{"title":"位置误差对蜂群无人机合成孔径DOA收敛的影响","authors":"Zhong Chen, Shihyuan Yeh, J. Chamberland, G. Huff","doi":"10.23919/USNC/URSI49741.2020.9321600","DOIUrl":null,"url":null,"abstract":"This paper reports on the direction-of-arrival (DOA) estimation using micro-UAV swarm-based (MUSB) arrays in the presence of location errors. The MUSB array is a virtually three-dimensional (3-D) random time-varying array reconstructed from swarming UAVs (unmanned aerial vehicle). In practice, the sensor position errors cannot be omitted and will impact the DOA estimation performance. The goal of this work is to evaluate the impact of the position errors in the proposed MUSB array and study the convergence under low snapshot conditions using the iterative Multiple Signal Classification (iterative- MUSIC) algorithm. Measurements on a thirty location test platform are provided to benchmark the performance with expectations.","PeriodicalId":443426,"journal":{"name":"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Impact of Position Errors on Synthetic Aperture DOA Convergence Based on Swarming UAV s\",\"authors\":\"Zhong Chen, Shihyuan Yeh, J. Chamberland, G. Huff\",\"doi\":\"10.23919/USNC/URSI49741.2020.9321600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reports on the direction-of-arrival (DOA) estimation using micro-UAV swarm-based (MUSB) arrays in the presence of location errors. The MUSB array is a virtually three-dimensional (3-D) random time-varying array reconstructed from swarming UAVs (unmanned aerial vehicle). In practice, the sensor position errors cannot be omitted and will impact the DOA estimation performance. The goal of this work is to evaluate the impact of the position errors in the proposed MUSB array and study the convergence under low snapshot conditions using the iterative Multiple Signal Classification (iterative- MUSIC) algorithm. Measurements on a thirty location test platform are provided to benchmark the performance with expectations.\",\"PeriodicalId\":443426,\"journal\":{\"name\":\"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/USNC/URSI49741.2020.9321600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/USNC/URSI49741.2020.9321600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Impact of Position Errors on Synthetic Aperture DOA Convergence Based on Swarming UAV s
This paper reports on the direction-of-arrival (DOA) estimation using micro-UAV swarm-based (MUSB) arrays in the presence of location errors. The MUSB array is a virtually three-dimensional (3-D) random time-varying array reconstructed from swarming UAVs (unmanned aerial vehicle). In practice, the sensor position errors cannot be omitted and will impact the DOA estimation performance. The goal of this work is to evaluate the impact of the position errors in the proposed MUSB array and study the convergence under low snapshot conditions using the iterative Multiple Signal Classification (iterative- MUSIC) algorithm. Measurements on a thirty location test platform are provided to benchmark the performance with expectations.