{"title":"利用相邻时频点之间的角度相关性进行单源点检测以估算 DOA","authors":"Lu Li;Maoshen Jia;Dingding Yao","doi":"10.1109/LSENS.2024.3464515","DOIUrl":null,"url":null,"abstract":"This letter proposes multisource direction-of-arrival (DOA) estimation using the correlation between angles of adjacent time– frequency (TF) points for a first-order ambisonics sensor array. For a TF point in the recorded signal, we define the adjacent TF points whose angles are close to that of this point as angle correlation points (ACPs) and then explore the relation between the probability that this point is a single-source point (SSP) and the number of ACPs. We found that there is a positive correlation between the number of ACPs and the probability that a point is an SSP. Hence, SSP detection is proposed using the angle correlation between adjacent TF points. In addition, 2-D weight kernel density estimation is designed to estimate the probability density of angles of detected SSPs. Finally, peak search is adopted for DOA estimation. Experiments in simulated and real environments show that the DOA estimation performance of the proposed method exceeds that of some existing methods.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 10","pages":"1-4"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points\",\"authors\":\"Lu Li;Maoshen Jia;Dingding Yao\",\"doi\":\"10.1109/LSENS.2024.3464515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter proposes multisource direction-of-arrival (DOA) estimation using the correlation between angles of adjacent time– frequency (TF) points for a first-order ambisonics sensor array. For a TF point in the recorded signal, we define the adjacent TF points whose angles are close to that of this point as angle correlation points (ACPs) and then explore the relation between the probability that this point is a single-source point (SSP) and the number of ACPs. We found that there is a positive correlation between the number of ACPs and the probability that a point is an SSP. Hence, SSP detection is proposed using the angle correlation between adjacent TF points. In addition, 2-D weight kernel density estimation is designed to estimate the probability density of angles of detected SSPs. Finally, peak search is adopted for DOA estimation. Experiments in simulated and real environments show that the DOA estimation performance of the proposed method exceeds that of some existing methods.\",\"PeriodicalId\":13014,\"journal\":{\"name\":\"IEEE Sensors Letters\",\"volume\":\"8 10\",\"pages\":\"1-4\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10684438/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10684438/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
本文提出利用一阶环境声学传感器阵列相邻时频(TF)点角度之间的相关性进行多源到达方向(DOA)估计。对于记录信号中的一个 TF 点,我们将角度与该点相近的相邻 TF 点定义为角度相关点(ACP),然后探讨该点为单源点(SSP)的概率与 ACP 数量之间的关系。我们发现,ACP 的数量与某点是单源点的概率之间存在正相关关系。因此,我们提出利用相邻 TF 点之间的角度相关性来检测 SSP。此外,还设计了二维权核密度估计来估计检测到的 SSP 的角度概率密度。最后,采用峰值搜索进行 DOA 估计。在模拟和真实环境中的实验表明,所提方法的 DOA 估计性能超过了一些现有方法。
Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points
This letter proposes multisource direction-of-arrival (DOA) estimation using the correlation between angles of adjacent time– frequency (TF) points for a first-order ambisonics sensor array. For a TF point in the recorded signal, we define the adjacent TF points whose angles are close to that of this point as angle correlation points (ACPs) and then explore the relation between the probability that this point is a single-source point (SSP) and the number of ACPs. We found that there is a positive correlation between the number of ACPs and the probability that a point is an SSP. Hence, SSP detection is proposed using the angle correlation between adjacent TF points. In addition, 2-D weight kernel density estimation is designed to estimate the probability density of angles of detected SSPs. Finally, peak search is adopted for DOA estimation. Experiments in simulated and real environments show that the DOA estimation performance of the proposed method exceeds that of some existing methods.