{"title":"多天线多载波系统的低复杂度 DoA-ToA 信号估计","authors":"Chandrashekhar Rai, Debarati Sen","doi":"arxiv-2409.08650","DOIUrl":null,"url":null,"abstract":"Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is\nan stringent requirement for several wireless systems like sonar, radar,\ncommunications, and dual-function radar communication (DFRC). Due to the use of\nhigh carrier frequency and bandwidth, most of these systems are designed with\nmultiple antennae and subcarriers. Although the resolution is high in the large\narray regime, the DoA-ToA estimation accuracy of the practical on-grid\nestimation methods still suffers from estimation inaccuracy due to the spectral\nleakage effect. In this article, we propose DoA-ToA estimation methods for\nmulti-antenna multi-carrier systems with an orthogonal frequency division\nmultiplexing (OFDM) signal. In the first method, we apply discrete Fourier\ntransform (DFT) based coarse signature estimation and propose a low complexity\nmultistage fine-tuning for extreme enhancement in the estimation accuracy. The\nsecond method is based on compressed sensing, where we achieve the\nsuper-resolution by taking a 2D-overcomplete angle-delay dictionary than the\nactual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP\nmethod, we apply the low complexity 2D-OMP method on the matrix data model that\nmakes the use of CS methods practical in the context of large array regimes.\nThrough numerical simulations, we show that our proposed methods achieve the\nsimilar performance as that of the subspace-based 2D-MUSIC method with a\nsignificant reduction in computational complexity.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems\",\"authors\":\"Chandrashekhar Rai, Debarati Sen\",\"doi\":\"arxiv-2409.08650\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is\\nan stringent requirement for several wireless systems like sonar, radar,\\ncommunications, and dual-function radar communication (DFRC). Due to the use of\\nhigh carrier frequency and bandwidth, most of these systems are designed with\\nmultiple antennae and subcarriers. Although the resolution is high in the large\\narray regime, the DoA-ToA estimation accuracy of the practical on-grid\\nestimation methods still suffers from estimation inaccuracy due to the spectral\\nleakage effect. In this article, we propose DoA-ToA estimation methods for\\nmulti-antenna multi-carrier systems with an orthogonal frequency division\\nmultiplexing (OFDM) signal. In the first method, we apply discrete Fourier\\ntransform (DFT) based coarse signature estimation and propose a low complexity\\nmultistage fine-tuning for extreme enhancement in the estimation accuracy. The\\nsecond method is based on compressed sensing, where we achieve the\\nsuper-resolution by taking a 2D-overcomplete angle-delay dictionary than the\\nactual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP\\nmethod, we apply the low complexity 2D-OMP method on the matrix data model that\\nmakes the use of CS methods practical in the context of large array regimes.\\nThrough numerical simulations, we show that our proposed methods achieve the\\nsimilar performance as that of the subspace-based 2D-MUSIC method with a\\nsignificant reduction in computational complexity.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08650\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems
Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is
an stringent requirement for several wireless systems like sonar, radar,
communications, and dual-function radar communication (DFRC). Due to the use of
high carrier frequency and bandwidth, most of these systems are designed with
multiple antennae and subcarriers. Although the resolution is high in the large
array regime, the DoA-ToA estimation accuracy of the practical on-grid
estimation methods still suffers from estimation inaccuracy due to the spectral
leakage effect. In this article, we propose DoA-ToA estimation methods for
multi-antenna multi-carrier systems with an orthogonal frequency division
multiplexing (OFDM) signal. In the first method, we apply discrete Fourier
transform (DFT) based coarse signature estimation and propose a low complexity
multistage fine-tuning for extreme enhancement in the estimation accuracy. The
second method is based on compressed sensing, where we achieve the
super-resolution by taking a 2D-overcomplete angle-delay dictionary than the
actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP
method, we apply the low complexity 2D-OMP method on the matrix data model that
makes the use of CS methods practical in the context of large array regimes.
Through numerical simulations, we show that our proposed methods achieve the
similar performance as that of the subspace-based 2D-MUSIC method with a
significant reduction in computational complexity.