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Journal ArticleDOI

Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization

Hing Cheung So, +1 more
- 01 Aug 2011 - 
- Vol. 59, Iss: 8, pp 4035-4040
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TLDR
It is proved that the performance of the improved LLS estimator achieves Cramer-Rao lower bound at sufficiently small noise conditions and the variances of the position estimates are derived and confirmed by computer simulations.
Abstract
A conventional approach for passive source localization is to utilize signal strength measurements of the emitted source received at an array of spatially separated sensors. The received signal strength (RSS) information can be converted to distance estimates for constructing a set of circular equations, from which the target position is determined. Nevertheless, a major challenge in this approach lies in the shadow fading effect which corresponds to multiplicative measurement errors. By utilizing the mean and variance of the squared distance estimates, we devise two linear least squares (LLS) estimators for RSS-based positioning in this paper. The first one is a best linear unbiased estimator while the second is its improved version by exploiting the known relation between the parameter estimates. The variances of the position estimates are derived and confirmed by computer simulations. In particular, it is proved that the performance of the improved LLS estimator achieves Cramer-Rao lower bound at sufficiently small noise conditions.

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Citations
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Journal ArticleDOI

Error Data Analytics on RSS Range-Based Localization

TL;DR: A generalized measurement model is adopted to find optimal estimators whose estimated error is equal to the Cramer-Rao Lower Bound (CRLB), and a general expression for localization error data analytics is presented which can explain and predict the accuracy of range-based localization algorithms.
Proceedings ArticleDOI

RSS based method for sensor localization with unknown transmit power and uncertainty in path loss exponent

TL;DR: A novel RSS localization method with closed-form solution based on two-step weighted least squares estimator is proposed for the case with the unknown transmit power and uncertainty in PLE and simulations show that the proposed method has the better performance than the least square method (LS).
Journal ArticleDOI

Massive MIMO for High-Accuracy Target Localization and Tracking

TL;DR: A novel high-accuracy target localization method by using massive MIMO to provide massive signal components validated by numerical simulation results show that the proposed system can achieve decimeter accuracy for target localization and tracking.

Robust Wireless Localization in Harsh Mixed Line-of-Sight/Non-Line-of-Sight Environments

TL;DR: Two new classes of localization algorithms have been proposed to jointly estimate the positions and measurement error statistics and are applicable primarily for non-cooperative localization in wireless cellular radio networks.
Journal ArticleDOI

Efficient local optimisation‐based approach for non‐convex and non‐smooth source localisation problems

TL;DR: This study proposes a novel approach to solve the source localisation problem with noisy range measurements based on a smooth non-convex approximation of the original objective function.
References
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TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Journal ArticleDOI

Locating the nodes: cooperative localization in wireless sensor networks

TL;DR: Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements in wireless sensor networks.
Journal ArticleDOI

A simple and efficient estimator for hyperbolic location

TL;DR: An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed and is shown to attain the Cramer-Rao lower bound near the small error region.
Journal ArticleDOI

Relative location estimation in wireless sensor networks

TL;DR: This work derives CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively for sensor location estimation when sensors measure received signal strength or time-of-arrival between themselves and neighboring sensors.
Journal ArticleDOI

Least squares algorithms for time-of-arrival-based mobile location

TL;DR: It is shown that the CWLS estimator yields better performance than the LS method and achieves both the Crame/spl acute/r-Rao lower bound and the optimal circular error probability at sufficiently high signal-to-noise ratio conditions.
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