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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An Efficient V2X Based Vehicle Localization Using Single RSU and Single Receiver
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Outlier identification for TOA-based source localization in the presence of noise
Imri Enosh,Anthony J. Weiss +1 more
TL;DR: A simple efficient method for outlier detection in source localization models when additive noise is present and it is proved that despite the additive noise, perfect detection of the outliers is possible when the amplitudes of the outlier are larger than a given value.
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Distributed target localization using quantized received signal strength
TL;DR: The Maximum Likelihood of the Quantized RSS is derived and Particle Swarm Optimization is used to provide an initial estimate for the gradient algorithm, and a practical quantization threshold designer is presented for RSS data.
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An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks
TL;DR: Simulation results show that the proposed hybrid positioning approach significantly outperforms the previously considered localization solutions in WSNs, thanks to the joint process of the received signals’ power and time difference of arrival.
References
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Fundamentals of statistical signal processing: estimation theory
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
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.
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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.