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Relevant bibliographies by topics / Source enumeration

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Journal articles

Academic literature on the topic 'Source enumeration'

Author: Grafiati

Published: 21 December 2024

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Journal articles on the topic "Source enumeration"

1

Lee, Yunseong, Chanhong Park, Taeyoung Kim, et al. "Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation." Applied Sciences 11, no. 4 (2021): 1942. http://dx.doi.org/10.3390/app11041942.

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Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

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2

Kim, Taeyoung, Yunseong Lee, Chanhong Park, et al. "Source Enumeration Method using Eigenvalue Gap Ratio and Performance Comparison in Rayleigh Fading." Journal of the Korea Institute of Military Science and Technology 24, no. 5 (2021): 492–502. http://dx.doi.org/10.9766/kimst.2021.24.5.492.

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In electronic warfare, source enumeration and direction-of-arrival estimation are important. The source enumeration method based on eigenvalues of covariance matrix from received is one of the most used methods. However, there are some drawbacks such as accuracy less than 100 % at high SNR, poor performance at low SNR and reduction of maximum number of estimating sources. We suggested new method based on eigenvalues gaps, which is named AREG(Accumulated Ratio of Eigenvalues Gaps). Meanwhile, FGML(Fast Gridless Maximum Likelihood) which reconstructs the covariance matrix was suggested by Wu et al., and it improves performance of the existing source enumeration methods without modification of algorithms. In this paper, first, we combine AREG with FGML to improve the performance. Second, we compare the performance of source enumeration and direction-of-arrival estimation methods in Rayleigh fading. Third, we suggest new method named REG(Ratio of Eigenvalues Gaps) to reduce performance degradation in Rayleigh Fading environment of AREG.

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3

Cuichun Xu and S. Kay. "Source Enumeration via the EEF Criterion." IEEE Signal Processing Letters 15 (2008): 569–72. http://dx.doi.org/10.1109/lsp.2008.2001112.

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4

Ge, Shengguo, Siti Nurulain Mohd Rum, Hamidah Ibrahim, Erzam Marsilah, and Thinagaran Perumal. "A Source Number Enumeration Method at Low SNR Based on Ensemble Learning." International Journal of Emerging Technology and Advanced Engineering 13, no. 3 (2023): 81–90. http://dx.doi.org/10.46338/ijetae0323_08.

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Source number estimation is one of the important research directions in array signal processing. To solve the difficulty of estimating the number of signal sources under a low signal-to-noise ratio (SNR), a source number enumeration method based on ensemble learning is proposed. This method first preprocesses the signal data. The specific process is to decompose the original signal into several intrinsic mode functions (IMF) by using Complementary Ensemble Empirical Mode Decomposition (CEEMD), and then construct a covariance matrix and perform eigenvalue decomposition to obtain samples. Finally, the source number enumeration model based on ensemble learning is used to predict the number of sources. This model is divided into two layers. First, the primary learner is trained with the dataset, and then the prediction result on the primary learner is used as the input of the secondary learner for training, and then the prediction result is obtained. Computer theoretical signals and real measured signals are used to verify the proposed source number enumeration method, respectively. Experiments show that this method has better performance than other methods at low SNR, and it is more suitable for real environment. Keywords—Source number estimation; Array signal processing; SNR; IMF; CEEMD; Ensemble learning.

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5

Loyer, P., J. M. Moureaux, and M. Antonini. "Lattice codebook enumeration for generalized Gaussian source." IEEE Transactions on Information Theory 49, no. 2 (2003): 521–27. http://dx.doi.org/10.1109/tit.2002.807306.

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6

Cozzens, J. H., and M. J. Sousa. "Source enumeration in a correlated signal environment." IEEE Transactions on Signal Processing 42, no. 2 (1994): 304–17. http://dx.doi.org/10.1109/78.275604.

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7

Akeroyd, Michael A., William M. Whitmer, David McShefferty, and Graham Naylor. "Sound-source enumeration by hearing-impaired adults." Journal of the Acoustical Society of America 139, no. 4 (2016): 2210. http://dx.doi.org/10.1121/1.4950605.

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8

Diaz-Santos, Jose A., and Kathleen E. Wage. "Whitening and source enumeration for large underwater arrays." Journal of the Acoustical Society of America 148, no. 4 (2020): 2477. http://dx.doi.org/10.1121/1.5146862.

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9

Garg, Vaibhav, Ignacio Santamaria, David Ramirez, and Louis L. Scharf. "Subspace Averaging and Order Determination for Source Enumeration." IEEE Transactions on Signal Processing 67, no. 11 (2019): 3028–41. http://dx.doi.org/10.1109/tsp.2019.2912151.

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10

Eguizabal, Alma, Christian Lameiro, David Ramirez, and Peter J. Schreier. "Source Enumeration in the Presence of Colored Noise." IEEE Signal Processing Letters 26, no. 3 (2019): 475–79. http://dx.doi.org/10.1109/lsp.2019.2895548.

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