非合作双基地雷达参考通道多径影响和抑制方法研究

王雪松; 潘嘉蒙; 陈龙; 鲍庆龙; 陈健

doi:10.12000/JR25155

非合作双基地雷达参考通道多径影响和抑制方法研究

DOI: 10.12000/JR25155 CSTR: 32380.14.JR25155

国防科技大学自动目标识别国家重点实验室长沙 410073

基金项目: 国家自然科学基金(62201594, 62201588)

详细信息

作者简介:
王雪松，硕士生，主要研究方向为非合作双基地雷达杂波抑制

潘嘉蒙，博士，讲师，主要研究方向为无源探测、目标跟踪和雷达信号处理

陈　龙，硕士生，主要研究方向为非合作双基地雷达信号同步

鲍庆龙，博士，副教授，主要研究方向为雷达数据采集、无源探测和雷达信号处理

陈　健，博士，讲师，主要研究方向为雷达抗干扰与信号处理

通讯作者:
潘嘉蒙panjiameng@nudt.edu.cn

鲍庆龙 baoqinglong@nudt.edu.cn

责任主编：万显荣 Corresponding Editor: WAN Xianrong
中图分类号: TN95
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出版历程
- 收稿日期: 2025-08-18
- 修回日期: 2025-09-18

Multipath effects and Suppression in Reference Channels of Passive Bistatic Radar

State Key Laboratory of Automatic Target Recognition, National University of Defense Technology, Changsha 410073,China

Funds: The National Natural Science Foundation of China (62201594, 62201588)

More Information

Corresponding author: PAN Jiameng, panjiameng@nudt.edu.cn; BAO Qinglong, baoqinglong@nudt.edu.cn

摘要

摘要: 非合作双基地雷达凭借其抗隐身、抗干扰等特性，在民用与军用领域具有重要应用价值。然而在实际应用中，不可控雷达辐射源及复杂地理环境导致参考信号中不可避免地混入多径干扰与噪声污染，致使参考信号与回波信号互相关处理检测性能显著劣化于理想最优匹配滤波器，并产生固定虚假目标，成为制约非合作双基地雷达实战应用的瓶颈问题。该文针对该问题展开系统性研究：首先分析了参考通道存在多径和噪声时的互相关结果，定量分析了参考通道中多径强度和噪声功率与检测概率的映射关系。其次针对线性调频信号，提出了一种基于去斜解调的参考通道多径抑制算法，该算法利用线性调频信号特性，将不同时延的多径分量转化为显著的频率偏移，相同多径分量相较于现有主流分数阶傅里叶变化产生更大频移效果，能以显著降低滤波器阶数实现相当的抑制效果，得益于更彻底的信号分离度，相较于常规算法该算法在整体上能更有效地提升目标检测概率。在强多径淹没直达波的实际外场试验场景下，实测数据处理结果验证了所提方法在消除虚假目标、修正距离偏移和提升目标检测概率方面的有效性。
- 无源雷达 /
- 互相关 /
- 检测概率损失 /
- 多径抑制 /
- 去斜解调
Abstract: Non-cooperative bistatic radar exhibits significant application value for both civilian and military applications due to its anti-stealth and anti-jamming capabilities. However, its practical implementation faces challenges from unavoidable multipath interference and noise contamination in reference signals, stemming from uncontrollable radar illuminators and complex geographical environments. These effects substantially degrade the performance of cross-correlation processing between reference and echo signals compared with the ideal matched filter, resulting in stationary false targets. Such issues remain a critical bottleneck to operational deployment. This study systematically addresses these challenges by analyzing cross-correlation degradation under multipath and noise in the reference channel, and by establishing a quantitative mapping between multipath intensity, noise power, and detection probability. For Linear Frequency Modulated (LFM) signals, a dechirp-based multipath suppression algorithm is proposed. The algorithm exploits the inherent properties of LFM signals, transforming multipath components with different delays into distinct frequency offsets. Compared with mainstream Fractional Fourier Transform methods, this approach exhibits greater frequency separation among multipath components, enabling effective suppression with significantly reduced filter orders. The algorithm outperforms conventional methods in improving overall detection probability. Measured data processing in practical field-test scenarios (direct-path signals overwhelmed by strong multipath interference) validates the method’s efficacy in eliminating false targets, correcting range offsets, and enhancing detection probability.
- Passive radar /
- Cross-Correlation (CC) /
- Detection probability degradation /
- Multipath suppression /
- Dechirp

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图 1 双基地无源雷达的几何结构图

Figure 1. Geometric structure diagram of bistatic passive radar

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图 2 存在相位差的相参积累结果

Figure 2. The result of coherent integration with phase difference

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图 3 CFAR遮蔽“扩展”现象

Figure 3. CFAR masking extension

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图 4 参考通道信干比对检测概率的影响

Figure 4. The effect of reference channel signal-to- interference ratio on detection probability

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图 5 去斜解调和分数阶傅里叶变换处理流程对比

Figure 5. Comparison of Dechirp and FrFT processing

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图 6 基于去斜解调的参考信号多径抑制算法流程图

Figure 6. Dechirping-based multipath suppression algorithm flowchart for reference signals

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图 7 参考通道处理前的结果

Figure 7. Results before reference channel processing

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图 8 去斜解调的参考信号多径抑制结果和匹配滤波器的检测结果

Figure 8. Reference signal multipath suppression results of the Dechirp and detection results of the Matched Filter

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图 9 不同滤波器阶数的检测概率

Figure 9. Detection probability results for different filter orders

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图 10 性能影响分析

Figure 10. Performance impact analysis

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图 11 实验场景示意图

Figure 11. Schematic diagram of the experimental scenario

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图 12 实测数据不同域的结果

Figure 12. Results from different domains of measured data

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图 13 多径抑制前后的自相关和互相关结果

Figure 13. Autocorrelation and Cross-Correlation results before and after multipath suppression

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图 14 本文算法处理前后多个脉冲目标检测结果

Figure 14. Target detection results of multiple pulses before and after processing by the proposed algorithm

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表 1 目标检测性能仿真参数

Table 1. Target detection performance simulation parameters

参数	数值	参数	数值
脉宽	100 μs	带宽	5 MHz
重复周期	1 ms	采样频率	20 MHz
参考通道直达波功率	40 dBm, 25 dBm	监视通道直达波功率	20 dBm
参考通道多径时延范围	(0.2 μs, 1.5 μs]	监视通道杂波总功率($ \sum\nolimits_{i = 0}^L {\beta _i^2} $)	10 dBm
参考通道多径数量	10	监视通道目标回波功率	–20 dBm
参考通道中信干比	[0 dB, 40 dB]	监视通道目双基地时延	140 μs

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表 2 仿真参数

Table 2. Simulation parameters

参数	数值	参数	数值
脉冲宽度	30 μs	目标回波信噪比	–15 dB, –5 dB
重复周期	100 μs	目标双基地延时	40 μs, 45 μs
调频带宽	5 MHz	多径延时范围	(0 μs, 10 μs]
参考通道直达波信噪比	40 dB	参考通道多径数量	10
载频	0 MHz	参考通道信号信干比	10 dB
CFAR训练单元	60	监视通道直达波信噪比	20 dB
CFAR保护单元	8	采样频率	20 MHz
滤波器阶数	71	CFAR虚警概率	$1 \times {10^{ - 6}}$

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