A Distributed Positioning System Based on a Predictive Fingerprinting Method Enabling Sub-Metric Precision in IEEE 802.11 Networks

Maddio, Stefano; Passafiume, Marco; Cidronali, Alessandro; Manes, G.

doi:10.1109/tmtt.2015.2496196

Cited by 31 publications

(63 citation statements)

References 43 publications

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“…The antennas operate in circular polarization, permitting a reliable link regardless of the relative orientation of the tag [19,25,26]. Furthermore, circular polarization is a strong aid in contrasting multipath impairment [27,28,29].…”

Section: Proposed Hardware Infrastructurementioning

confidence: 99%

“…In [19], each M-dimensional steering vector collected for each of the N = 4 anchor nodes (each composed by M = 7 antenna elements) was used to compose a single MxN-dimensional global steering vector, which became the input of the direct localization algorithm placed on the server (Figure 1). …”

Section: Proposed Hardware Infrastructurementioning

confidence: 99%

“…A generic Maximum-Likelihood fingerprinting algorithm is based on finding the solution to the problem in Equation (3) [19,31,37]. The

prefixC (() x, y)

function is called the pseudospectrum function or the estimator and is defined as a

R^{2} \to boldR

function in the localization domain.…”

Section: One-step Localization Algorithmmentioning

confidence: 99%

“…One way of keeping this source of estimation bias under control is exploiting the capability of each anchor to make a coarse spatial subdivision for the area of competence [19,39] (Figure 6). …”

Section: One-step Localization Algorithmmentioning

confidence: 99%

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An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks

Passafiume

Maddio

Cidronali

2017

Sensors

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Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.

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Section: Proposed Hardware Infrastructurementioning

confidence: 99%

Section: Proposed Hardware Infrastructurementioning

confidence: 99%

“…A generic Maximum-Likelihood fingerprinting algorithm is based on finding the solution to the problem in Equation (3) [19,31,37]. The

prefixC (() x, y)

function is called the pseudospectrum function or the estimator and is defined as a

R^{2} \to boldR

function in the localization domain.…”

Section: One-step Localization Algorithmmentioning

confidence: 99%

Section: One-step Localization Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks

Passafiume

Maddio

Cidronali

2017

Sensors

Self Cite

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show abstract

“…This positioning method can use the signal strength indication between the reference nodes and positioning nodes, and design algorithms for positioning. In the wireless sensor networks, according to whether measuring the distance between the nodes in the positioning process, the positioning modes are divided into two categories which are range-based [2] and range-free positioning modes [3][4][5][6][7]. This paper describes the indoor positioning method based on RSSI fingerprint database, which is range-free.…”

Section: Introductionmentioning

confidence: 99%

Advanced Indoor Positioning Using Zigbee Wireless Technology

Uradziński

Guo

Liu

et al. 2017

Wireless Pers Commun

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The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning using a new improved Zigbee network fingerprint method. This method can provide more accurate positioning by filtering algorithm for improving the fingerprint Zigbee database accuracy. In our experiment the following two steps have been completed. Firstly, we filtered out the interference data generated in the fingerprint database source data acquisition process what improved the accuracy of establishing the fingerprint database. Next, the nearest algorithm, the weighted nearest algorithm and Bayesian algorithm were used to calculate pedestrian's location, and then the results are compared and analyzed. As a result, the average error with the improved fingerprint database is less than or equal to 0.81 m in a long distance range. The accuracy of the results got much better compared with the results without filtering. A newly developed ZigBee system by us can be applied to the location based services in a bigger space inside the buildings (distances up to 40 m) or in the underground mines.

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