The Adaptive Fingerprint Localization in Dynamic Environment Article Swipe
Keliu Long
,
Chongwei Zheng
,
Kun Zhang
,
Chuan Tian
,
Chong Shen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jsen.2022.3175742
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jsen.2022.3175742
Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jsen.2022.3175742
- https://ieeexplore.ieee.org/ielx7/7361/9812846/09775814.pdf
- OA Status
- bronze
- Cited By
- 15
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285191941
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285191941Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jsen.2022.3175742Digital Object Identifier
- Title
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The Adaptive Fingerprint Localization in Dynamic EnvironmentWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-17Full publication date if available
- Authors
-
Keliu Long, Chongwei Zheng, Kun Zhang, Chuan Tian, Chong ShenList of authors in order
- Landing page
-
https://doi.org/10.1109/jsen.2022.3175742Publisher landing page
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https://ieeexplore.ieee.org/ielx7/7361/9812846/09775814.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/7361/9812846/09775814.pdfDirect OA link when available
- Concepts
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Computer science, Autoencoder, Fingerprint (computing), Artificial intelligence, Convolutional neural network, Pattern recognition (psychology), Deep learning, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2313528934, https://openalex.org/W2985031189, https://openalex.org/W3158478700, https://openalex.org/W2999385526, https://openalex.org/W2769171246, https://openalex.org/W3043504588, https://openalex.org/W3126930479, https://openalex.org/W2944136374, https://openalex.org/W3126031589, https://openalex.org/W3120412245, https://openalex.org/W3107657322, https://openalex.org/W3089639240, https://openalex.org/W3033586993, https://openalex.org/W2275041219, https://openalex.org/W2999654750, https://openalex.org/W2992647966, https://openalex.org/W3150871581, https://openalex.org/W2886915282, https://openalex.org/W3128443425, https://openalex.org/W4293242672, https://openalex.org/W3035383331, https://openalex.org/W3011387517, https://openalex.org/W3047861007, https://openalex.org/W2998115938, https://openalex.org/W6683584131, https://openalex.org/W2792020330, https://openalex.org/W2992172366, https://openalex.org/W3109816597, https://openalex.org/W2547002943, https://openalex.org/W2924475988, https://openalex.org/W3138093987, https://openalex.org/W3025790845, https://openalex.org/W2977754667, https://openalex.org/W3120381956, https://openalex.org/W3128817902, https://openalex.org/W4200607307, https://openalex.org/W2980217352, https://openalex.org/W2890784233, https://openalex.org/W3164026182, https://openalex.org/W4200181196, https://openalex.org/W3011320190, https://openalex.org/W3154072325, https://openalex.org/W6804336345, https://openalex.org/W2991570458, https://openalex.org/W3132545547, https://openalex.org/W2143228105, https://openalex.org/W2111986491, https://openalex.org/W45133090, https://openalex.org/W2089695767, https://openalex.org/W2785787327, https://openalex.org/W3009009611, https://openalex.org/W3215319504, https://openalex.org/W3106381443, https://openalex.org/W4287778862, https://openalex.org/W41554520 |
| referenced_works_count | 55 |
| abstract_inverted_index.a | 59, 75 |
| abstract_inverted_index.To | 53 |
| abstract_inverted_index.an | 4 |
| abstract_inverted_index.in | 20, 149, 164, 178, 188 |
| abstract_inverted_index.is | 3, 18, 39, 81, 155 |
| abstract_inverted_index.it | 96 |
| abstract_inverted_index.of | 7, 28, 106, 109, 141, 147, 172, 185 |
| abstract_inverted_index.on | 64, 138 |
| abstract_inverted_index.so | 201 |
| abstract_inverted_index.to | 25, 41, 83, 91, 103, 133, 168 |
| abstract_inverted_index.we | 57 |
| abstract_inverted_index.GPI | 148 |
| abstract_inverted_index.and | 31, 44, 47, 51, 116, 126, 160, 200 |
| abstract_inverted_index.are | 49, 131 |
| abstract_inverted_index.but | 111 |
| abstract_inverted_index.can | 97 |
| abstract_inverted_index.due | 24 |
| abstract_inverted_index.few | 161 |
| abstract_inverted_index.its | 26 |
| abstract_inverted_index.low | 29, 192 |
| abstract_inverted_index.not | 98 |
| abstract_inverted_index.on. | 202 |
| abstract_inverted_index.pay | 100 |
| abstract_inverted_index.the | 104, 145, 153, 170, 173, 183 |
| abstract_inverted_index.(1-D | 79 |
| abstract_inverted_index.(PA) | 129 |
| abstract_inverted_index.CAE) | 80 |
| abstract_inverted_index.also | 112 |
| abstract_inverted_index.cost | 30 |
| abstract_inverted_index.easy | 194 |
