| 一种鲁棒的被动声目标识别方法 |
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| 引用本文： | 庄志洪,丁庆海,路建伟,张清泰. 一种鲁棒的被动声目标识别方法[J]. 系统工程与电子技术, 1999, 21(8) |
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| 作者姓名： | 庄志洪 丁庆海 路建伟 张清泰 |
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| 作者单位： | 南京理工大学电光学院,210094 |
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| 摘 要： | 针对噪声环境下一些声目标识别技术性能严重下降的问题，提出了一种基于一阶补偿的径向基函数网络模型，并给出了该网络参数选择方法。实际应用表明，这种神经网络对噪声具有较强的鲁棒性，与传统的径向基函数网络相比，其识别性能等同于信噪比提高大约１０～１５ｄＢ。
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| 关 键 词： | 目标识别 信噪比 目标分类 鲁棒性 |
A Robust Method in Passive Acoustic Target Recognition |
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| Zhuang Zhihong,Ding Qinghai,Lu Jianwei,Zhang Qingtai. A Robust Method in Passive Acoustic Target Recognition[J]. System Engineering and Electronics, 1999, 21(8) |
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| Authors: | Zhuang Zhihong Ding Qinghai Lu Jianwei Zhang Qingtai |
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| Abstract: | A more robust passive acoustic target recognizer is researched to solve the problem of performance degradation in a noisy environment. The model of RBFN with first|order equalization is presented and the algorithms for estimating the model parameters are given. Experimental results show that this model is more robust to noise, with respect to a conventional RBFN recognizer, this network makes an improvement in recognition performance which is equivalent to about 15dB gain in SNR. |
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| Keywords: | Passive acoustic target recognition Radial basis function Robustness |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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