基于低推导复杂度的5阶NCS高速前斜视SAR成像算法

邓坤; 田欣然; 殷尘霄; 张陆; 陈展野; 黄岩

doi:10.12000/JR25187

基于低推导复杂度的5阶NCS高速前斜视SAR成像算法

DOI: 10.12000/JR25187 CSTR: 32380.14.JR25187

邓坤^{1, 2},
田欣然¹,
殷尘霄¹,
张陆¹,
陈展野^{1, 3, ,},
黄岩^{1, 2, ,}

1.
东南大学毫米波全国重点实验室南京 210096
2.
紫金山实验室南京 211100
3.
东南大学电磁空间科学与技术研究院南京 210096

基金项目: 国家自然科学基金(62271142, U2341206)；江苏省杰出青年基金(BK20250070)

详细信息

作者简介:
邓　坤，博士生，主要研究方向为雷达信号处理、合成孔径雷达成像与运动补偿技术

田欣然，硕士生，主要研究方向为机载合成孔径雷达成像技术

殷尘霄，硕士生，主要研究方向为毫米波雷达信号处理、合成孔径雷达成像与运动补偿技术

张　陆，硕士生，主要研究方向为毫米波雷达信号处理、合成孔径雷达成像技术

陈展野，博士，副教授，主要研究方向为新型电磁调控系统信息处理、雷达数字仿真与数据增广以及雷达运动目标检测

黄　岩，博士，教授，主要研究方向为电磁智能对抗技术、目标智能感知技术、毫米波雷达、计算机视觉

通讯作者:
陈展野 chenzhanye@seu.edu.cn

黄岩 yellowstone0636@hotmail.com

责任主编：张增辉 Corresponding Editor: ZHANG Zenghui

中图分类号: TN957.52
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出版历程
- 收稿日期: 2025-09-26
- 修回日期: 2025-11-20

Fifth-order NCS Algorithm for High-speed Squint-forward-Looking SAR Imaging with Low Derivation Complexity

DENG Kun^{1, 2},
TIAN Xinran¹,
YIN Chenxiao¹,
ZHANG Lu¹,
CHEN Zhanye^{1, 3
, ,},
HUANG Yan^{1, 2
, ,}

1.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
2.
Purple Mountain Laboratory, Nanjing 211100, China
3.
Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62271142, U2341206), The Outstanding Youth Foundation of Jiangsu Province (BK20250070)

More Information

Corresponding author: CHEN Zhanye, chenzhanye@seu.edu.cn; HUANG Yan, yellowstone0636@hotmail.com

摘要

摘要: 高速前斜视(斜视角>70°)合成孔径雷达(SAR)成像受制于严重的距离多普勒耦合和多普勒空变。传统非线性调频变标(NCS)算法能够在大斜视(斜视角>30°)模式下有效消除多普勒空变，但其推导过程存在近似处理且推导复杂度随阶数急剧增长，难以推广至高阶形式，限制其在高速前斜视SAR系统中的应用。针对这一难题，该文证明基于驻定相位法(POSP)和级数反演法( MSR)进行傅里叶变换( FT)/傅里叶逆变换( IFT)实现方位数据域变换呈现规律性特征，据此提出一种基于低推导复杂度的5阶NCS算法，并针对NCS算法设计几何校正方法。确定斜距模型和NCS阶数，该方法仅需一次FT/IFT的推导即可获得NCS处理后的信号解析式，有效简化多普勒参数线性方程组的构建及NCS参数的求解过程，显著降低算法推导复杂度。基于前斜视成像几何模型，该文提出相应的瞬时投影几何模型，推导适用于NCS算法的几何校正方法。相比传统NCS算法，所提算法在保证计算效率的前提下具备更优的SAR成像性能，仿真与实测数据处理均验证了其在高速前斜视场景下的有效性和优越性。
- 高速前斜视SAR /
- 非线性调频变标 /
- 驻定相位法 /
- 级数反演法 /
- 低推导复杂度 /
- 几何校正 /
- 瞬时投影几何模型
Abstract: High-speed squint-forward-looking Synthetic Aperture Radar (SAR) imaging (squint angle: >70°) is challenged by severe range-Doppler coupling and Doppler space variance. Traditional Nonlinear Chirp Scaling (NCS) algorithms can effectively mitigate Doppler space variance under high-squint conditions (squint angle: >30°), but they rely on approximate treatments and exhibit rapidly increasing derivation complexity at high scaling orders. This makes high-order generalization difficult and limits their application in high-speed squint-forward-looking SAR systems. To address this issue, this study demonstrates that Fourier Transform (FT) and Inverse FT (IFT) implementations, based on the principle of stationary phase and the method of series reversion for azimuth data domain transformation, exhibit regular structural patterns. Building on this insight, a fifth-order NCS algorithm with low derivation complexity is proposed, along with a dedicated geometric correction method. For a given predefined slant range model and NCS order, the proposed algorithm requires only a single FT/IFT derivation to obtain the analytical expression of the signal after NCS processing, thereby simplifying both the construction of the Doppler parameter linear equation system and the solution of NCS parameters. This significantly reduces the complexity of the algorithm derivation. Furthermore, an instantaneous projection geometric model is established based on the high-speed squint-forward-looking SAR imaging geometry, enabling the development of a tailored geometric correction method. Compared with traditional NCS algorithms, the proposed fifth-order NCS algorithm achieves superior imaging performance while maintaining computational efficiency. Simulated and real data processing validate its effectiveness and advantages in high-speed squint-forward-looking scenarios.

