Accurate signal strength prediction based positioning for indoor WLAN systems

Narzullaev, Anvar; Park, Yongwan; Jung, Ho-Youl

doi:10.1109/plans.2008.4569989

Cited by 28 publications

(13 citation statements)

References 5 publications

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“…Since the TOA and AOA location systems involve significant changes on hardware devices and infrastructures which make these two systems difficult to be widely applied in practice, the RSS-based trilateration approach is more preferred by current work [37]. Different from the TOA systems, the distances between the APs and tracking target are calculated by the RSS propagation models.…”

Section: Trilateration and Triangulationmentioning

confidence: 99%

“…Different from the TOA systems, the distances between the APs and tracking target are calculated by the RSS propagation models. In [37], Narzullaev compared three representative models used for Wi-Fi RSS-based trilateration approach: (i) log-distance loss model which assumes that the mean of RSSs approximately decreases logarithmically with the propagation distance, (ii) multislope loss model which achieves a larger granularity of the predicted locations and requires a shorter sample collection time, and (iii) multiwall loss model which carefully takes the path loss caused by the walls and floors into account.…”

Section: Trilateration and Triangulationmentioning

confidence: 99%

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Construction of Time‐Stamped Mobility Map for Path Tracking via Smith‐Waterman Measurement Matching

Zhou

Tian

et al. 2014

Mathematical Problems in Engineering

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Path tracking in wireless and mobile environments is a fundamental technology for ubiquitous location-based services (LBSs). In particular, it is very challenging to develop highly accurate and cost-efficient tracking systems applied to the anonymous areas where the floor plans are not available for security and privacy reasons. This paper proposes a novel path tracking approach for large Wi-Fi areas based on the time-stamped unlabeled mobility map which is constructed from Smith-Waterman received signal strength (RSS) measurement matching. Instead of conventional location fingerprinting, we construct mobility map with the technique of dimension reduction from the raw measurement space into a low-dimensional embedded manifold. The feasibility of our proposed approach is verified by the real-world experiments in the HKUST campus Wi-Fi networks, sMobileNet. The experimental results prove that our approach is adaptive and capable of achieving an adequate precision level in path tracking.

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Section: Trilateration and Triangulationmentioning

confidence: 99%

Section: Trilateration and Triangulationmentioning

confidence: 99%

Construction of Time‐Stamped Mobility Map for Path Tracking via Smith‐Waterman Measurement Matching

Zhou

Tian

et al. 2014

Mathematical Problems in Engineering

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“…Ahn et al proposed an on-line noise elimination based attenuation model in [31]. In [32], Narzullaev et al studied three representative types of attenuation models, the one-slope, multi-slope, and multi-wall models. The first type is usually applied to the outdoor environment with the assumption of the log-distance path loss property.…”

Section: Related Workmentioning

confidence: 99%

Correlated received signal strength correction for radio-map based indoor Wi-Fi localization

Zhou

Zhang

Tian

et al. 2014

Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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The purpose of received signal strength (RSS) correction in radio-map based Wi-Fi localization is to obtain a set of fine-grain location-dependent RSS fingerprints, and eventually achieve the purpose of highly accurate and reliable localization. To meet this goal, the RSS correction is conducted on the raw RSS samples to eliminate the environmental noise from the radio-map. This paper shows the comprehensive analysis on the autocorrelation property of the chronological RSS samples in the same RSS sequence, and meanwhile presents the correlated RSS correction approach. Furthermore, the correlated RSS correction approach can also be integrated into the conventional radio-map based K nearest neighbor (KNN) and weighted KNN (WKNN) localization algorithms. The experimental results conducted on the real Wi-Fi RSS samples recorded in a representative indoor environment prove that the proposed correlated RSS correction approach can result in the significant improvement of accuracy over the conventional radio-map based localization.

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“…Narrow-band wireless network signals, e.g. Wi-Fi, Bluetooth, ZigBee etc., can provide ranging estimations based on either signal free space path loss model or received signal strength pattern (Evennou and Marx 2006;Narzullaev, Park, and Jung 2008). However, narrow-band signals are easily disrupted by signal attenuation and multipath, reducing the positioning accuracy (Kaemarungsi and Krishnamurthy 2012;Sen et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Detection of UWB ranging measurement quality for collaborative indoor positioning

Jing

Bonenberg

Pinchin

et al. 2015

Journal of Location Based Services

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Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determines the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence potentially achieves centimetre level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning.

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Accurate signal strength prediction based positioning for indoor WLAN systems

Cited by 28 publications

References 5 publications

Construction of Time‐Stamped Mobility Map for Path Tracking via Smith‐Waterman Measurement Matching

Construction of Time‐Stamped Mobility Map for Path Tracking via Smith‐Waterman Measurement Matching

Correlated received signal strength correction for radio-map based indoor Wi-Fi localization

Detection of UWB ranging measurement quality for collaborative indoor positioning

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