A 3-D indoor radio propagation model for WiFi and RFID (original) (raw)
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On Potentials and Limitations of a Hybrid WLAN-RFID Indoor Positioning Technique
International Journal of Navigation and Observation, Hindawi, special issue "Integrating Radio Positioning and Communications: New Synergies", Vol. 2010, 11 pages, 2010
This paper addresses the important issue of position estimation in indoor environments. Starting point of the research is positioning techniques that exploit the knowledge of power levels of RF signals from multiple 802.11 WLAN APs (Access Points). In particular, the key idea in this paper is to enhance the performance of a WLAN fingerprinting approach by coupling it to a RFIDbased procedure. WLAN and RFID technologies are synergistically used to provide a platform for a more performing positioning process, in which the very strong identification capabilities of the RFID technology allow to increase the accuracy of positioning systems via WLAN fingerprinting. The algorithm performance is assessed through general and repeatable experimental campaigns, during which the main algorithm parameters are dimensioned. The results testify both to the feasibility of the solution and to its higher accuracy (attainable at very reduced costs) compared to traditional positioning techniques.
Dynamic 3-D Indoor Radio Propagation Model and Applications with Radios from 433 MHZ to 2.4 GHz
Int'l J. of Communications, Network and System Sciences, 2012
Proliferation of indoor sensor infrastructure has created a new niche for mobile communications, yet research in indoor radio propagation still has not generated a definite model that is able to 1) precisely capture radio signatures in 3-D environments and 2) effectively apply to radios at a wide range of frequency bands. This paper first introduces the impact of wall obstructions on indoor radio propagation by experimental results through a full cycle of an indoor construction process; it then exploits a dynamic 3-D indoor radio propagation model in a two-story building using radio technologies at both 433 MHz and 2.4 GHz. Experimental measurements and evaluation results show that the proposed 3-D model generates accurate signal strength values at all data evaluation positions. Comparing the two radio technologies, this study also indicates that low frequency radios (such as 433 MHz) might not be attractive for indoor mobile computing applications because of larger experimental errors or constant absence of measurement data.
Modelling Wireless Propagation for Indoor Localization
Journal of Cyber Security and Mobility, 2016
This paper presents a ray-tracing technique to model the multi-path fading effect in indoor spaces. Random set P of points on all surfaces inside a given hypothetical indoor space are chosen. Each p i ∈ P is considered to be a point from which the transmitted signal reflects just before reaching the receiver. The received signal is the vector sum of various reflections that arrive at the receiver. The received signal strength (RSS) is then computed from the signal envelope. This technique provides RSS statistics that are similar to the models of signal propagation developed after extensive measurements in multi-path environments. In addition, this technique captures the spatial correlation of signal impairment. For example, path loss computed with this technique shows that co-moving receivers experience correlated signal fades while those moving in different spaces see un-correlated fading. The technique presented here is a low cost, first principle approach to simulate channel impairments due to multi-path effect and interference. It benefits any wireless simulation study that needs the signal-space mapping and context such as indoor localization. This randomized ray-tracing technique does not compete with or replace other, more accurate ray-tracing techniques that use either brute force or geometric optics to obtain site-specific signal-to-space mapping.
Wi-Fi signal strengths database construction for indoor positioning systems using Wi-Fi RFID
2013 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), 2013
Nowadays, fingerprinting based Wi-Fi positioning systems successfully provide location information to mobile users. Main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration. Indoor positioning system accuracy highly depends on calibration (sampling) intensity. This procedure requires huge amount of time and effort, and makes large-scale deployments of indoor positioning systems non-trivial. Newly constructed database may no longer be valid if there are any major changes in the target site. In this research we present a new approach of constructing fingerprint database. We propose a hybrid calibration procedure that combines signal sampling process with path-loss prediction algorithm. Instead of manual signal sampling, proposed method requires several Wi-Fi RFID tags to be installed in a target site. Advantage of such tag is that it can be read directly by commercial Wi-Fi access points from long distance. Several RFID tags mounted in target area will monitor the signal strength levels continuously and send scan data to the server. Whenever there are significant changes in signal levels detected, server will initiate database reconstruction procedure. Compared to existing calibration procedure our method requires only few signal samples from RFID tags to be collected and rest of the database is recovered using path-loss prediction algorithm.
