| 稀疏驱动自适应线谱增强的水下目标谱熵检测 |
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| 引用本文： | 金盛龙,迟骋,李宇,黄海宁.稀疏驱动自适应线谱增强的水下目标谱熵检测[J].声学学报,2021,46(6):1059-1069. |
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| 作者姓名： | 金盛龙 迟骋 李宇 黄海宁 |
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| 作者单位： | 1. 中国科学院声学研究所 北京 100190; |
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| 基金项目： | 国防基础科研计划重大项目(JCKY2016206A003)国家自然科学基金项目(11904386,62001469)资助 |
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| 摘 要： | 针对无人平台在水下复杂环境中的线谱弱目标自主检测问题,提出了一种采用稀疏驱动自适应线谱增强(ALE)为前处理的监督学习目标检测方法。该方法在ALE代价函数中引入稀疏性lp范数,并将稀疏正则化推广到01/2稀疏驱动ALE比常规ALE的处理增益高11.5 dB。利用水下无人平台海上拉距试验的数据对算法性能进行验证,在宽带强干扰影响下,该方法可有效检测远距离声源,虚警率为3.5%时,检测率达95.8%,有效提高了对线谱弱目标的检测概率,具有较强的环境适应性。
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| 关 键 词： | 辐射噪声 线谱检测 水下弱目标 谱熵 稀疏性 |
| 收稿时间： | 2020-03-21 |
A supervised learning detection method with pre-processing of sparsity-based adaptive line enhancer |
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| Institution: | 1. Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190;2. Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190;3. University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | For the weak line-spectrum target detection of unmanned underwater vehicles in the complex environment,a supervised learning detection method with pre-processing of sparsity-based Adaptive Line Enhancer(ALE) is proposed.This method incorporates a lp-norm sparse penalty into the cost function of ALE,and it also promotes the sparse regularization model to the 01/2-norm SALE is 11.5 dB higher than that of conventional ALE.The effectiveness of the method is verified by using the Unmanned Underwater Vehicle(UUV) experimental data.Under the influence of wideband strong interferences,the false alarm rate is 3.5% and the detection rate is 95.8%,which improved the detection probability of weak line-spectrum targets. |
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