Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM
To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Ada...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024025313 |
| _version_ | 1797267706999734272 |
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| author | Shang Shang Jian Zhu Qiang Liu Yishan Shi Tiezhu Qiao |
| author_facet | Shang Shang Jian Zhu Qiang Liu Yishan Shi Tiezhu Qiao |
| author_sort | Shang Shang |
| collection | DOAJ |
| description | To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm's effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection. |
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| id | doaj.art-e7d35f2304fe41bb9bd88f66113cebec |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:51Z |
| publishDate | 2024-02-01 |
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| spelling | doaj.art-e7d35f2304fe41bb9bd88f66113cebec2024-03-09T09:28:28ZengElsevierHeliyon2405-84402024-02-01104e26500Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELMShang Shang0Jian Zhu1Qiang Liu2Yishan Shi3Tiezhu Qiao4Ocean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, ChinaCorresponding author.; Ocean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, ChinaOcean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, ChinaOcean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, ChinaOcean College, Jiangsu University of Science and Technology, Zhenjiang, 212003, ChinaTo effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm's effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection.http://www.sciencedirect.com/science/article/pii/S2405844024025313CEEMDAN decompositionRCMDEAWT denoisingIZOA optimization algorithmELM |
| spellingShingle | Shang Shang Jian Zhu Qiang Liu Yishan Shi Tiezhu Qiao Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM Heliyon CEEMDAN decomposition RCMDE AWT denoising IZOA optimization algorithm ELM |
| title | Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM |
| title_full | Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM |
| title_fullStr | Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM |
| title_full_unstemmed | Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM |
| title_short | Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM |
| title_sort | low altitude small target detection in sea clutter background based on improved ceemdan izoa elm |
| topic | CEEMDAN decomposition RCMDE AWT denoising IZOA optimization algorithm ELM |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024025313 |
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