Fundamental limits and improved algorithms for linear least‐squares wireless position estimation | Zendy

Guvenc Ismail | Zendy; Gezici Sinan | Zendy; Sahinoglu Zafer | Zendy

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Fundamental limits and improved algorithms for linear least‐squares wireless position estimation

Author(s) -

Guvenc Ismail,

Gezici Sinan,

Sahinoglu Zafer

Publication year - 2010

Publication title -

wireless communications and mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.42

H-Index - 64

eISSN - 1530-8677

pISSN - 1530-8669

DOI - 10.1002/wcm.1029

Subject(s) - estimator , computer science , position (finance) , algorithm , least squares function approximation , selection (genetic algorithm) , non linear least squares , nonlinear system , estimation theory , statistics , mathematics , artificial intelligence , finance , economics , physics , quantum mechanics

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. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd.

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