An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning Article Swipe
Yong Jiu Shi
,
Wenzhong Shi
,
Xintao Liu
,
Xianjian Xiao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20154244
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20154244
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.
Related Topics
Concepts
Trilateration
Skewness
Algorithm
Computer science
Signal strength
Filter (signal processing)
Variance (accounting)
Standard deviation
Range (aeronautics)
Sample (material)
Statistics
Tracing
Stability (learning theory)
Mathematics
Engineering
Wireless
Node (physics)
Computer vision
Telecommunications
Chemistry
Aerospace engineering
Business
Chromatography
Operating system
Accounting
Structural engineering
Machine learning
Metadata
- Type
- letter
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20154244
- https://www.mdpi.com/1424-8220/20/15/4244/pdf?version=1596106293
- OA Status
- gold
- Cited By
- 43
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3045926382
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3045926382Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20154244Digital Object Identifier
- Title
-
An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based PositioningWork title
- Type
-
letterOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-30Full publication date if available
- Authors
-
Yong Jiu Shi, Wenzhong Shi, Xintao Liu, Xianjian XiaoList of authors in order
- Landing page
-
https://doi.org/10.3390/s20154244Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/15/4244/pdf?version=1596106293Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/15/4244/pdf?version=1596106293Direct OA link when available
- Concepts
-
Trilateration, Skewness, Algorithm, Computer science, Signal strength, Filter (signal processing), Variance (accounting), Standard deviation, Range (aeronautics), Sample (material), Statistics, Tracing, Stability (learning theory), Mathematics, Engineering, Wireless, Node (physics), Computer vision, Telecommunications, Chemistry, Aerospace engineering, Business, Chromatography, Operating system, Accounting, Structural engineering, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
43Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 6, 2023: 10, 2022: 6, 2021: 13Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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