Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
ÇѱÛÁ¦¸ñ(Korean Title) |
UWB ½Ã½ºÅÛ¿¡¼ ÇÕ¼º°ö ½Å°æ¸ÁÀ» ÀÌ¿ëÇÑ °Å¸® ÃßÁ¤ |
¿µ¹®Á¦¸ñ(English Title) |
Distance Estimation Using Convolutional Neural Network in UWB Systems |
ÀúÀÚ(Author) |
³²°æ¸ð
Á¤ÅÂÀ±
Á¤¼ºÈÆ
Á¤ÀǸ²
Gyeong-Mo Nam
Tae-Yun Jung
Sunghun Jung
Eui-Rim Jeong
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 10 PP. 1290 ~ 1297 (2019. 10) |
Çѱ۳»¿ë (Korean Abstract) |
º» ³í¹®¿¡¼´Â ultra-wideband(UWB) ½Ã½ºÅÛ¿¡¼ ÇÕ¼º°ö ½Å°æ¸Á(CNN)À» ÀÌ¿ëÇÑ °Å¸® ÃßÁ¤ ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀº UWB ½ÅÈ£¸¦ ÀÌ¿ëÇÏ¿© ¼Û½Å±â¿Í ¼ö½Å±â »çÀÌÀÇ °Å¸®¸¦ ÃßÁ¤Çϱâ À§ÇÏ¿© ¼ö½Å½ÅÈ£ÀÇ Å©±â »ùÇ÷ΠÀÌ·ç¾îÁø 1Â÷¿ø º¤Å͸¦ 2Â÷¿ø Çà·Ä·Î À籸¼ºÇϸç, ÀÌ 2Â÷¿ø Çà·Ä·ÎºÎÅÍ ÇÕ¼º°ö ½Å°æ¸Á ȸ±Í¸¦ ÀÌ¿ëÇÏ¿© °Å¸®¸¦ ÃßÁ¤ÇÑ´Ù. IEEE 802.15.4a Ç¥ÁØÀÇ UWB ½Ç³» °¡½Ã¼± ä³Î¸ðµ¨À» ÀÌ¿ëÇÏ¿© ¼ö½Å½ÅÈ£¸¦ »ý¼ºÇÏ¿© ÇнÀµ¥ÀÌÅ͸¦ ¸¸µé¸ç ÇÕ¼º °ö ½Å°æ¸Á ¸ðµ¨À» ÇнÀ½ÃŲ´Ù. ¶ÇÇÑ ½ÇÁ¦ ÇÊµå ½ÃÇèÀ» ÅëÇØ ½Ç³»È¯°æ¿¡¼ÀÇ ½ÇÇè µ¥ÀÌÅ͸¦ ÀÌ¿ëÇÏ¿© °Å¸®ÃßÁ¤ ¼º´ÉÀ» È®ÀÎÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀº ±âÁ¸ÀÇ ¹®Åΰª ±â¹ÝÀÇ °Å¸® ÃßÁ¤ ±â¹ý°úÀÇ ¼º´Éºñ±³µµ ¼öÇàÇϴµ¥, °á°ú¿¡ µû¸£¸é 10m °Å¸®¿¡¼ Á¦¾È±â¹ýÀº 0.6mÀÇ Á¦°ö±Ù Æò±Õ Àڽ ¿¡·¯¸¦ º¸À̴µ¥ ±âÁ¸±â¹ýÀº 1.6m·Î ÈξÀ Å« ¿¡·¯¸¦ º¸ÀδÙ. |
¿µ¹®³»¿ë (English Abstract) |
The paper proposes a distance estimation technique for ultra-wideband (UWB) systems using convolutional neural network (CNN). To estimate the distance from the transmitter and the receiver in the proposed method, 1 dimensional vector consisted of the magnitudes of the received samples is reshaped into a 2 dimensional matrix, and by using this matrix, the distance is estimated through the CNN regressor. The received signal for CNN training is generated by the UWB channel model in the IEEE 802.15.4a, and the CNN model is trained. Next, the received signal for CNN test is generated by filed experiments in indoor environments, and the distance estimation performance is verified. The proposed technique is also compared with the existing threshold based method. According to the results, the proposed CNN based technique is superior to the conventional method and specifically, the proposed method shows 0.6 m root mean square error (RMSE) at distance 10 m while the conventional technique shows much worse 1.6 m RMSE. |
Å°¿öµå(Keyword) |
ÃÊ ±¤´ë¿ª ½Ã½ºÅÛ
°Å¸® ÃßÁ¤
ÇÕ¼º°ö ½Å°æ¸Á
À§Ä¡ ÃøÀ§
µµÂø½Ã°£ ÃßÁ¤
Ultra-wideband systems
Distance Estimation
Convolutional Neural Network
Localization
ToA estimation
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