| 王海峰,行鸿彦,陈 梦,陈子正.基于 SSA-SVM 的海杂波背景下小信号检测方法[J].电子测量与仪器学报,2022,36(4):24-31 |
| 基于 SSA-SVM 的海杂波背景下小信号检测方法 |
| Small signal detection method based on SSA-SVM model in sea clutter |
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| DOI： |
| 中文关键词: 微弱信号检测 支持向量机 麻雀搜索算法 海杂波 |
| 英文关键词:weak signal detection support vector machine sparrow search algorithm sea clutter |
| 基金项目:国家重点研发计划(2021YFE0105500)、国家自然科学基金（62171228）项目资助 |
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| 中文摘要: |
| 针对传统检测方法不能有效地从强混沌背景噪声中检测出小信号,本文研究了强杂波背景下小目标检测原理,提出了
一种基于 SSA-SVM 的混沌小信号检测方法。 利用麻雀搜索算法优化 SVM 惩罚参数 C 与核函数参数 σ 提高预测准确性,从而
降低检测门限,提高检测率。 在 Lorenz 混沌系统中加入目标信号进行仿真,结果表明:提出的方法能有效地从强混沌背景噪声
中检测出小信号,瞬态小信号预测的均方根误差为 0. 000 434 3(信噪比为-137. 707 3 dB),比传统 SVM 算法预测信号的均方根
误差 0. 049(信噪比为-54. 60 dB)降低了两个数量级。 利用 IPIX 雷达实测海杂波数据,对所提方法进行实验验证,进一步说明
了该方法的有效性。 |
| 英文摘要: |
| In view of the traditional detection methods that cannot effectively detect small signals from the strong chaotic background
noise, this paper studies the small target detection principle in the strong clutter background, and proposes a chaotic small signal
detection method based on SSA-SVM. The sparrow search algorithm is used to optimize the penalty parameter C and kernel function
parameter σ of SVM to improve the accuracy of prediction, thus reducing the detection threshold and increasing the detection rate.
Adding target signals to Lorenz chaotic system for simulation, the results show that the proposed method can effectively detect small
signals from strong chaotic background noise, and the root mean square error of prediction of transient small signals is 0. 000 434 3
(signal-to-noise ratio is -137. 707 3 dB), which is two orders of magnitude lower than the root mean square error of prediction signals of
traditional SVM algorithm of 0. 049 (signal-to-noise ratio is -54. 60 dB). The proposed method is verified experimentally by using the
sea clutter data measured by IPIX radar, which further demonstrates the effectiveness of the proposed method. |
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