HTML全文

图 1 非线性频调变标处理流程

Figure 1. Nonlinear chirp scaling (NCS) processing flowcharts

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图 2 高速前斜视SAR成像几何模型

Figure 2. Geometric model of high-speed squint-forward SAR imaging

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图 3 二维波数谱的泰勒级数近似误差

Figure 3. Taylor series approximation error for 2D wavenumber spectrum

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图 4 方位时域相位的泰勒级数近似误差

Figure 4. Taylor series approximation error for azimuth time-domain phases

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图 5 NCS参数变换关系图

Figure 5. NCS parameter transformation relationship diagram

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图 6 高速前斜视SAR瞬时投影几何

Figure 6. Instantaneous high-speed squint-forward SAR imaging geometry

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图 7 所提算法流程图

Figure 7. Flowchart of the proposed algorithm

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图 8 所提方法仿真结果

Figure 8. Simulation results of the proposed algorithm

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图 9 TNCS算法^[33]的仿真数据处理结果

Figure 9. Simulation data processing results of the TNCS algorithm^[33]

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图 12 所提算法的仿真数据处理结果

Figure 12. Simulation data processing results of the proposed algorithm

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图 10 HPCA算法^[34]的仿真数据处理结果

Figure 10. Simulation data processing results of HPCA algorithm^[34]

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图 11 MFNCS算法^[6]的仿真数据处理结果

Figure 11. Simulation data processing results of MFNCS algorithm^[6]

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图 13 实测数据成像结果

Figure 13. Imaging results of the measured data

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图 14 图13中区域A的放大结果

Figure 14. Magnified results of the Region A in Fig. 13

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图 15 图14中子区域的放大结果

Figure 15. Magnified results of the Sub-Regions in Fig. 14

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图 16 图15中目标a的方位包络图

Figure 16. Azimuth profiles of the Target a in Fig. 15

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图 17 实测SAR图像几何校正结果

Figure 17. Geometric correction result of the measured SAR image

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表 1 SAR系统的仿真参数

Table 1. Simulation parameters of the SAR system

参数	数值	参数	数值
波长	0.0175 m	波束宽度	6°
PRF	10 kHz	参考斜视角	80°
带宽	150 MHz	参考速度	1500 m/s
采样率	200 MHz	参考斜距	45 km
方位时间	1.0 s	飞行高度	4 km

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表 2 点目标方位聚焦性能评估指标

Table 2. Azimuth focusing performance evaluation metrics of point targets

方法	边缘点T₁		边缘点T₂		中心点T₃		边缘点T₄		边缘点T₅
方法	PSLR	ISLR	PSLR	ISLR	PSLR	ISLR	PSLR	ISLR	PSLR	ISLR
TNCS	–3.23	–2.88	–4.23	–4.90	–13.12	–11.07	–0.10	0.55	–1.0	2.12
HPCA	–10.36	–8.62	–12.83	–11.20	–13.21	–11.45	–12.24	–10.78	–2.19	–3.51
MFNCS	–9.36	–7.43	–13.20	–11.05	–13.24	–10.99	–12.82	–10.81	–12.64	–11.23
所提算法	–13.22	–11.09	–13.21	–11.40	–13.25	–11.46	–13.20	–11.23	–13.07	–11.49
注：加粗数值表示最优。

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表 3 实测数据成像评估指标

Table 3. Imaging evaluation metrics of the measured data

方法	香农熵^[54]	对比度^[55]	清晰度^[56]	成像时间
TNCS	1.8098	2.1408	5.8583e+22	50.47s
HPCA	1.7664	2.2064	6.0789e+22	51.43s
MFNCS	1.8047	2.4689	6.1331e+22	54.12s
所提算法	1.7592	2.6456	6.2129e+22	50.64s
注：加粗数值表示最优。

下载: 导出CSV

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