2021 Fiscal Year Research-status Report
2D/3D indoor localization using multiple leaky coaxial cables over irregular wireless coverage environment.
| Project/Area Number |
20K04484
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| Research Institution | Okayama University |
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Principal Investigator |
侯 亜飛 岡山大学, 自然科学学域, 助教 (60598457)
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| Co-Investigator(Kenkyū-buntansha) |
上原 一浩 岡山大学, 自然科学研究科, 教授 (10221798)
冨里 繁 岡山大学, 自然科学研究科, 准教授 (60362951)
田野 哲 岡山大学, 自然科学研究科, 教授 (80378835)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | LCX localization / MIMO / Neural Network / RSSI prediction |
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| Outline of Annual Research Achievements |
(1) In this year, using the LCX channel model, we built an indoor scenario and generated RSSI data. Based on the data correlation analysis and prediction theory, the lower bound and upper bound of predictability of generated LCX RSSI data have been researched. Then the RSSI value ahead has been predicted using a probabilistic neural network (PNN). The similar research on indoor localization has also been realized with more accurate localization level using PNN.
(2) A new research direction of localization of multiple users over wireless coverage using LCX has been proposed, which evaluated the TOA estimation of multiple users in a simultaneous way. Furthermore, we also investigated the capacity improvement of LCX system using spatial modulation with the main idea of on/off the LCX slots.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
(1)The research in this year presented a localization method using multiple LCXs for an indoor multipath-rich environment. It improved the localization accuracy by learning the RSSI data generated from multiple LCXs. The results show the RSSI-PNN method is promising and more than 90% of the localization errors are within 1 m. Compared with the conventional TDOA method, the RSSI-PNN method has better localization performance especially in the middle area of the wireless coverage of LCXs over the indoor environment.
(2) We also evaluated a low-complexity method to realize the simultaneous indoor localization of multiple users. It can detect multiple TDoA from multiple users. However, it is still difficult to find the correct relationship between users locations and multiple TDoAs.
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| Strategy for Future Research Activity |
(1)For the final fiscal year, an efficient indoor localization method such as particle filter based interactive and iterative scheme will be proposed and evaluated. In addition, it also needs to reduce its computational complexity.
(2)Till now, all results related to indoor localization were limited to the 2-D cases. In final fiscal year, 3-D LCX channel model will be simulated for the evaluation of the proposed indoor localization method.
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| Causes of Carryover |
Due to the coronavirus situation, it is difficult to join some international conferences which saves the travel expenses. In addition, the measurement for channel modelling has not be operated which saves the lending fee of measurement devices.
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