| abstract_inverted_index.five | 179 |
| abstract_inverted_index.from | 86 |
| abstract_inverted_index.high | 198 |
| abstract_inverted_index.more | 101 |
| abstract_inverted_index.only | 99 |
| abstract_inverted_index.part | 6 |
| abstract_inverted_index.this | 55 |
| abstract_inverted_index.with | 157, 191 |
| abstract_inverted_index.After | 119 |
| abstract_inverted_index.Wi-Fi | 13, 35 |
| abstract_inverted_index.among | 11 |
| abstract_inverted_index.based | 14, 36, 63, 137 |
| abstract_inverted_index.daily | 165 |
| abstract_inverted_index.data; | 89 |
| abstract_inverted_index.fused | 132 |
| abstract_inverted_index.life, | 10 |
| abstract_inverted_index.model | 136, 154 |
| abstract_inverted_index.novel | 60 |
| abstract_inverted_index.that, | 120 |
| abstract_inverted_index.train | 134 |
| abstract_inverted_index.using | 69, 144 |
| abstract_inverted_index.which | 12 |
| abstract_inverted_index.(CNN), | 95 |
| abstract_inverted_index.(DANN) | 125 |
| abstract_inverted_index.(GPI). | 73 |
| abstract_inverted_index.Domain | 121 |
| abstract_inverted_index.Indoor | 0 |
| abstract_inverted_index.Neural | 93, 123 |
| abstract_inverted_index.Policy | 71 |
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| abstract_inverted_index.modern | 8 |
| abstract_inverted_index.noise. | 118 |
| abstract_inverted_index.online | 166 |
| abstract_inverted_index.reduce | 117 |
| abstract_inverted_index.stage. | 151 |
| abstract_inverted_index.system | 17, 38 |
| abstract_inverted_index.target | 142 |
| abstract_inverted_index.theory | 146 |
| abstract_inverted_index.widely | 32 |
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| abstract_inverted_index.address | 54 |
| abstract_inverted_index.dynamic | 42, 189 |
| abstract_inverted_index.extract | 84 |
| abstract_inverted_index.improve | 169 |
| abstract_inverted_index.labeled | 162 |
| abstract_inverted_index.offline | 150 |
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| abstract_inverted_index.similar | 90 |
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| abstract_inverted_index.typical | 180 |
| abstract_inverted_index.Finally, | 152 |
| abstract_inverted_index.Firstly, | 74 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.accuracy | 199 |
| abstract_inverted_index.compress | 113 |
| abstract_inverted_index.designed | 82 |
| abstract_inverted_index.features | 85 |
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| abstract_inverted_index.multiple | 65 |
| abstract_inverted_index.problem, | 56 |
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| abstract_inverted_index.transfer | 66 |
| abstract_inverted_index.updating | 48 |
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| abstract_inverted_index.Iteration | 72 |
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| abstract_inverted_index.attention | 102 |
| abstract_inverted_index.different | 107 |
| abstract_inverted_index.framework | 62 |
| abstract_inverted_index.long-term | 196 |
| abstract_inverted_index.redundant | 114 |
| abstract_inverted_index.scenarios | 181 |
| abstract_inverted_index.tendency, | 193 |
| abstract_inverted_index.unlabeled | 139, 158 |
| abstract_inverted_index.Aggressive | 128 |
| abstract_inverted_index.advantages | 27 |
| abstract_inverted_index.collection | 46 |
| abstract_inverted_index.dimensions | 108 |
| abstract_inverted_index.fine-tuned | 156 |
| abstract_inverted_index.researches | 23 |
| abstract_inverted_index.1-Dimension | 76 |
| abstract_inverted_index.Adversarial | 122 |
| abstract_inverted_index.Autoencoder | 78 |
| abstract_inverted_index.Generalized | 70 |
| abstract_inverted_index.deployment. | 33 |
| abstract_inverted_index.evaluations | 177 |
| abstract_inverted_index.fingerprint | 15, 45, 88, 140 |
| abstract_inverted_index.information | 105, 115 |
| abstract_inverted_index.intelligent | 9 |
| abstract_inverted_index.performance | 171 |
| abstract_inverted_index.positioning | 22, 61 |
| abstract_inverted_index.predictions | 167 |
| abstract_inverted_index.susceptible | 40 |
| abstract_inverted_index.environment, | 43, 190 |
| abstract_inverted_index.fingerprints | 159, 163 |
| abstract_inverted_index.localization | 1, 16, 37, 135, 174 |
| abstract_inverted_index.Convolutional | 77, 92 |
| abstract_inverted_index.effectiveness | 184 |
| abstract_inverted_index.fingerprints, | 110 |
| abstract_inverted_index.indispensable | 5 |
| abstract_inverted_index.stabilization | 197 |
| abstract_inverted_index.recalibration, | 195 |
| abstract_inverted_index.time-consuming | 50 |
| abstract_inverted_index.one-dimensional | 87 |
| abstract_inverted_index.labor-intensive. | 52 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.81751341 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |