Study of the performance of RSSI based Bluetooth Smart indoor positioning

Neburka, Jakub; Tlamsa, Zdenek; Benes, Vlastimil; Polák, Ladislav; Kaller, Ondrej; Bolecek, Libor; Šebesta, Jiří; Kron, Tomas

doi:10.1109/radioelek.2016.7477344

Cited by 40 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the RSSI distance method acquires the received signal strength (RSS) from the Bluetooth anchor point to determine the distance-loss model, and then estimates the user’s position through some algorithms. Neburka [ 11 ] investigated the performance of BLE indoor positioning based on RSSI in ideal and real indoor environments. Wang [ 12 ] proposed a Bluetooth-based trilateration method employing the Bluetooth transmission model and trilateration positioning, which can only estimate an approximate region of the user.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

189

119

View full text Add to dashboard Cite

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

show abstract

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

189

119

View full text Add to dashboard Cite

show abstract

“…There are two main types of Wi-Fi-based indoor positioning technologies: the received signal strength indicator (RSSI)-based ranging positioning algorithm [12][13][14] and the fingerprint-based positioning algorithm [15][16][17]. The RSSI-based ranging positioning algorithm usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model and then estimates the target position using trilateration or multilateration methods.…”

Section: Related Workmentioning

confidence: 99%

“…This method used the approximate relative positions of the robots estimated by their RSSIs to deploy the WMN. The performance of Bluetooth low-energy (BLE) RSSI-based technology was explored for an indoor positioning system in different transmission conditions [13]. Another BLE-based scheme is proposed in [14], where the higher precision needed an extra training phase for localization.…”

Section: Related Workmentioning

confidence: 99%

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Sinha

Hwang

2019

Electronics

View full text Add to dashboard Cite

The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.

show abstract

“…However, reference points must be installed for positioning algorithm to compute. Bluetooth technology has better resolution than Wi-Fi (Neburka, J. et al, 2016). As navigation area increases, multiples of Bluetooth reference points must be placed for better accuracy.…”

Section: Introductionmentioning

confidence: 99%

Iot Enabled Indoor Navigation System Design for Emergencies

Duru¹,

Karaş²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Global Navigation Satellite System is widely accepted and it has good positioning accuracy for outdoor applications. However, it does not provide sufficient positioning accuracy for inside of buildings. Signals are attenuated inside of buildings or dense high building areas because various materials between satellites and receiver create interference to the signal propagation. Thus, indoor positioning technologies have emerged to cover the problem. In this paper, indoor positioning technologies have been investigated and UWB based indoor positioning technology has been chosen between them. In addition to indoor positioning, accelerometer and Wi-Fi connection has been added to the system for mobility. The accelerometer detects emergency cases and alerts relatives of an intended person by using web service. Hereby, remote navigation system design is presented for first aid of the user. The system can detect emergencies, especially falls using the accelerometer and it can locate patients anywhere in the world. The designed system showed that it is possible to detect emergencies and transfer to the web with a 24.17cm average positioning error. Also, it is an assuring system for future remote positioning applications.

show abstract

Study of the performance of RSSI based Bluetooth Smart indoor positioning

Cited by 40 publications

References 12 publications

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Iot Enabled Indoor Navigation System Design for Emergencies

Contact Info

Product

Resources

About