A Novel Fused Positioning Feature for Handling Heterogeneous Hardware Problem

Fang, Shih-Hau; Wang, Chu-Hsuan

doi:10.1109/tcomm.2015.2442989

Cited by 36 publications

(20 citation statements)

References 63 publications

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“…While the size of the database has doubled, the database establishment time is only equivalent to one sixth of the traditional grid sampling method. The fingerprint database sampled by the IN device is 3 L and the corresponding position is 3 Y . In order to verify the positioning accuracy of the three fingerprint data acquisition methods, we randomly selected 250 grids to acquire the test data, which expressed as 1 2 250 { , } T T T T  .…”

Section: Methodsmentioning

confidence: 99%

“…In the stage of fingerprint database establishment, the traditional fingerprint based indoor positioning should first divide area to be located into a plurality of grids with known positions. Then, the fingerprint data is sampled by the handheld device standing in the grid [1]- [3]. Finally, the sampled data and grid position are paired and stored in the fingerprint database.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

Duan¹,

Hu²,

Feng³

et al. 2018

Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)

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The demand of indoor positioning become more and more urgent with popularity of the smart home, smart medical, etc. In recent years, the research on indoor positioning has achieved some results, especially the method based on Wi-Fi, which is due to Wi-Fi technology is mature, Wi-Fi signal is relatively stable and has a wide range of coverage, etc. Thus, fingerprint based Wi-Fi indoor positioning is low cost, easy to acquire data and implement. However, there are still many problems in the practical application even though the theoretical research is mature. In this paper, we proposed a fast fingerprint data acquisition method based on dense sampling and interpolation aim at the problem of fingerprint database establishment, which acquire data efficiently and take into account maintenance of fingerprint database later.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

Duan¹,

Hu²,

Feng³

et al. 2018

Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)

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show abstract

“…Alternatively, the set of fingerprints can be used to create a template of the Wi-Fi signal at the reference location, using for example the average and variance of the RSSI values or selected percentiles of the RSSI values [22]. Finally, the set of access points in a fingerprint can also be extended by the pairwise difference of the RSSI values [23], after which the above approaches can be applied to the extended fingerprint.…”

Section: Methodsmentioning

confidence: 99%

“…Sometimes, the paper only shows results of experiments where each access point is received everywhere in the environment [23,24,25]. In other cases, the RSSI of access points that are not received are set to the minimal possible RSSI value [18] or cause a penalty in the comparison process [8,17].…”

Section: Methodsmentioning

confidence: 99%

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting

Berkvens

Peremans

Weyn

2016

Sensors

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Localization systems are increasingly valuable, but their location estimates are only useful when the uncertainty of the estimate is known. This uncertainty is currently calculated as the location error given a ground truth, which is then used as a static measure in sometimes very different environments. In contrast, we propose the use of the conditional entropy of a posterior probability distribution as a complementary measure of uncertainty. This measure has the advantage of being dynamic, i.e., it can be calculated during localization based on individual sensor measurements, does not require a ground truth, and can be applied to discrete localization algorithms. Furthermore, for every consistent location estimation algorithm, both the location error and the conditional entropy measures must be related, i.e., a low entropy should always correspond with a small location error, while a high entropy can correspond with either a small or large location error. We validate this relationship experimentally by calculating both measures of uncertainty in three publicly available datasets using probabilistic Wi-Fi fingerprinting with eight different implementations of the sensor model. We show that the discrepancy between these measures, i.e., many location estimates having a high location error while simultaneously having a low conditional entropy, is largest for the least realistic implementations of the probabilistic sensor model. Based on the results presented in this paper, we conclude that conditional entropy, being dynamic, complementary to location error, and applicable to both continuous and discrete localization, provides an important extra means of characterizing a localization method.

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“…Crowd-sourcing has been a useful tool for indoor localization. Because the crowdsourced data are collected from a huge number of different users conveying various mobile devices, it has potential to help solving challenging problems such as heterogeneous hardware [38] and security issues [39]. In this study, we apply crowdsourced data to learn the hidden trajectory and extract the floor plan, to compensate for the absence of true map information.…”

Section: Mapless Localizationmentioning

confidence: 99%

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Yoo

Park

2019

Applied Sciences

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This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.Wi-Fi RSSI based indoor localization [1-4] is one of the standard approaches for indoor localization. It is able to utilize the RSSI measurements received from a large number of access points (APs) that are already built in construction. However, the Wi-Fi RSSI as a function of distance between a receiver (smartphone) and a transmitter (wireless AP) is nonlinear and varying due to interference of the indoor environments such as the other radio signals, walls, and obstacles. To address this problem, many machine learning based localization methods [5-8] have been developed, which learn a pattern of the RSSI measurements corresponding locations across the interested positioning area. In addition, due to its unbiased estimation capability, it is likely to be combined with other kinds of localization, such as pedestrian localization using inertial measurement unit (IMU) [9,10], visual localization [11,12], and magnetic sensor-based localization [13,14].In particular, semisupervised learning algorithms have been recently suggested for efficient indoor localization, which reduce the human effort necessary for collecting training data [15][16][17][18][19][20]. For example, for indoor localization, a large amount of unlabeled data can be easily collected by recording only Wi-Fi RSSI measurements without requiring position labels, which can save resources for collection and calibration. By contrast, labeled training data have to be created manually. Adding a large amount of Appl. Sci. 2019, 9, 3930 2 of 21 unlabeled data in the semisupervised learning framework can prevent the decrement in the estimation accuracy that occurs when using only a small amount of labeled data.Given the advantage of semisupervised learning, this study describes (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless loc...

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A Novel Fused Positioning Feature for Handling Heterogeneous Hardware Problem

Cited by 36 publications

References 63 publications

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

Fast High-Density Fingerprint Data Acquisition Based on Dense Sampling

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

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