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Table 3 Acronyms of the estimators used in the localization simulations

From: On a unified framework for linear nuisance parameters

Notations

Data models

Estimation methods

J-BLUE-TSE-TOA, k=1

White TSE model (25)a, M=1

Joint estimation (2)

OSP-BLUE-TSE-TOA, k=1

OSP-based estimation (6) or (9)

D-BLUE-TSE-TOAb, k=1

Differential estimation (22)

D-LS-TSE-TOAb, k=1

LS estimator based on the unwhitened differential observations in (19)

J-LS-SD-TOA

Unwhitened SD-TOA model (29), M=2

LS estimator with correlated model noise

J-BLUE-SD-TOA

Whitened SD-TOA model (31b), M=2

Joint estimation (2)

OSP-BLUE-SD-TOA

OSP-based estimation (6) or (9)

D-BLUE-SD-TOA

Differential estimation (22)

J-LS-SD-TDOA

Unwhitened SD-TDOA model (34), M=1

LS estimator with correlated model noise

J-BLUE-SD-TDOA

Whitened SD-TDOA model (36b), M=1

Joint estimation (2)

OSP-BLUE-SD-TDOA

OSP-based estimation (6) or (9)

D-BLUE-SD-TDOA

Differential estimation (22)

J-LS-SD-RSS

Unwhitened SD-RSS model (40), M=1

LS estimator with correlated model noise

J-BLUE-SD-RSS

Whitened SD-RSS model (43b), M=1

Joint estimation (2)

OSP-BLUE-SD-RSS

OSP-based estimation (6) or (9)

D-LS-SD-RSSc

LS estimator based on the unwhitened differential observations in (19)

D-BLUE-SD-RSSc

Differential estimation (22)

  1. aThe J-LS-SD-TDOA is used as an initial value (i.e., k=0), which is guaranteed to be near the global solution
  2. bD-BLUE-TSE-TOA and D-LS-TSE-TOA can equivalently be considered to work with the TDOA measurements
  3. cD-LS-SD-RSS and D-BLUE-SD-RSS can equivalently be considered to work with the DRSS measurements