A BLE RSSI ranking based indoor positioning system for generic smartphones

Ma, Zixiang; Poslad, Stefan; Bigham, John; Zhang, Xiaoshuai; Men, Liang

doi:10.1109/wts.2017.7943542

Cited by 57 publications

(25 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method chooses the subset of RPs with shortest ED to the TP. Here, another algorithm, called RSSI ranking -Kendall Tau Correlation Coefficient (RR-KTCC) proposed by Zixiang Ma et al [2] which was used to address the issue about equipment difference in BLE positioning system, will be employed to search for the nearest points.…”

Section: B K-nearest Points Searching Algorithmmentioning

confidence: 99%

“…And n is the number of APs selected. Therefore, the positioning error can be denoted as = √( − 0 )( − 0 ) 2…”

Section: mentioning

confidence: 99%

“…As both indoor and outdoor spaces become more complex, especially in urban areas, the need for indoor positioning and navigation services [1], [2], [3] becomes paramount. There are many other value-add location-based services (LBS) applications, e.g., commercial ads, can be made more personalized if they are location-aware [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

Poslad

et al. 2018

2018 Wireless Telecommunications Symposium (WTS)

Self Cite

View full text Add to dashboard Cite

Optimally choosing wireless Access Points (APs) as urban areas become more densely packed with them becomes increasingly challenging. In WiFi-based Indoor Positioning Systems (IPS), Selecting Wireless Access Point (AP), namely, WiFi routers, is significant as the more APs that are selected, the higher the computation, energy and time cost. This is unsuitable for networking low-resource devices as part of an Internet of Things. In addition, selecting the optimum number of APs not only reduces redundant information but also improves the positioning accuracy. In this paper, we present a novel AP selection method that uses the RSSI Interval Overlap Degree (IOD) to discriminate between known location Reference Points. We validated our algorithm in an office-like indoor space at a Queen Mary computer science lab. The results show that our algorithm has an improved performance, which is 13.6%, 18.2%, and 7.6% better than IG (information gain), MI (mutual information), SD (standard deviation) used as baseline algorithms, respectively.

show abstract

Section: B K-nearest Points Searching Algorithmmentioning

confidence: 99%

“…And n is the number of APs selected. Therefore, the positioning error can be denoted as = √( − 0 )( − 0 ) 2…”

Section: mentioning

confidence: 99%

See 1 more Smart Citation

An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

Poslad

et al. 2018

2018 Wireless Telecommunications Symposium (WTS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has the advantages of low transmission power, high performance, and easy deployment, and has become a widely used positioning technology. The positioning accuracy is generally 3-5 m [7,8]. Pseudolite indoor positioning technology is a mainstream solution to seamless indoor and outdoor positioning and navigation.…”

Section: Introductionmentioning

confidence: 99%

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Huang

et al. 2020

Sensors

View full text Add to dashboard Cite

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

show abstract

“…On the other hand, Bluetooth can offer better indoor positioning accuracy than Wi-Fi AP’s [ 6 , 7 ]. The fingerprint-based indoor positioning method comprises offline training and online testing to realize localization [ 8 , 9 , 10 ], but it needs a lot of computation and some other resource consumption. IBeacon is a Bluetooth signal source.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time Sequences

Guo

et al. 2018

Sensors

View full text Add to dashboard Cite

Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target’s location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5–8.5 s for the transition between states and by approximately 24 s for the entire process.

show abstract

A BLE RSSI ranking based indoor positioning system for generic smartphones

Cited by 57 publications

References 18 publications

An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time Sequences

Contact Info

Product

Resources

About