Results 221 to 230 of about 14,931 (256)

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

IEEE Transactions on Antennas and Propagation, 2013
The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the ...
Carlin, Matteo, Rocca, Paolo, Oliveri, Giacomo, Viani, Federico, Massa, Andrea   +4 more
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Direction of Arrival (DOA) Estimation

2019
The importance of DOA estimation in radar processing for automotive applications cannot be overstated. It forms the third component of the radar cube: range, velocity, and angle. In practice, DOA estimation is often complicated by the fact that there will be multiple and unknown number of source signals impinging on the receiver array at the same time,
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Coherent DiLL for direction of arrival estimation

6th International Conference on Signal Processing, 2002., 2003
Recently, a new direction of arrival (DOA) estimation scheme, direction lock loop (DiLL) was proposed (see Hou, W. and Kwon, H.M., Proc. IEEE Vehicular Technology Conference, 2000; Min, S. et al., Proc. CDMA Int. Conf., 2001). It is configured to operate in noncoherent mode, in other words, it uses an amplitude squaring of the spatial correlator output.
null Dongyoun Seo, null Seunghyun Min, null Kwang Bok Lee, null Yong-Hwan Lee   +3 more
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Wavelet packets-based direction-of-arrival estimation

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
Wavelet packets-based MUSIC (WP-MUSIC) is proposed to improve the performance of classical MUSIC in scenarios of closely spaced DOA and low signal-to-noise ratio (SNR). With WP-MUSIC the fullband signal is decomposed into several subbands by wavelet packets, and then MUSIC is applied to each subband.
null Yanbo Xue, null Jinkuan Wang, null Zhigang Liu   +2 more
openaire   +1 more source

Maximum-likelihood wideband direction-of-arrival estimation

Sixth Multidimensional Signal Processing Workshop, 2003
The specific problem that was addressed is one in which there is limited data in both the temporal and spatial dimensions, so that one cannot assume the use of ordinary Fourier transforms on the time domain outputs of each sensor. Rather, zero-mean Gaussian statistics were assumed, and the likelihood of the observed data was directly maximized with ...
D.R. Fuhrmann, M.I. Miller
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Direction-of-arrival estimation using separated subarrays

Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154), 2002
In this paper we study direction-of-arrival (DOA) estimation with a particular class of sparse linear arrays, characterized by two widely separated subarrays. Since a large array aperture is obtained with a small number of elements, this structure can provide very accurate angle estimates at a reasonable cost, but at the expense of near ambiguities ...
F. Athley, C. Engdahl
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Estimation of Direction of Arrival Algorithms

2013
MUSIC and ESPRIT algorithms are two of the most widely known and used signal source estimation techniques. This study aims at the comparison of the performances of these two techniques. In this study, the signals coming toward uniform linear antenna arrays are estimated using MUSIC and ESPRIT algorithms.
Orul, T., AFACAN, ERKAN
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Coarray Tensor Direction-of-Arrival Estimation

IEEE Transactions on Signal Processing, 2023
Hang Zheng, Chengwei Zhou, Zhiguo Shi, Yujie Gu, Yimin D. Zhang   +4 more
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
exaly  

Deep Binaural Direction of Arrival Estimation

The objective of binaural direction of arrival (DoA) estimation is to find the DoA of a sound source by measuring the sound field with a binaural array. This field increasingly applies deep learning to this task, particularly convolutional neural networks which are trained on relatively raw representations of the binaural audio.
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