A source number enumeration method at low SNR bsed on ensemble learning
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 pr...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
IJETAE
2023
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/106717/ |
| _version_ | 1848864810731569152 |
|---|---|
| author | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran |
| author_facet | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran |
| author_sort | Ge, Shengguo |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:54:44Z |
| format | Article |
| id | upm-106717 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:54:44Z |
| publishDate | 2023 |
| publisher | IJETAE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1067172024-08-06T02:44:56Z http://psasir.upm.edu.my/id/eprint/106717/ A source number enumeration method at low SNR bsed on ensemble learning Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran 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. IJETAE 2023 Article PeerReviewed Ge, Shengguo and Mohd Rum, Siti Nurulain and Ibrahim, Hamidah and Marsilah, Erzam and Perumal, Thinagaran (2023) A source number enumeration method at low SNR bsed on ensemble learning. International Journal of Emerging Technology and Advanced Engineering, 13 (3). pp. 81-90. ISSN 2250-2459 https://ijetae.com/Volume13Issue3.html 10.46338/ijetae0323_08 |
| spellingShingle | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran A source number enumeration method at low SNR bsed on ensemble learning |
| title | A source number enumeration method at low SNR bsed on ensemble learning |
| title_full | A source number enumeration method at low SNR bsed on ensemble learning |
| title_fullStr | A source number enumeration method at low SNR bsed on ensemble learning |
| title_full_unstemmed | A source number enumeration method at low SNR bsed on ensemble learning |
| title_short | A source number enumeration method at low SNR bsed on ensemble learning |
| title_sort | source number enumeration method at low snr bsed on ensemble learning |
| url | http://psasir.upm.edu.my/id/eprint/106717/ http://psasir.upm.edu.my/id/eprint/106717/ http://psasir.upm.edu.my/id/eprint/106717/ |