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
Error Source Analysis of Target Localization Based on Weighted Linear Least Squares in Wireless Acoustic Sensor Network
TL;DR: In this paper, weighted linear least squares method (WLLS) was proposed to realize target localization by dividing the localization equations and introducing new parameters, the localization problem is linearized and computational complexity is reduced.
Journal ArticleDOI
One-to-one non-linear transformation for RSS-based localization with unknown transmit power
Book ChapterDOI
Nonlinear Filtering for Emission Source Tracking Using Biased RSS Measurements
Xianqing Li,Zhansheng Duan +1 more
TL;DR: A framework to jointly estimate the dynamic source state and static sensor biases using nonlinear filters such as Extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) is proposed.
Proceedings ArticleDOI
A semidefinite programming approach for target localization in the presence of sensors location errors
TL;DR: In this work, a stationary target localization based on differential received signal strength in the presence of location errors is presented and the proposed algorithm is able to provide a more accurate estimation.
Journal ArticleDOI
Diffractive RSS Based Multinetwork Aided 3D Positioning for Distributed Massive MIMO Systems
Xiaoge Wu,Ming Jiang +1 more
TL;DR: This paper proposes to employ a deep belief network (DBN) to analyze the received signal strengths (RSS) generated by a diffraction model, where the impact from interfering persons on the targeted user equipment (UE) is considered.
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
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.