Results 101 to 110 of about 13,266 (201)

DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays

open access: yesSensors, 2017
A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the ...
Jianfeng Li, Feng Wang, Defu Jiang
doaj   +1 more source

Energy Efficient Low-Complexity RIS-Aided 3-D DoA Estimation and Target Tracking Algorithm via Matrix Completion

open access: yesIEEE Access
In this paper, we propose an algorithm for fast direction-of-arrival (DoA) tracking in reconfigurable intelligent surface aided systems. We reduce the total power consumption by reducing the number of radio-frequency chains in the access point, which ...
Aral Ertug Zorkun   +3 more
doaj   +1 more source

An Efficient 2-D DOA Estimation for a Cylindrical Conformal Array with Unknown Mutual Coupling

open access: yesInternational Journal of Antennas and Propagation, 2018
The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms.
Chao Liu, Shunian Yin
doaj   +1 more source

Selective Range Iterative Adaptive Approach for High-Resolution DOA Estimation

open access: yesIEEE Access, 2019
In this paper, the problem of direction-of-arrival (DOA) estimation for a uniform linear array with single-snapshot observations is addressed. Two non-parametric DOA estimators are developed, which can be applied in any azimuth range with one snapshot ...
Yuan Chen   +2 more
doaj   +1 more source

Direction of Arrival (DOA) Estimation Using Kronecker Subspace [PDF]

open access: yesSignal and Data Processing, 2018
Sina Majidian, Farzan Haddadi
openaire   +1 more source

On Improvements of Cyclic MUSIC

open access: yesEURASIP Journal on Advances in Signal Processing, 2005
Many man-made signals encountered in communications exhibit cyclostationarity. By exploiting cyclostationarity, cyclic MUSIC has been shown to be able to separate signals with different cycle frequencies, thus, to be able to perform signal selective ...
Yan Huiqin, Fan H Howard
doaj   +1 more source

DOA estimation based on sparse Bayesian learning with moving synthetic virtual array

open access: yesElectronics Letters
In scenarios with constrained physical aperture sizes, aiming to enhance the resolution and accuracy of Direction of Arrival (DOA) estimation, this paper proposes a novel approach that integrates a moving synthetic virtual array with Sparse Bayesian ...
Chao Zhu, Zhenmiao Deng
doaj   +1 more source

Estimation of Direction of Arrival (DoA) of DVB T signals in mobile receiving configuration

open access: yes, 2008
To improve the quality of the mobile reception of DVB-T (Digital Video Broadcasting on Terrestrial networks) signal, the knowledge of the propagation channel characteristics is necessary. In this aim, this paper presents sounding methods and results for the estimation of Direction of Arrival (DoA) of DVB T signals in mobile receiving configuration. The
Nivole, Franck   +4 more
openaire   +3 more sources

DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming

open access: yesRemote Sensing
The Direction of Arrival (DOA) parameter is a key parameter in directional channel modeling for GNSS systems and multipath suppression. However, achieving high-precision, low-complexity DOA estimation of multiple signal sources without requiring a known ...
Jian Wu   +4 more
doaj   +1 more source

Joint DOA and TOA estimation method based on Pareto multi-task learning

open access: yes上海师范大学学报. 自然科学版
A joint estimation method for direction of arrival (DOA) and time of arrival (TOA) was proposed based on Pareto multi-task learning, which transformed the traditional multi-task learning problem into a multi-objective optimization problem for solution. A
CHEN Erqi, WEI Shuang
doaj   +1 more source

Home - About - Disclaimer - Privacy