [eess.SP]

Inbar Fijalkow, Elad Heiman, Hagit Messer. Parameter estimation from heterogeneous/multimodal data sets. IEEE Signal Processing Letters, 2016, 23 (3), pp.390-393. ⟨10.1109/LSP.2016.2523886⟩. ⟨hal-01276150⟩