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An investigation of min-max method problems for RSSI-based indoor localization: Theoretical and experimental studies |
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| รหัสดีโอไอ | |
| Creator | 1. Apidet Booranawong 2. Kiattisak Sengchuai 3. Nattha Jindapetch 4. Hiroshi Saito |
| Title | An investigation of min-max method problems for RSSI-based indoor localization: Theoretical and experimental studies |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2563 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 47 |
| Journal No. | 3 |
| Page no. | 313-325 |
| Keyword | RSSI, Bounding-box, Localization, Theoretical study, Experiment |
| URL Website | https://www.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | Astudy of limitations of a min-max or a bounding-box method for received signal strength indicator (RSSI)-based indoor localization is introduced in this paper. The main goal of our study is toclearly understand how the widely used min-max method determines an unknown target position, and to investigateits significant limitations.For this purpose,weprovide both theoretical and experimental studies. The theoretical study first gives anunderstanding of min-max theoretical limitations, while anexperimental study then revealsmorelimitations. Experiments were donein an indoor environment,a laboratory room, wherewe employed an LPC2103F with a CC2500 RF moduleas a wireless node. Our resultsindicatethat the min-max method can be efficiently used toestimatean unknown target'sposition. However, such a method has limitations in severalcases. First, itproduces asignificantlyhigh estimation error when theunknown target is located outside an internal zone, the area within reference node positions. Second, fluctuationsof measured RSSI signalsin an obstacle environment is amajor problemthatproduces significantly moreestimationerrors. Various effects inthis case are detailed in the paper. Our information will beuseful to develop more efficient min-maxmethods. |
