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In this paper, theoretical lower bounds on performance of Linear Least-Squares (LLS) position estimators are obtained, and performance differences between LLS and Nonlinear Least-Squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors.
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