动平台分布孔径雷达相参合成探测方法与试验验证

王元昊; 王宏强; 杨琪

doi:10.12000/JR24141

动平台分布孔径雷达相参合成探测方法与试验验证

doi: 10.12000/JR24141

国防科技大学电子科学学院湖南长沙 410073

基金项目: 国家自然科学基金项目(62201591, 61921001, 62105363, 62035014)；国家重点研发计划资助课题(2022YFB3902400)

详细信息

作者简介:
王元昊，博士研究生，主要研究方向为分布式雷达相参合成、波束形成

王宏强，博士，研究员，主要研究方向为雷达信号处理、太赫兹雷达技术

杨　琪，博士，副教授，主要研究方向为雷达信号处理、雷达成像

通讯作者:
王宏强 oliverwhq@tom.com

责任主编：鲁耀兵 Corresponding Editor: LU Yaobing
中图分类号: TN95
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出版历程
- 收稿日期: 2024-07-08
- 修回日期: 2024-09-04

Coherent Detection Method for Moving Platform based Distributed Aperture Radar and Experimental Verification

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: National Natural Science Foundation of China (62201591, 61921001, 62105363, 62035014), National Key Research and Development Program of China (2022YFB3902400)

More Information

Corresponding author: WANG Hongqiang, oliverwhq@tom.com

摘要

摘要: 动平台分布孔径雷达不仅可以通过多部小孔径雷达相参合成等效获得大孔径雷达的探测性能，也可进一步通过机动性和灵活部署增强探测和抗毁伤能力，是未来雷达重要发展方向之一。但由于多雷达间存在内部钟差和外部传播路径差，各雷达发射信号无法直接相参合成，需进行必要的时间和相位相参参数校正，且分布孔径雷达间距通常远超半波长，合成方向图将存在栅瓣问题，影响目标角度估计。为获得相参参数，该文以闭环式框架为基础，给出动平台分布孔径雷达认知相参框架，并结合运动条件下相参参数的变化规律，提出多脉冲关联相参参数估计方法以提升参数估计精度。同时，针对栅瓣问题，结合平台运动特性提出一种基于阵列构型累积的无模糊角度估计方法。最后，在仿真验证基础上基于所提框架设计了3节点地面动平台分布孔径雷达原理样机并开展了试验验证，试验结果表明在运动场景下，相比单部孔径雷达可以实现最高14.2 dB的信噪比增益，从而提升了目标的测距精度，同时在一定条件下实现了目标角度的无模糊测量，证明了所提方法和框架的有效性。该文工作将对未来分布孔径雷达的工程化实现及发展起到一定的引导作用。
- 分布孔径相参合成 /
- 认知雷达 /
- 运动平台 /
- 无线同步 /
- 栅瓣抑制
Abstract: Abstract . As one of the most promising next-generation radars, Moving platform based Distributed Aperture Radar (MDAR) cannot only coherently combining distributed apertures to obtain the same detection performance of a large aperture, but also enhance the detection and anti-damage capabilities through mobility and flexible deployment. However, time and phase synchronization among radars should be done before coherently combining due to internal clock differences and external propagation path differences. Moreover, grating lobes will generate as the distance between multiple radars usually exceeds half a wavelength, which affects the estimation accuracy of target angle. To obtain Coherent Parameters (CPs), this paper established a cognitive framework for MDAR based on closed-loop structure. And a multi pulse correlation CPs estimation method considering motion conditions is proposed to improve estimation accuracy. In the meanwhile, an unambiguous angle estimation method based on array configuration accumulation is proposed considering platform motion characteristics. Finally, based on the simulation verification and the proposed framework, a prototype of a 3-node ground MDCAR system is designed and experiments are conducted. Compared to a single radar, a maximum value of 14.2 dB signal-to-noise ratio improvement can be achieved, which can further enhance range detection accuracy. Besides, unambiguous angle estimation is also realized under certain conditions. This work is expected to provide support for the research and development of MDCAR.
- Coherently combining distributed apertures /
- Cognitive radar /
- Moving platform /
- Wireless synchronization /
- Grating lobe suppression

HTML全文

图 1 动平台分布孔径雷达认知相参框架

Figure 1. Cognitive coherence framework for MDCAR

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图 2 多脉冲积累示意图

Figure 2. Illustration of multi-pulse accumulation

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图 3 多脉冲关联相参参数估计方法流程图

Figure 3. Flow chart of multi pulse correlated CPs method

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图 4 虚拟阵列重构示意图

Figure 4. Schematic diagram of virtual array reconstruction

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图 5 方向图与阵列构型关系示意

Figure 5. Illustration of the relationship between array geometry and beam pattern

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图 6 动平台分布孔径雷达探测几何

Figure 6. Target detection geometry for MDCAR

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图 7 单元雷达天线方向图

Figure 7. Unit radar antenna pattern

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图 8 平台运动控制精度与相参合成效率的关系

Figure 8. Impact of platform control errors on coherent efficiency

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图 9 相参参数估计性能

Figure 9. Performance of CPs estimation

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图 10 检测统计量随角度和SNR关系

Figure 10. Relationship between test statistics and angle and SNR

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图 11 检测概率和SNR关系

Figure 11. Relationship between test detection probabilities and SNR

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图 12 所提动平台分布孔径雷达系统总体硬件框架

Figure 12. Hardware framework of the proposed MDCAR system

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图 13 原子钟频率随时间变化情况

Figure 13. Relationship between the frequency of the atomic clock and the time

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图 14 时频标校示意图

Figure 14. Schematic diagram of time-frequency calibration

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图 15 系统工作详细时序

Figure 15. Detailed system working schedule

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图 16 目标探测几何

Figure 16. Target detection geometry

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图 17 MDCAR原理样机

Figure 17. Prototype of MDCAR

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图 18 相参参数估计结果

Figure 18. CPs estimation results

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图 19 一维距离像对比

Figure 19. Comparison of range profiles

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图 20 信噪比提升情况

Figure 20. Improvement of SNR

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图 21 目标距离量测结果对比

Figure 21. Comparison of measured target range

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图 22 目标角度量测结果对比

Figure 22. Comparison of measured target angle

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表 1 相参参数估计仿真参数

Table 1. Simulation parameters of CPs estimation

参数	数值
节点数目	2
信号波形	正负线性调频信号
节点初始位置	[0, 0; 0, 50] m
发射中心频率	300 MHz
发射带宽	10 MHz
脉冲重复频率	2000 Hz
仿真持续时间	1 s
节点运动速度	[0, 10] m/s
目标初始位置	[10000, 8000] m
目标运动速度	[–200, 0] m/s
初始内部相位误差	0°

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表 2 角度估计主要仿真参数

Table 2. Simulation parameters of angle estimation

参数	数值
节点数目	3
节点初始位置	[0, 0; 0, 1.5; 0, 3] m
发射中心频率	300 MHz
节点运动速度 (相参情形)	[0, 10; 0, 10; 0, 10] m/s
节点运动速度 (非相参情形)	[0, 10; 0, 20; 0, 30] m/s
脉冲重复间隔	25 ms
目标角度	10°
所用脉冲个数	3

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表 3 雷达参数

Table 3. Radar parameters

参数	数值
节点数目	3
发射中心频率	230 MHz
信号带宽	1 MHz
脉冲宽度	30 us
脉冲重复频率	2000 Hz
认知-校正时延	0.5 s

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