Enhancements to linear least squares localization through reference selection and ML estimation
IEEE Wireless Communications and Networking Conference, WCNC
284 - 289
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26865
Linear least squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some distance measurements. It requires selecting one of the fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. However, selection of the reference FT is commonly performed arbitrarily in the literature. In this paper, a method for selection of the reference FT is proposed, which improves the location accuracy compared to a fixed selection of the reference FT. Moreover, a covariancematrix based LLS estimator is proposed in line of sight (LOS) and non-LOS (NLOS) environments which further improves accuracy since the correlations between the observations are exploited. Simulation results prove the effectiveness of the proposed techniques. © 2008 IEEE.
Showing items related by title, author, creator and subject.
Urfalioglu O.; Thormählen, T.; Broszio H.; Mikulastik P.; Cetin, A.E. (2011)In general, feature points and camera parameters can only be estimated with limited accuracy due to noisy images. In case of collinear feature points, it is possible to benefit from this geometrical regularity by correcting ...
Gürler Ü.; Kvam P. (2011)In most reliability studies involving censoring, one assumes that censoring probabilities are unknown. We derive a nonparametric estimator for the survival function when information regarding censoring frequency is available. ...
Gursoy, M.C.; Gezici, S. (2011)Cognitive radio transmissions in the presence of channel uncertainty are considered. In practical scenarios, cognitive secondary users need to perform both channel sensing in order to identify whether the channel is being ...