Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

Mazuelas, Santiago; Bahillo, Alfonso; Lorenzo, Rubén M.; Fernández, Patricia; Lago, F.A.; García, Estrella; Blas, Juan; Abril, Evaristo J.

doi:10.1109/jstsp.2009.2029191

Cited by 379 publications

(298 citation statements)

References 21 publications

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“…The lab's layout and dimensions are similar to those used in comparable research studies. 5 We have accordingly defined the layout of the experimental equipment taking into account the RSSI levels observed in section ''IEEE 802.11 parameters setting.'' Figure 3 The benchmark lab is divided into four accessible zones, identified in the figure by capital letters A, B, C, and D, and two professor offices which were intentionally non-accessible during our experimental sessions.…”

Section: Environment Definition and Experimental Setupmentioning

confidence: 99%

Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

Martínez‐Gómez

Horno

Castillo-Cara

et al. 2016

International Journal of Distributed Sensor Networks

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The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.

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Section: Environment Definition and Experimental Setupmentioning

confidence: 99%

Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

Martínez‐Gómez

Horno

Castillo-Cara

et al. 2016

International Journal of Distributed Sensor Networks

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“…A scheme of the procedure of the location algorithm can be found in the Algorithm 1. It is worth noting that the minimization problem in (7) is iteratively solved with a Levenberg-Marquardt method [7] in a distributed fashion. Both procedures are iterated until convergence, being 1 and 2 the required number of iterations to attain it.…”

Section: B Path Loss Exponent Estimation Through Real-time Rss Measumentioning

confidence: 99%

“…In order to avoid the dependency on an estimated model obtained with previous off-line measurements, the introduction of real-time estimation of the propagation model is proposed in [7]. In particular, the authors derived a non-cooperative positioning algorithm which dynamically estimates the propagation models that best fit the propagation environments, by means of RSS measurements only.…”

Section: Introductionmentioning

confidence: 99%

Real-time path loss and node selection for cooperative localization in wireless sensor networks

Bel

Vicario

2010

2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops

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Abstract-RSS-based localization is considered a lowcomplexity algorithm with respect to other range techniques such as TOA or AOA. The accuracy of RSS methods depends on the suitability of the propagation models used for the actual propagation conditions. In indoor environments, in particular, it is very difficult to obtain a good propagation model. For that reason, we present a cooperative location algorithm that dynamically estimates the path loss exponent by using RSS measurements. Since the energy consumption is a key point in sensor networks, we propose a node selection in order to avoid the use of all the nodes in the network for positioning purposes. Hence, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. We present results based on both computer simulations and experimentation with the Crossbow IRIS motes showing that the proposed scheme offers a good performance in terms of position accuracy.

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“…Despite the recent developments in localization with wireless sensor networks (e.g., by means of the radio signal strength (RSS), [1][2][3][4][5][6][7]) and in pedestrian tracking (e.g., urban dead-reckoning, [8,9]), indoor navigation is still considered an open problem [10][11][12][13]. Specifically, three challenging aspects can be recognized in indoor navigation: the required accuracy is typically higher than in outdoor applications, the GPS signal is typically not available and there is low reliability of the sensor measurements (e.g., inertial navigation system (INS) measurements and RSS).…”

Section: Introductionmentioning

confidence: 99%

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

et al. 2014

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This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user's point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user's motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building.

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Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

Cited by 379 publications

References 21 publications

Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

Real-time path loss and node selection for cooperative localization in wireless sensor networks

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

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