约束优化的空间变迹算法的旁瓣抑制应用

doi:10.12305/j.issn.1001-506X.2022.11.02

摘要/Abstract

摘要：

合成孔径雷达(synthetic aperture radar, SAR)回波信号具有很高的动态范围, 会导致强目标的旁瓣覆盖临近较弱目标的主瓣, 造成漏检。传统的加窗方法在消除旁瓣的同时会导致分辨率的下降和目标主瓣能量的降低, 空间变迹(spatially variant apodization, SVA)算法的提出有效解决了这个矛盾。然而, 现有的各种SVA改进算法在有效抑制旁瓣的同时却导致了主瓣能量的降低。因此, 本文提出了一种基于约束优化的改进的SVA算法。通过严格约束滤波器的单调性和有效点的选取, 有效解决了主瓣能量降低的问题, 可以在有效抑制旁瓣的同时保留图像的细节信息, 有利于对图像目标进一步的检测与识别。

关键词: 合成孔径雷达, 旁瓣抑制, 空间变迹算法, 约束优化

Abstract:

Synthetic aperture radar (SAR) echo signals have a high dynamic range, it will cause the sidelobe of the strong target to cover the main lobe of the weak target and cause miss detection. Although the traditional windowing method can eliminate the sidelobe, it can result in the decrease of the resolution and the energy of the main lobe. The spatial variant apodization algorithm (SVA) has been proposed to effectively solve this problem. However, while the existing improved SVA algorithms can effectively suppress the sidelobe, but at the same time, it leads to the reduction of the main lobe energy. Therefore, this paper proposes an improved SVA algorithm based on constrained optimization, which can effectively solve the problem of the main lobe energy reduction by strictly constraining the monotonicity of the filter and the selection of effective points. It can effectively suppress the sidelobes while retaining the details of the image, which is beneficial to the image target further detection and identification.

Key words: synthetic aperture radar (SAR), sidelobe suppression, spatial variant apodization (SVA) algorithm, constrained optimization

中图分类号:

TN951

徐正, 巩光众, 罗运华, 李广德. 约束优化的空间变迹算法的旁瓣抑制应用[J]. 系统工程与电子技术, 2022, 44(11): 3298-3304.

Zheng XU, Guangzhong GONG, Yunhua LUO, Guangde LI. Application of improved spatial variant apodization algorithm through constrained optimization in sidelobe suppression[J]. Systems Engineering and Electronics, 2022, 44(11): 3298-3304.

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目标间距/m	原始回波	MSVA	本文算法	相对于MSVA能量提升比例/%
0.35	1	0.307 0	0.911 0	196.74
0.5	1	0.653 5	0.778 1	19.07
0.8	1	0.811 0	0.900 7	11.06
1.0	1	0.920 7	0.972 7	5.64
1.5	1	0.948 0	0.977 9	3.15
3.0	1	0.976 2	0.991 8	1.60

指标	原始回波	MSVA	本文算法
PSLR/dB	-14.732 6	-21.686 6	-47.707 4
ISLR/dB	-16.812 3	-43.116 1	-49.038 8
主瓣宽度	1	0.793 3	0.886 7

指标	原始图像	MSVA	本文算法
归一能量	1	0.290 5	0.437 7

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