Yukiko Yoshikawa, Yuto Yasuda, T. Ishii, S. Izumi, H. Kawaguchi
{"title":"超声阵列传感器在高干扰环境下的12.5 m距离测量","authors":"Yukiko Yoshikawa, Yuto Yasuda, T. Ishii, S. Izumi, H. Kawaguchi","doi":"10.1109/I2MTC50364.2021.9459822","DOIUrl":null,"url":null,"abstract":"We present a distance measurement technique in a high-interference environment using ultrasonic array sensors and a direct sequence spread spectrum. In this paper, we first explain our time-of-flight calculation method for enhancing the reflected waves from an object, followed by the results obtained using this method. Object detection in an environment with significant interference could be achieved using machine learning. The evaluation results show that the proposed method can measure the distance between the sensors and static target in the range of 4-12.5 m ± 5 cm (with 99% accuracy) and a target moving at a typical human walking speed in the range of 2–7 m ± 30 cm (with 77% accuracy). We also conducted 3D measurements that were able to detect an object at 11 m outdoors from the sensors.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"458 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"12.5-m Distance Measurement in High-Interference Environment Using Ultrasonic Array Sensors\",\"authors\":\"Yukiko Yoshikawa, Yuto Yasuda, T. Ishii, S. Izumi, H. Kawaguchi\",\"doi\":\"10.1109/I2MTC50364.2021.9459822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a distance measurement technique in a high-interference environment using ultrasonic array sensors and a direct sequence spread spectrum. In this paper, we first explain our time-of-flight calculation method for enhancing the reflected waves from an object, followed by the results obtained using this method. Object detection in an environment with significant interference could be achieved using machine learning. The evaluation results show that the proposed method can measure the distance between the sensors and static target in the range of 4-12.5 m ± 5 cm (with 99% accuracy) and a target moving at a typical human walking speed in the range of 2–7 m ± 30 cm (with 77% accuracy). We also conducted 3D measurements that were able to detect an object at 11 m outdoors from the sensors.\",\"PeriodicalId\":6772,\"journal\":{\"name\":\"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)\",\"volume\":\"458 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2MTC50364.2021.9459822\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC50364.2021.9459822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
12.5-m Distance Measurement in High-Interference Environment Using Ultrasonic Array Sensors
We present a distance measurement technique in a high-interference environment using ultrasonic array sensors and a direct sequence spread spectrum. In this paper, we first explain our time-of-flight calculation method for enhancing the reflected waves from an object, followed by the results obtained using this method. Object detection in an environment with significant interference could be achieved using machine learning. The evaluation results show that the proposed method can measure the distance between the sensors and static target in the range of 4-12.5 m ± 5 cm (with 99% accuracy) and a target moving at a typical human walking speed in the range of 2–7 m ± 30 cm (with 77% accuracy). We also conducted 3D measurements that were able to detect an object at 11 m outdoors from the sensors.