Indoor RSSI Trilateral Algorithm Considering Piecewise and Space-Scene

Shi, Yong; Long, Yi; Lu, Fuqiang; Xu, Zezhong; Xiao, Xuan; Shi, Shuming

doi:10.1109/smartcloud.2017.52

Cited by 7 publications

(10 citation statements)

References 1 publication

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“…Using MATLAB Fitting Tools and a similar fitting process in paper [ 14 , 15 ], Table 5 shows the that α and

are distinctly different. It is foreseeable that the distance mapping process will still make a difference using the factors.…”

Section: Positioning Improvementmentioning

confidence: 99%

“…Still, they may not effectively deal with the ever-changing dynamics of the indoor environment [ 10 ] and have left-skewed distributions [ 11 ]. The mean filter [ 12 ] is widely accepted because it has similar accuracy and anti-interference performance, but has less burden in filtering computation [ 13 , 14 ]. Besides smoothing, RSSI screening is another effective filtering method, e.g., by selecting the max N RSSIs (N = 13 is optimal) [ 13 ], and the least variance RSSIs over time [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Using MATLAB Fitting Tools and a similar fitting process in paper [ 14 , 15 ], Table 5 shows the that α and

are distinctly different. It is foreseeable that the distance mapping process will still make a difference using the factors.…”

Section: Positioning Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shi

Liu

et al. 2020

Sensors

Self Cite

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“…Reference [5] discovers the length inconsistency between fingerprints collected in real-time and those in the library and attributes the disparity to the interference of indoor environment and the power attenuation or damage of sensors. To overcome the problem, References [6,7] filter the fingerprint length via piecewise fitting and prove the validity of the filtering method.…”

Section: =1mentioning

confidence: 99%

“…This paper aims to reduce the dimensionality in fingerprint algorithm and achieve the optimal positioning accuracy at the minimal cost. For these purposes, the fingerprint positioning method was discussed in details considering piecewise filtering and space-scene constraints [8] and subjected to accuracy and complexity verification through the case study.…”

Section: =1mentioning

confidence: 99%

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Fingerprint positioning based on piecewise filtering of received signal strength indices and space-scene constraints

Shi¹,

Xiao²,

Lu³

et al. 2018

RCES

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This paper aims to reduce the dimensionality in fingerprint algorithm and achieve the optimal positioning accuracy at the minimal cost. For these purposes, the piecewise feature of iBeacon signal transmission was taken as the filtering factor of fingerprint positioning and adopted to filter the received signal strength indices (RSSIs) collected in real time. Then, the related fingerprints were filtered into fragments for subsequent online matching. After that, the indoor space-scene was divided into passage and hall, and the relevant constraint factor and data structure were discussed for fingerprint indexing. On this basis, the author proposed a novel method to optimize fingerprint positioning considering RSSI filtering and space-scene constraints. The experiments on an office space-scene reveal that the proposed method achieved the same result as the traditional one using 88% shorter matching time. This research provides an efficient and accuracy way of fingerprint positioning.

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Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

Wang¹,

Liang²,

Li³

et al. 2022

IEEE Access

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In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original particle swarm optimization (PSO) used in visible light positioning (VLP) systems. In the proposed IPSO algorithm, an adaptive particle initialization method based on Min-Max algorithm is used to adjust the number of particles and ensure that there are always particles near the target node (TN). Moreover, a nonlinear decreasing strategy of inertia weight is designed to ensure the stability of particle velocity during the iterative process. Simulation results show that, compared with the original PSO algorithm, the averaged positioning accuracy of the proposed IPSO-Min-Max algorithm is enhanced significantly at the expense of limited time consumption. What's more, we also find that for the proposed IPSO-Min-Max algorithm the increase of particle generation spacing will reduce the positioning delay but with the penalty in positioning accuracy. Therefore, it is necessary to select an appropriate particle spacing value according to specific requirements.INDEX TERMS Visible light positioning (VLP), particle swarm optimization (PSO), received signal strength (RSS), Min-Max algorithm.

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Indoor RSSI Trilateral Algorithm Considering Piecewise and Space-Scene

Cited by 7 publications

References 1 publication

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

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

Fingerprint positioning based on piecewise filtering of received signal strength indices and space-scene constraints

Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

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