A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services

Vahidnia, Mohammad H.; Malek, M. R.; Mohammadi, Nazila; Alesheikh, Ali Asghar

doi:10.1007/s11277-012-0607-5

Cited by 18 publications

(8 citation statements)

References 28 publications

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“…Bahl [41] Manual Radio Map + KNN 2.5 m Hung-Huan [70] Path Loss model + Triangulation 1.6 m Vahidnia [71] Manual Radio Map + BPM 1.4 m Sun [72] Manual Radio Map + WKNN 1.2 m DIPS [48] Path Loss Model + KNN 1.2 m AIRY Ray-tracing + KNN 0.57 m…”

Section: Methods Used Accuracymentioning

confidence: 99%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

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Section: Methods Used Accuracymentioning

confidence: 99%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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“…Space partition methods [22,23] were presented to improve the location accuracy by dividing the physical space of the target region into small areas according to the RSS features. For each divided area, corresponding SVM model was trained and applied in the location to ease the effects of variable signal.…”

Section: Support Vector Machine Based Positioning Method the Learninmentioning

confidence: 99%

Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

Shi

Zhang

et al. 2015

Mobile Information Systems

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The wide spread of the 802.11-based wireless technology brings about a good opportunity for the indoor positioning system. In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR), which consists of offline training stage and online location stage. The model that describes the relations between the position and the received signal strength (RSS) of the mobile device is established at the offline training stage by SVR, and at the online location stage the exact position is determined by this model. Due to the complex indoor environment, RSS is vulnerable and changeable. To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction model. At the online location stage,k-times continuous measurement is utilized to obtain the high quality RSS input, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation shows that the proposed method has a higher positioning accuracy compared with the probability and neutral network method, and the demand for the storage capacity and computing power is also low at the same time.

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“…KNN is also easier to apply on smartphones because KNN computing is simpler than ANN. [47], and 7 points [48] for testing to get the accuracy. Iyad did the experiment at same building with this research used 38 test points and 306 reference points to cover a half part of 3rd floor Razak Tower (eastern side) [29].…”

Section: The Effect Of Human Body Around User On Accuracymentioning

confidence: 99%

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

Firdaus

Ahmad

Sahibuddin

et al. 2020

IJIE

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WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case.

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A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services

Cited by 18 publications

References 28 publications

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

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