| Project/Area Number |
18K11272
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 60060:Information network-related
|
|---|
| Research Institution | Tohoku Institute of Technology |
|---|
Principal Investigator |
Kudoh Eisuke 東北工業大学, 工学部, 教授 (80344696)
|
|---|
| Project Period (FY) |
2018-04-01 – 2023-03-31
|
| Project Status |
Completed (Fiscal Year 2022)
|
| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2022: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
|
|---|
| Keywords | 位置推定 / マルチセンシング情報 / IoT / ZigBee / ZigBEE / 屋内位置推定 / センサ |
|---|
| Outline of Final Research Achievements |
Although GPS-based location estimation is widely used outdoors, receiving radio waves directly from satellites is difficult in an indoor environment, which hinders location estimation using satellites signals. Additionally, radio waves from wireless devices installed indoors are influenced by fading and shadowing, making it difficult to achieve accurate location estimation only based on received signal power. On the other hand, sensed information such as temperature, humidity, and illuminance is also location-dependent.
In this study, we investigated an indoor location estimation method that incorporates not only received signal power but also multiple items of sensed information such as temperature, humidity, and illuminance. We demonstrated that using neural networks enables more accurate location estimation compared to the minimum mean square error estimation method. Furthermore, we revealed that incorporating time information enhances the accuracy of the estimation.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究は，高精度な屋内位置推定法に関するものであり，ゲームやナビゲーションなどのアプリケーションだけでなく，倉庫や工場における物品管理など広範囲な分野への応用が期待される．また，本位置推定法はさまざまなセンシング情報を利用するものであり，IoT技術の普及，Society5.0で不可欠なサイバー空間(仮想空間)の構築にも資する技術である．さらに，位置推定アルゴリズムとして，近年注目されているニューラルネットワークを利用する機械学習も利用している．
|
