Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Chenghong Zhan"'
Publikováno v:
IEEE Access, Vol 11, Pp 66568-66578 (2023)
In this article, we focus on addressing the problem of DOD, DOA, and Doppler Frequency estimation with co-prime MIMO radar. Traditional spatial-temporal smoothing methods suffer from high operational complexity and poor algorithm timeliness when over
Externí odkaz:
https://doaj.org/article/5b4fbe1022144f5ba60b4989dd819aec
Publikováno v:
Electronics Letters, Vol 59, Iss 15, Pp n/a-n/a (2023)
Abstract This letter proposes an efficient tensor model for Direction of Departure (DOD) and Direction of Arrival (DOA) estimation based on bistatic nested Multiple‐Input Multiple‐Output (MIMO) radar. Tensor offers a robust solution in characteri
Externí odkaz:
https://doaj.org/article/7a53c0b4e5114f0db40642cbd1c0fe49
Publikováno v:
Sensors, Vol 23, Iss 11, p 5214 (2023)
Sparse arrays are of deep concern due to their ability to identify more sources than the number of sensors, among which the hole-free difference co-array (DCA) with large degrees of freedom (DOFs) is a topic worth discussing. In this paper, we propos
Externí odkaz:
https://doaj.org/article/0aae75d3e9f344229363aafba17d4230
Publikováno v:
Sensors, Vol 23, Iss 2, p 906 (2023)
To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of
Externí odkaz:
https://doaj.org/article/456d8abf24234279bfe1aae3732309ae
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 185 (2022)
For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network). The
Externí odkaz:
https://doaj.org/article/d8554f40570b471eb59d3b6ccffb8743
Publikováno v:
International Journal of Antennas and Propagation, Vol 2021 (2021)
Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generali
Externí odkaz:
https://doaj.org/article/ccc1e84d597a485e91817bb200173d60
Publikováno v:
Sensors, Vol 22, Iss 4, p 1305 (2022)
For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem
Externí odkaz:
https://doaj.org/article/a264aec3aba64a67804b0e0271afbe66
Publikováno v:
International Journal of Antennas and Propagation, Vol 2021 (2021)
Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::851458196c124a54969c07e0c1edaca1
https://doi.org/10.1155/2021/7233651
https://doi.org/10.1155/2021/7233651
Publikováno v:
BMJ Open, Vol 14, Iss 11 (2024)
Objective To investigate the efficacy of esketamine as an adjuvant to epidural ropivacaine for labour analgesia by determining its effect on the median effective concentration (EC50) in a 20 ml volume of ropivacaine.Design A prospective, double-blind
Externí odkaz:
https://doaj.org/article/be2a6a17093b4cc8b29d27ca6331f452
Publikováno v:
Sensors; Volume 22; Issue 4; Pages: 1305
For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9cc2bb49585262bee607b300798bd091
https://pubmed.ncbi.nlm.nih.gov/35214207
https://pubmed.ncbi.nlm.nih.gov/35214207