Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

Pivato, P.; Palopoli, Luigi; Petri, Dario

doi:10.1109/tim.2011.2134890

Cited by 173 publications

(108 citation statements)

References 22 publications

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“…Unlike Section 6.1 to Section 6.3, we fix the target node location and vary the ranging error magnitude, so we can compare their performance from a different angle. Figures 8 and 9 depict the MSE versus ranging error with the target node located at (30,40) m and (80, 20) m, respectively. It is observed that when the ranging error is relatively small, the two-step estimator closely follows the CRLB and outperforms the other estimators.…”

Section: Estimation Accuracy Versus Ranging Errormentioning

confidence: 99%

Localization algorithms for asynchronous time difference of arrival positioning systems

Dong

2017

J Wireless Com Network

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An asynchronous time difference of arrival (ATDOA) positioning system requires no time synchronization among all the anchor and target nodes, which makes it highly practical and can be easily deployed. This paper first presents an ATDOA localization model, and then primarily focuses on two new localization algorithms for the system. The first algorithm is a two-step positioning algorithm that combines semidefinite programming (SDP) with a Taylor series method to achieve global convergence as well as superior estimation accuracy, and the second algorithm is a constrained least-squares method that has the advantage of low complexity and fast convergence while maintaining good performance. In addition, a novel receiver re-selection method is presented to significantly improve estimation accuracy. In this paper, we also derive the Cramer-Rao lower bound (CRLB) of the ATDOA positioning system using a distance-dependent noise variance model, which describes a realistic indoor propagation channel.

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Section: Estimation Accuracy Versus Ranging Errormentioning

confidence: 99%

Localization algorithms for asynchronous time difference of arrival positioning systems

Dong

2017

J Wireless Com Network

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“…It can be stated that while the proposed algorithm improves the location accuracy, it requires high computational complexity due to the complex mathematical models used. In [14], the accuracy of indoor localization measurement based on a wireless sensor network is analysed using position estimation procedure. This procedure is based on the received-signal-strength measurements collected in a real indoor environment.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Localization Accuracy and Coverage of Centroid Localization Algorithm using NS-2

Naguib¹

2017

International Journal of Advanced Research in Computer and Comm

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Localization gets more interest because it is very important for many applications of a wireless sensor networks (WSNs). This paper presents three contributions according to centroid localization algorithm (CLA). The first one is improving the localization accuracy of sensor nodes using the nearest three anchors technique; this proposed algorithm is called enhanced centroid localization algorithm (ECLA). The second contribution which is called centroid localization algorithm using reference nodes (CLAR), aims to increase the percentage of localized sensor nodes (i.e. Coverage) by using reference nodes (localized sensor nodes) broadcasting their estimated position to all unknown sensor nodes (i.e. non-localized nodes). But from simulation results CLAR has higher localization error than CLA, so that the objective is to have low localization error and high coverage which is done by applying the nearest three anchors and/or reference nodes. The final proposed algorithm is called enhanced centroid localization algorithm using reference nodes (ECLAR). The work done in this paper is based on NS-2 simulation by applying different scenarios then measuring the performance of the proposed algorithms. Simulation results show that enhanced centroid localization algorithm using reference nodes (ECLAR) has a better performance (lower localization error and higher percentage of localized sensor nodes) than the conventional centroid localization algorithm (CLA).

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“…Some examples in the literature are the use of the RSS of RF 2 Wireless Communications and Mobile Computing signals [10,11], a combination of RF and ultrasonic signals [12,13], and different data fusion schemes [14,15]. In Höflinger et al [16], the authors propose an acoustic-based system using high-pitch chirps and at least 3 known-location receivers to achieve a localization error of approximately 30 cm.…”

Section: Introductionmentioning

confidence: 99%

Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals

Sánchez-Hevia

Ayllón

Gil-Pita

et al. 2017

Wireless Communications and Mobile Computing

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We propose a new acoustic self-localization and orientation estimation algorithm for smartphones networks composed of commercial off-the-shelf devices equipped with two microphones and a speaker. Each smartphone acts as an acoustic transceiver, which emits and receives acoustic signals. Node locations are found by combining estimates of the range and direction of arrival (DoA) between node pairs using a maximum likelihood (ML) estimator. A tailored optimization algorithm is proposed to simultaneously solve the DoA uncertainty problem that arises from the use of only 2 microphones per node and obtain the azimuthal orientation of each node without requiring an electronic compass.

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Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

Cited by 173 publications

References 22 publications

Localization algorithms for asynchronous time difference of arrival positioning systems

Localization algorithms for asynchronous time difference of arrival positioning systems

Enhancing Localization Accuracy and Coverage of Centroid Localization Algorithm using NS-2

Indoor Self-Localization and Orientation Estimation of Smartphones Using Acoustic Signals

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