Propagation Modeling for Accurate Indoor WLAN RSS-Based Localization
2010 IEEE 72nd Vehicular Technology Conference - Fall, 2010
WLAN RSS-based localization has been a hot research topic for the last years. To obtain high accuracy in the noisy wireless channel, WLAN location determination systems usually use a calibration phase, where a radio map, capturing the signal strength signatures at different locations in the area of interest, is built. The radio map construction process takes a lot of time and effort, reducing the value of WLAN localization systems. In this paper, we propose 3D ray tracing as a way for automatically generating a highly accurate radiomap. We compare this method to previously used propagation modelingbased methods like the Wall Attenuation Factor and 2D ray tracing models. We evaluate the performance of each method and its computational cost in a typical residential environment. We also examine the sensitivity of the localization accuracy to inaccurate material parameters. Our results quantify the accuracycomplexity tradeoff of the different proposed techniques with 3D ray tracing giving the best localization accuracy compared to measurements with acceptable computational requirements on a typical PC.
Realistic Indoor Radio Propagation for Sub-GHz Communication
Sensors, 2018
This research article proposes a novel ray-launching propagation loss model that is able to use an environment model that contains the real geometry. This environment model is made by applying a Simultaneous Localization and Mapping (SLAM) algorithm. As a solution to the rising demands of Internet of Things applications for indoor environments, this deterministic radio propagation loss model is able to simulate an accurate coverage map that can be used for localization applications or network optimizations. Since this propagation loss model uses a 2D environment model that was captured by a moving robot, an automated validation model is developed so that a wireless sensor network can be used for validating the propagation loss model. We validated the propagation loss model by evaluated two environment models towards the lowest Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the Mean Error (ME). Furthermore, the correlation between the number of rays and the RMSE is analyzed and the correlation between the number of reflections versus the RMSE is also analyzed. Finally, the performance of the radio propagation loss model is analyzed.
Wideband radio propagation modeling for indoor geolocation applications
IEEE Communications Magazine, 1998
A framework f o r statistical modeling of the wideband character-ABSTRACT istics o f t h e frequency-selective f a d i n g m u l t i p a t h indoor radio channel f o r geolocation applications is presented. Multipath characteristics o f the channel are divided into three classes according t o availability and the strength of the direct line of sight (DLOS) path w i t h respect t o the other paths. Statistics o f t h e error in estimating the t i m e o f arrival o f t h e DLOS p a t h i n a b u i l d i n g is related t o t h e receiver's sensitivity and dynamic range. The effects of external walls on estimating t h e location o f the DLOS path
IEEE Transactions on Mobile Computing
Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed.
Received signal strength models for WLAN and BLE-based indoor positioning in multi-floor buildings
2015 International Conference on Location and GNSS (ICL-GNSS), 2015
This paper investigates the similarities and differences of the signal strength fluctuations and positioning accuracy in indoor scenarios for three types of wireless area networks: two Wireless Local Area Networks (WLANs) at 2.4 GHz and 5 GHz frequency, respectively, and one Wireless Personal Area Network (WPAN), namely the Bluetooth Low Energy (BLE). Two pathloss models based on weighted centroids and non-negative least squares estimation are presented: one including a floor loss factor, and the other one ignoring the floor losses, and the three signal types are compared in terms of the path-loss parameters, channel fluctuations and positioning accuracy, namely the distance errors and floor detection probabilities. The comparison is done based on real-field measurement data collected from a university building in Tampere, Finland. It is shown that all these three signal types have similar shadowing variances and close path-loss parameter values, and that a path-loss model considering floor losses gives the best floor detection probability, but not necessarily the smallest distance error.
A Comparative Performance Evaluation of Indoor Geolocation Technologies
Interdisciplinary Information Sciences, 2006
As more location aware services are emerging in the market, the needs for accurate and reliable localization has increased and in response to this need a number of technologies and associated algorithms are introduced in the literature. Severe multipath fading in indoor areas, poses a challenging environment for accurate localization. In this article we provide a comprehensive overview of existing indoor localization techniques. We address the bandwidth requirement, advantages and disadvantages of received-signal-strength (RSS) and time-of-arrival (TOA) based localization algorithms. We describe a repeatable framework for comparative performance evaluation of localization algorithms. Using this framework we compare the performances of two TOA-based and two RSS-based algorithms. The TOA-based algorithms are the least square TOA (LS-TOA) and closest neighbor with TOA grid (CN-TOAG). The RSS-based algorithms are the maximum likelihood estimator and the recently introduced ray tracing assisted closest neighbor (RT-CN).