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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