| 基于LASSO的雷达脉压压缩方法 |
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| 引用本文： | 邵玉娥,王暕来,周生华,刘宏伟,张月红. 基于LASSO的雷达脉压压缩方法[J]. 电子科技, 2009, 33(11): 7-10. DOI: 10.16180/j.cnki.issn1007-7820.2020.11.002 |
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| 作者姓名： | 邵玉娥 王暕来 周生华 刘宏伟 张月红 |
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| 作者单位： | 1.西安电子科技大学 电子工程学院,陕西 西安 710071;2.航天一院 战术武器事业部,北京 100076;3.雷达信号处理国家重点实验室,陕西 西安 710071;4.空军西安飞行学院,陕西 西安 710071 |
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| 基金项目： | 国家杰出青年科学基金(61525105);大学研究与教学计划外国学者基金(111项目B18039);中央大学基础研究基金(JB180215);长江学者和大学创新研究团队的计划国家自然科学基金(61601340) |
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| 摘 要： | 雷达抗干扰性能是衡量一部雷达优劣的重要指标,直接决定着雷达作战效能的发挥。常见的抗干扰措施有副瓣对消、脉冲压缩、动目标检测、恒虚警处理等。文中结合LASSO回归算法,提出了一种基于LASSO的脉冲压缩抗干扰措施。该方法利用LASSO回归具有稀疏性的特质,结合雷达回波信号设置匹配字典,使用冗余预测变量构造数据集,并使用交叉验证构造LASSO模型,拟合识别预测变量,实现了目标检测。将该方法与脉冲压缩方法对比,仿真实验证明采用LASSO算法不仅不需要考虑旁瓣的影响,还获得了更好的目标分辨能力,并且在较小信噪比条件下目标检测效果也比较好。
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| 关 键 词： | 目标检测 LFM信号 脉冲压缩 LASSO 匹配字典 线性回归 特征提取 |
| 收稿时间： | 2019-08-21 |
Radar Pulse Compression Method Based on LASSO |
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| SHAO Yu'e,WANG Jianlai,ZHOU Shenghua,LIU Hongwei,ZHANG Yuehong. Radar Pulse Compression Method Based on LASSO[J]. Electronic Science and Technology, 2009, 33(11): 7-10. DOI: 10.16180/j.cnki.issn1007-7820.2020.11.002 |
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| Authors: | SHAO Yu'e WANG Jianlai ZHOU Shenghua LIU Hongwei ZHANG Yuehong |
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| Affiliation: | 1. School of Electronic Engineering,Xidian University,Xi’an 710071,China;2. Department of Tactical Weapons,First Aerospace Institute, Beijing 100076,China;3. State Key Laboratory of Radar Signal Processing, Xi’an 710071,China;4. PLA Air Force Xi’an Flight Academy,Xi’an 710071,China |
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| Abstract: | The anti-jamming performance of radar is an important indicator to measure the pros and cons of a radar, which directly determines the performance of radar combat. Common anti-interference measures include sidelobe cancellation, pulse compression, moving target detection, constant false alarm processing. In this paper, a LASSO-based impulse compression anti-jamming measure is proposed. The method use the characteristics of sparsity of LASSO regression, and combines radar echo signals to set matching dictionary. Then, the data set is constructed by using redundant predictors. The LASSO model is constructed by cross validation, and predictors are identified to achieve the target detection. Compared with the pulse compression method, these simulation results show that the LASSO algorithm can obtain better target resolution without considering the influence of side lobes, and the target detection effect is better under the condition of smaller SNR. |
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| Keywords: | target detection LFM signal pulse compression LASSO matching dictionary linear regression feature extraction |
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