An Analysis of Wi-Fi Based Indoor Positioning Accuracy

Jēkabsons, Gints; Kairish, Vadim; Zuravlyov, Vadim

doi:10.2478/v10143-011-0031-4

Cited by 59 publications

(46 citation statements)

References 5 publications

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“…The results showed that this parameter has rather high fluctuations and is quite difficult to assess. One of the areas of research that utilizes the evaluation of signal strength at the input of receiver is the widely used identification methods for the estimation of device positioning in the network coverage area [5][6][7]. The main informative parameter in these studies is also the RSSI parameter while the attenuation in free space and additional beacons to reduce the fluctuations of signal are applied in order to assess location of the devices.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a spatial method for the estimation of signal strenth at the input of the 802.11 standard receiver

Mykhalevskiy

2017

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a spatial method for the estimation of signal strenth at the input of the 802.11 standard receiver

Mykhalevskiy

2017

EEJET

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“…A set of particles is defined as a set of hypotheses of the robot position and orientation denoted at time t as S r (t) = (x [1] r (t), x [2] r (t), · · · , x [M] r (t)) , where [M] is the number of particles. First, it updates the particles representing the position and orientation of the robot by using the motion model, which is given by Eq.…”

Section: Positioning Algorithmmentioning

confidence: 99%

“…Although the hardware required to support WiFi [1,2] and Bluetooth [3,4] is easily integrated into mobile devices, both WiFi-and Bluetooth-based positioning systems are easily disturbed because interference by other signals affects their accuracy. Ultrasonic [5,6] and LRF [7,8] positioning systems have the advantage of high positioning accuracy and a relatively simple system structure.…”

Section: Introductionmentioning

confidence: 99%

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

Wang

Takahashi

2018

Robomech J

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This paper proposes a low-cost infrared (IR) LED system with a novel arrangement that enables the self-localization of an indoor mobile robot. The proposed system uses only low-cost IR LED emitters installed on the ceiling of a building and low-cost IR receivers, each equipped with a photodiode, located on top of the mobile robot for detection. The IR LEDs, which are driven by an on-off keying modulator with various frequencies, are used as active landmarks. The mobile robot localizes itself based on the IR LED signals it receives. Although it would be desirable to assign a unique ID to each emitter, this would be difficult to realize because of the limited number of available frequencies and the production cost resulting from the use of independent microcontrollers. Therefore, we designed a novel landmark configuration consisting of an IR LED array with unique ID-encoding capabilities based on a combination of different frequencies and the repeated use of each ID encoding LED to address the problem of limited frequencies. The novel landmark arrangement reduces the costs associated with production, ID encoding, and computation. A Monte-Carlo localization method with a belief function is utilized to estimate the position and orientation of the mobile robot based on the decoded ID information and received signal strength. The proposed system was tested in a real environment and the experimental results confirmed the validity and accuracy of the system and method.

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“…A common indoor positioning strategy uses the pattern of the received signal strengths (RSS) of available Wi-Fi access points (APs) to calculate the current position of a Wi-Fi receiver. The strategies are able to achieve an accuracy under 10 m (Jekabsons, 2011;Zandbergen, 2009). For Wi-Fi fingerprinting which was introduced in Bahl and Padmanabhan (2000) a raster of positions is defined, where reference RSS values for each position are recorded in a trainings phase.…”

Section: Operational Principle Of the Intelligent Checkpoint (Icp) Comentioning

confidence: 99%

Indoor Smartphone Navigation Using a Combination of Wi-Fi and Inertial Navigation With Intelligent Checkpoints

Hofer

Retscher

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:For Wi-Fi positioning location fingerprinting is one of the most commonly employed localization technique. To achieve an acceptable level of positioning accuracy on the few meter level, i.e., to provide at least room resolution in buildings, such an approach is very labour consuming as it requires a high density of reference points. Thus, the novel approach developed aims at a significant reduction of workload for the training phase. The basic idea is to intelligently choose waypoints along possible users' trajectories in the indoor environment. These waypoints are termed intelligent checkpoints (iCPs) and serve as reference points for the fingerprinting localization approach. They are selected along the trajectories in such a way that they define a logical sequence with their ascending order. Thereby, the iCPs are located, for instance, at doors at entrances to buildings, rooms, along corridors, etc., or in low density along the trajectory to provide a suitable absolute user localization. Continuous positioning between these iCPs is obtained with the help of the smartphones' inertial sensors. While walking along a selected trajectory to the destination a dynamic recognition of the iCPs is performed and the drift of the inertial sensors is reduced as the iCP recognition serves as absolute position update. Conducted experiments in a multi-storey office building have shown that positioning accuracy of around 2.0 m are achievable which goes along with a reduction of workload by three quarter using this novel approach. The iCP concept and performance are presented and demonstrated in this paper.

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An Analysis of Wi-Fi Based Indoor Positioning Accuracy

Cited by 59 publications

References 5 publications

Development of a spatial method for the estimation of signal strenth at the input of the 802.11 standard receiver

Development of a spatial method for the estimation of signal strenth at the input of the 802.11 standard receiver

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

Indoor Smartphone Navigation Using a Combination of Wi-Fi and Inertial Navigation With Intelligent Checkpoints

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