Journal ArticleDOI
Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization
Hing Cheung So,Lanxin Lin +1 more
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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.read more
Citations
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Journal ArticleDOI
Localization of Partial Discharge Using All-Fiber System Based on Crystal Fluorescent Fiber
TL;DR: In this article , a localization algorithm of received signal strength (RSS) using demodulating PD optical signal is proposed, which is based on the optical effects caused by PD, which influence the optical signal that is transmitted through an optical fiber sensor placed inside the equipment.
Journal ArticleDOI
Semidefinite Programming Algorithms for 3-D AOA-Based Hybrid Localization
TL;DR: In this article , a unified solution based on semidefinite programming (SDP) is developed for hybrid AOA/TOA, hybrid TDOA/TDOA, and hybrid RSS localization problems.
Dissertation
Blind Received Signal Strength Difference Based Source Localization with System Parameter Error and Sensor Position Uncertainty
TL;DR: Three received signal strength difference (RSSD) based methods are presented to localize a source with unknown transmit power and an efficient two stage estimator is proposed here.
Proceedings ArticleDOI
Accurate Distance Estimation for RSS Localization With Statistical Path Loss Exponent Model
TL;DR: In this article, a uniform search root finder (USRF) method is proposed to solve the original non-linear function without ignoring any of the underlying terms, which not only provides much more accurate ratio-of-distance estimates than the state of the art, but also provides significantly better localization accuracy.
Proceedings ArticleDOI
Tracking Micro Aerial Vehicles with High Maneuverability and Sudden Rotation in Complex Indoor Environments Using Deep Neural Networks in Three Dimensions based on the Received Signal Strength
TL;DR: A deep neural network-based method for tracking micro aerial vehicles in a complex indoor environment using a wireless sensor network based on the Received Signal Strength method without requiring to additional hardware is introduced.
References
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Journal ArticleDOI
Locating the nodes: cooperative localization in wireless sensor networks
Neal Patwari,Joshua N. Ash,Spyros Kyperountas,Alfred O. Hero,Randolph L. Moses,Neiyer S. Correal,Neiyer S. Correal +6 more
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
Yiu-Tong Chan,K.C. Ho +1 more
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.