An Indoor Positioning Algorithm using Bluetooth Low Energy RSSI

Chai, Song; An, Ru; Du, Zhengzhong

doi:10.2991/amsee-16.2016.72

Cited by 38 publications

(16 citation statements)

References 8 publications

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“…A number of k neighbors with the smallest Euclidean Distance will be used to predict the coordinates and floor level of the object. The coordinates, both X and Y, is calculated using (3). Since the smaller the distance means the data is more similar, a neighbor with a smaller distance will have more weight.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…On the contrary, Indoor Positioning has not reached its maximum results and is on the focus of researchers recently. Satellite signals cannot be used for Indoor Positioning System (IPS) purposes due to too much signal attenuation in an indoor environment [2] that results in IPS with an accuracy of more than 100m [3]. IPS is very useful and has many functionalities in an environment like museum, department store, university, etc.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

Thenuardi¹,

Soewito²

2020

Adv. sci. technol. eng. syst. j.

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Indoor positioning system (IPS) has become a high demand research field to be developed and has made considerable progress in recent years. Wi-Fi fingerprinting is the most promising technique that produces an acceptable result. However, despite the large amount of research that has been done using Wi-Fi fingerprinting, only a few Wi-Fi based IPS in the market can be said to be successful. Doing the research in a controlled environment and ignore the temporal signal changes may be the cause of such scenario. A long-term dataset was built to overcome this issue, yet the distance error of the state of the art was 2.48m. Therefore, we aim to reduce the distance error by combining two positioning algorithms which are Weighted k-Nearest Neighbor (WKNN) and Long-Short Term Memory (LSTM) using ensemble learning. The result shows that our ensemble method can reduce the localization error to 1.89m and improve the performance of the IPS by 23.7% when compared to the state of the art.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

Thenuardi¹,

Soewito²

2020

Adv. sci. technol. eng. syst. j.

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“…For the NLOS component, the small-scale fading is modeled with a Rayleigh distribution, whereas, for the LOS component, it is modeled by Rician distribution. In IPS, the fluctuating RSS are filtered using many approaches such as Gaussian filter [ 80 ], moving average filter [ 12 , 81 ], and exponential averaging [ 82 ].…”

Section: Signal Measurement Principlesmentioning

confidence: 99%

“…To minimize this problem, a low-pass smoothing filter can be employed. Some of the representative smoothing filters are moving average filter [ 12 , 81 ], Kalman filter [ 110 , 111 , 112 ], Gaussian filter [ 72 , 113 ], and exponential averaging [ 114 ].…”

Section: The Problems Of Practical Ipsmentioning

confidence: 99%

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Subedi

Pyun

2020

Sensors

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In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

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“…[15], in the location with noise, there is no application filtering in the noise reduction to improve the data accuracy. Reference [16] got more accurate data by applying the Kalman filtering method. In this research, there is no output for tracking the users' move from one coordinate to another coordinate.…”

mentioning

confidence: 99%

Monitoring Human Movement in Building Using Bluetooth Low Energy

Hoa

Soewito

2018

CommIT (Communication and Information Technology) Journal

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In recent years, smartphones have become more popular. Along with the increasing number of smartphone users, more features are requested by the users which can be used to solve their daily life problems. One of the most popular applications is related to location-based detection. Almost all smartphones have a feature of Global Positioning System (GPS). It can help users to go from one place to another place without getting lost by connecting to Google Map. However, there are some limitations to the GPS. When the GPS is used inside a building, it is difficult to search where the users are located, on which floor or room they are, and how to access the room inside the building. It is because GPS cannot show the user which level or room location the users are currently at. One of the possible solutions is using an Indoor Positioning System (IPS) which can detect an object or person inside a building by producing some signals that can be received by smartphones. This research aims to develop an application to monitor the movement of humans inside a building with low cost and low energy by using Android smartphones as the medium. It can get the coordinate of location by using Bluetooth Low Energy (BLE) beacon inside the building. It can also be used to detect multiple users in the same room.

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An Indoor Positioning Algorithm using Bluetooth Low Energy RSSI

Cited by 38 publications

References 8 publications

Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Monitoring Human Movement in Building Using Bluetooth Low Energy

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