Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning
Significant attention has been paid to indoor localization using smartphones in both research and industry.However, the accuracy and robustness of localization remain challenging issues, particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2024-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00358/ |
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| _version_ | 1841531334270386176 |
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| author | Xuechen CHEN Jiaxuan YI Aixiang WANG Xiaoheng DENG |
| author_facet | Xuechen CHEN Jiaxuan YI Aixiang WANG Xiaoheng DENG |
| author_sort | Xuechen CHEN |
| collection | DOAJ |
| description | Significant attention has been paid to indoor localization using smartphones in both research and industry.However, the accuracy and robustness of localization remain challenging issues, particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning (PDR) devices in contemporary smartphones, an advanced indoor localization fusion method, anchored in the twin delayed deep deterministic policy gradient (TD3) framework, was proposed.In this approach, a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process, and a comprehensive continuous action space was introduced for the agent.To evaluate the performance of the proposed method, experiments were conducted and this approach was compared with three state-of-the-art deep Q network (DQN) based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy. |
| format | Article |
| id | doaj-art-6c74f55254b743b6a7e6e4bb1805c05d |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-6c74f55254b743b6a7e6e4bb1805c05d2025-01-15T02:51:39ZzhoChina InfoCom Media Group物联网学报2096-37502024-03-018404855296622Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learningXuechen CHENJiaxuan YIAixiang WANGXiaoheng DENGSignificant attention has been paid to indoor localization using smartphones in both research and industry.However, the accuracy and robustness of localization remain challenging issues, particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning (PDR) devices in contemporary smartphones, an advanced indoor localization fusion method, anchored in the twin delayed deep deterministic policy gradient (TD3) framework, was proposed.In this approach, a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process, and a comprehensive continuous action space was introduced for the agent.To evaluate the performance of the proposed method, experiments were conducted and this approach was compared with three state-of-the-art deep Q network (DQN) based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00358/Wi-Fipedestrian dead reckoningindoor localizationtwin delayed deep deterministic policy gradientdeep reinforcement learning |
| spellingShingle | Xuechen CHEN Jiaxuan YI Aixiang WANG Xiaoheng DENG Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning 物联网学报 Wi-Fi pedestrian dead reckoning indoor localization twin delayed deep deterministic policy gradient deep reinforcement learning |
| title | Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| title_full | Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| title_fullStr | Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| title_full_unstemmed | Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| title_short | Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| title_sort | multi data fusionaided indoor localization based on continuous action space deep reinforcement learning |
| topic | Wi-Fi pedestrian dead reckoning indoor localization twin delayed deep deterministic policy gradient deep reinforcement learning |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00358/ |
| work_keys_str_mv | AT xuechenchen multidatafusionaidedindoorlocalizationbasedoncontinuousactionspacedeepreinforcementlearning AT jiaxuanyi multidatafusionaidedindoorlocalizationbasedoncontinuousactionspacedeepreinforcementlearning AT aixiangwang multidatafusionaidedindoorlocalizationbasedoncontinuousactionspacedeepreinforcementlearning AT xiaohengdeng multidatafusionaidedindoorlocalizationbasedoncontinuousactionspacedeepreinforcementlearning |