UWB-based 3D Localization using Least Squares Trilateration with Combination of Median Filter and Kalman Filter Article Swipe
Muhamad Naqib Mohd Shukri
,
Ammar Zakaria
,
Ahmad Shakaff Ali Yeon
,
Syed Muhammad Mamduh Syed Zakaria
,
Latifah Munirah Kamarudin
,
R. Visvanathan
,
Muhammad Azri Ahmad Baharom
,
H Khalid
,
Ahmad Helmi M Amin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2998/1/012022
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2998/1/012022
This study investigates the implementation of Ultra-Wideband (UWB) based 3D localization using Least Squares Trilateration algorithm with using median filter and Kalman filter. Two types of experiments were conducted indoors. First, TOF measurements between a statically placed UWB tag and its surrounding anchors were collected. Secondly, TOF measurements were collected by moving the UWB tag within the environment. TOF measurements from both experiments were then pre-processed using median filter to remove outliers and Kalman filter for data smoothing before being processed by Least Squares Trilateration algorithm to obtain the UWB tag estimated position. From the first experiment, the results showed a 99.69% improvement in terms of maximum error reduction, an 84.48% improvement in average distance error reduction, and a 99.25% improvement in standard deviation error reduction across the x, y, and z axes. From the second experiment, results showed improved trajectory smoothness after filtering. However, from both experiments, the challenge of obtaining accurate estimates of the z-axis position is addressed in the results and discussion section. Future works will focus on advanced signal processing techniques and improve the calibration of sensor devices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2998/1/012022
- OA Status
- diamond
- Cited By
- 1
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409730880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409730880Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2998/1/012022Digital Object Identifier
- Title
-
UWB-based 3D Localization using Least Squares Trilateration with Combination of Median Filter and Kalman FilterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-01Full publication date if available
- Authors
-
Muhamad Naqib Mohd Shukri, Ammar Zakaria, Ahmad Shakaff Ali Yeon, Syed Muhammad Mamduh Syed Zakaria, Latifah Munirah Kamarudin, R. Visvanathan, Muhammad Azri Ahmad Baharom, H Khalid, Ahmad Helmi M AminList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2998/1/012022Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/2998/1/012022Direct OA link when available
- Concepts
-
Trilateration, Kalman filter, Extended Kalman filter, Least-squares function approximation, Mathematics, Algorithm, Filter (signal processing), Median filter, Computer science, Artificial intelligence, Statistics, Computer vision, Triangulation, Image processing, Estimator, Geometry, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 2 |
| abstract_inverted_index.terms | 105 |
| abstract_inverted_index.types | 25 |
| abstract_inverted_index.using | 12, 18, 67 |
| abstract_inverted_index.works | 168 |
| abstract_inverted_index.84.48% | 111 |
| abstract_inverted_index.99.25% | 120 |
| abstract_inverted_index.99.69% | 102 |
| abstract_inverted_index.First, | 31 |
| abstract_inverted_index.Future | 167 |
| abstract_inverted_index.Kalman | 22, 74 |
| abstract_inverted_index.across | 127 |
| abstract_inverted_index.before | 79 |
| abstract_inverted_index.filter | 20, 69, 75 |
| abstract_inverted_index.median | 19, 68 |
| abstract_inverted_index.moving | 52 |
| abstract_inverted_index.obtain | 88 |
| abstract_inverted_index.placed | 37 |
| abstract_inverted_index.remove | 71 |
| abstract_inverted_index.second | 136 |
| abstract_inverted_index.sensor | 181 |
| abstract_inverted_index.showed | 100, 139 |
| abstract_inverted_index.signal | 173 |
| abstract_inverted_index.within | 56 |
| abstract_inverted_index.z-axis | 157 |
| abstract_inverted_index.Squares | 14, 84 |
| abstract_inverted_index.anchors | 43 |
| abstract_inverted_index.average | 114 |
| abstract_inverted_index.between | 34 |
| abstract_inverted_index.filter. | 23 |
| abstract_inverted_index.improve | 177 |
| abstract_inverted_index.maximum | 107 |
| abstract_inverted_index.results | 99, 138, 163 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 145 |
| abstract_inverted_index.accurate | 153 |
| abstract_inverted_index.advanced | 172 |
| abstract_inverted_index.devices. | 182 |
| abstract_inverted_index.distance | 115 |
| abstract_inverted_index.improved | 140 |
| abstract_inverted_index.indoors. | 30 |
| abstract_inverted_index.outliers | 72 |
| abstract_inverted_index.position | 158 |
| abstract_inverted_index.section. | 166 |
| abstract_inverted_index.standard | 123 |
| abstract_inverted_index.Secondly, | 46 |
| abstract_inverted_index.addressed | 160 |
| abstract_inverted_index.algorithm | 16, 86 |
| abstract_inverted_index.challenge | 150 |
| abstract_inverted_index.collected | 50 |
| abstract_inverted_index.conducted | 29 |
| abstract_inverted_index.deviation | 124 |
| abstract_inverted_index.estimated | 92 |
| abstract_inverted_index.estimates | 154 |
| abstract_inverted_index.obtaining | 152 |
| abstract_inverted_index.position. | 93 |
| abstract_inverted_index.processed | 81 |
| abstract_inverted_index.reduction | 126 |
| abstract_inverted_index.smoothing | 78 |
| abstract_inverted_index.collected. | 45 |
| abstract_inverted_index.discussion | 165 |
| abstract_inverted_index.filtering. | 144 |
| abstract_inverted_index.processing | 174 |
| abstract_inverted_index.reduction, | 109, 117 |
| abstract_inverted_index.smoothness | 142 |
| abstract_inverted_index.statically | 36 |
| abstract_inverted_index.techniques | 175 |
| abstract_inverted_index.trajectory | 141 |
| abstract_inverted_index.calibration | 179 |
| abstract_inverted_index.experiment, | 97, 137 |
| abstract_inverted_index.experiments | 27, 63 |
| abstract_inverted_index.improvement | 103, 112, 121 |
| abstract_inverted_index.surrounding | 42 |
| abstract_inverted_index.environment. | 58 |
| abstract_inverted_index.experiments, | 148 |
| abstract_inverted_index.investigates | 3 |
| abstract_inverted_index.localization | 11 |
| abstract_inverted_index.measurements | 33, 48, 60 |
| abstract_inverted_index.Trilateration | 15, 85 |
| abstract_inverted_index.pre-processed | 66 |
| abstract_inverted_index.Ultra-Wideband | 7 |
| abstract_inverted_index.implementation | 5 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.78668567 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |