Improving the precision of RSSI-based low-energy localization using path loss exponent estimation

Golestani, Ali; Petreska, Neda; Wilfert, Dennis; Zimmer, Christopher

doi:10.1109/wpnc.2014.6843302

Cited by 18 publications

(11 citation statements)

References 8 publications

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“…Studies have shown that the channel fading characteristic follows a lognormal distribution. RSSI distance measurement generally uses the logarithmic distance path-loss model [ 32 , 33 , 34 ]. It is expressed as

where d is the distance between the transmitter and the receiver, and n is a path-loss parameter related to the specific wireless transmission environment.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

196

119

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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.

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where d is the distance between the transmitter and the receiver, and n is a path-loss parameter related to the specific wireless transmission environment.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

196

119

View full text Add to dashboard Cite

show abstract

“…More importantly, it is necessary to fit the path-loss exponent according to the actual environment in which RSSIs are collected [33,34]. These parameters should be calculated again when a target node moves across the boundary of two different environments [34], even be constantly updated if necessary [35]. Furthermore, the piecewise fitting and min-max method are proposed for local adaption to the real environment, 4 m and 8 m are the breakpoints, and the curvatures of different sections are noticeably different [15,34,36].…”

Section: Received Signal Strength Indicator (Rssi)-based Trilateratiomentioning

confidence: 99%

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Shi

Liu

et al. 2020

Sensors

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Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.

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“…However, there are no specific formulas to compute accurate distances owing to the impacts of real-world indoor environments and manufacturer implementations [60]. According to [57,[61][62][63][64][65] and using a simple path-loss propagation model [66], the RSSI distance measurement is given as where γ is the path loss exponent depending on the environment, d is the distance between the mobile device and a reference access point, and is a variable accounting for the variation of the mean, often referred to as shadow fading [64,65,67]. Moreover, d 0 usually is given as 1 m.…”

Section: Measuring Principlesmentioning

confidence: 99%

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Khanh

Nguyen

Pham

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social

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Improving the precision of RSSI-based low-energy localization using path loss exponent estimation

Cited by 18 publications

References 8 publications

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

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