Multivariable fuzzy inference system for fingerprinting indoor localization

Oussalah, Mourad; Alakhras, Marwan; Hussein, Mousa I.

doi:10.1016/j.fss.2014.08.001

Cited by 27 publications

(24 citation statements)

References 44 publications

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“…where k is the index of a landmark in the landmark graph, R k is the detection rule of the reference landmark l k , and R * t is the type of the detected landmark at time t; then δðR k , R * t Þ is the validity function of the detected landmark, with δ as the Dirac delta function that is denoted as Journal of Sensors landmark to time t. Then, the path validity r is the rectangle function adopted from our earlier work [14] and can be described as…”

Section: Journal Of Sensorsmentioning

confidence: 99%

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Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

2020

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Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the positioning accuracy.

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Section: Journal Of Sensorsmentioning

confidence: 99%

“…This achieves better location accuracy than Map Filtering and Pedestrian Dead Reckoning (PDR) [13]. Besides, the accuracy of the constructed radio map is compared to state-of-the-art approaches ZEE [10] and manual fingerprint map building techniques [14].…”

Section: Introductionmentioning

confidence: 99%

Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

2020

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“…Khatab et al developed a Fuzzy Least Squares Support Vector Machine-based indoor fingerprint system by using RSS. The average estimation error of the proposed system was 2.56 m [26]. Ngo et al proposed a twophase structure to improve the performance of the fingerprint algorithm.…”

Section: Id:p0195mentioning

confidence: 99%

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Erdem¹,

Tuncer²,

Doğan³

2019

IJCIS

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One of the important functions of sensor networks is that they collect data from the physical environment and transmit them to a center for processing. The location from which the collected data is obtained is crucial in many applications, such as search and rescue, disaster relief, and target tracking. In this respect, determination of the location with low-cost, scalable, and efficient algorithms is required. This study presents the implementation of a fingerprint-based location determination algorithm by using the cascade artificial neural network (ANN). A 15.6 × 13.8 m 2 implementation area, in which an anchor node is placed at each corner, is divided into grids with a 60-cm edge. The proposed algorithm consists of two phases: offline and online. In the offline phase, first a mobile device with an Xbee sensor, which is able to move sensitively and communicate with anchor nodes, is used. With this device, the implementation area is visited, and at each grid point, received signal strength indicator (RSSI) values and real distances measured from the anchor nodes are recorded in a database. The training of the cascade ANN is done using the database for both range and location determination. In the online phase, the RSSIs measured by the anchor nodes are provided as the input to the cascade ANN algorithm by means of a mobile device in any coordinate. The location of the mobile device and its distance to the anchor nodes are determined with minimum error. To show the superiority of the proposed method, the results obtained are compared with those in the literature and it has been shown that this location determination is made with a smaller error.

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“…Here, Wk-NN can be used as a matching algorithm based on stored and online RSSI values [4,20,21]. In order to determine weight of any reference point, D j is sorted in an ascending order.…”

Section: Fingerprinting Localizationmentioning

confidence: 99%

Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

Subedi

Pyun

2017

Journal of Sensors

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Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultrawideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

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Multivariable fuzzy inference system for fingerprinting indoor localization

Cited by 27 publications

References 44 publications

Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

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