面向先验信息缺失的无人系统空域抗干扰方法研究

潘子豪; 张邦宁; 甄攀; 朱博文; 王宁; 郭道省

doi:10.11999/JEIT260296

面向先验信息缺失的无人系统空域抗干扰方法研究

doi: 10.11999/JEIT260296 cstr: 32379.14.JEIT260296

陆军工程大学通信工程学院南京 210007

详细信息

作者简介:
潘子豪：男，博士研究生，研究方向为无人机通信，阵列信号处理，自适应波束形成

张邦宁：男，教授，研究方向为卫星通信，通信抗干扰

甄攀：男，博士研究生，研究方向为无线环境地图，深度学习

朱博文：男，硕士研究生，研究方向为无线环境地图，扩散模型

王宁：男，硕士研究生，研究方向为阵列信号处理，自适应波束形成

郭道省：男，教授，研究方向为无人机通信，卫星通信，频谱感知，阵列信号处理

通讯作者:
郭道省　xyzgfg@sina.com

中图分类号: TN973.3
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- 被引次数: 0
出版历程
- 修回日期: 2026-06-29
- 录用日期: 2026-06-29
- 网络出版日期: 2026-07-13

Spatial-Domain Anti-Jamming for Unmanned Systems with Lacking Prior Information

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

摘要

摘要: 无人智能系统在复杂电磁环境下的可靠通信是保障其自主任务能力的关键。尤其对于无人机这类典型的空中通信节点，其通信链路直接暴露于开放电磁空间，面临的干扰威胁尤为突出。然而，在实际应用中，由于对干扰特征、期望信号来向及多径传播等信息认知不足，导致传统空域抗干扰方法的性能严重下降。针对先验信息缺失场景，本文研究了一种面向无人系统空域抗干扰方法。具体而言，首先采用空间平滑算法对接收信号进行解相干处理，随后进行空间谱估计以检测出潜在入射信号的来向。然后，遍历估计的谱峰，采用协方差矩阵重构波束形成算法依次提取该方向的信号，实现混合信号的分离。最后，设计了一种基于频谱相似度和时延的信号类型识别方法，将分离的信号分别归类为干扰、直射和多径信号，并根据信号类型，在保留直射信号、抑制干扰的前提下，对多径信号进行选择性合并或抑制等灵活处理。仿真实验验证了所提算法的可行性与有效性。结果表明，所提算法能够在先验信息缺失的条件下有效抑制干扰并灵活处理多径，改善了通信性能。
- 无人系统 /
- 空域抗干扰 /
- 自适应波束形成 /
- 空间平滑
Abstract: Objective Unmanned systems play an increasingly vital role in critical scenarios such as modern emergency response, public safety, intelligent transportation, and so on. Their autonomous perception, intelligent decision-making, and collaborative control capabilities form the core guarantee for efficient task execution. Ensuring the safe, reliable, and continuous operation of unmanned platforms within complex environments constitutes the fundamental basis of unmanned intelligent technology. However, the communication links of unmanned systems are highly susceptible to exposure within open, non-cooperative electromagnetic environments, facing threats of intentional jamming or unintentional interference. Furthermore, communication processes may lack the information of the desired signal, jamming, as well as the multipath effect. This renders existing spatial-domain anti-jamming solutions ineffective, severely compromising the stability of perception, decision-making, and control feedback loops. Consequently, this paper proposes a spatial-domain anti-jamming framework designed to autonomously identify interference, preserve signals, and reconstruct communication links operating in the absence of prior information. Methods The proposed spatial-domain anti-jamming method first employs a spatial smoothing algorithm for the received signal, followed by Capon spatial estimation to detect the direction of arrival (DOA) of incoming signals, where the spatial smoothing aims to decohere for the detection of the DOA for potential multipath signals. Subsequently, spectrum peaks are extracted based on the estimated Capon spatial spectrum, with each peak corresponding to an incident signal. To separate the mixed signals, all estimated peaks are traversed, and a covariance matrix reconstruction-based beamforming algorithm is employed to extract the signals associated with each peak, thereby achieving signal separation. Then, a signal-type identification method based on spectral similarity and time delay is proposed. The KL divergence is used to assess the similarity between each separated signal spectrum and the reference spectrum, with a threshold set to identify jamming. Subsequently, time delay is employed to distinguish direct-path and multipath signals among the remaining signals. Finally, different processing strategies are performed according to the identified signal type. Specifically, multipath signals may be treated as jamming to be suppressed or alternatively combined with a direct-path signal after time-delay alignment. Results and Discussions By designing two cases, including jamming alone and jamming plus multipath, the performance of the proposed method is evaluated using metrics such as output signal-to-jamming-plus-noise ratio (SJNR), beam response, bit error rate (BER), and error vector magnitude (EVM). Simulation results demonstrate that the proposed method consistently maintains near-optimal SINR, which varies with SNR at a fixed SNR (Fig. 3a) and varies with JSR at a fixed SNR (Fig. 3b) under jamming. The time-domain waveform and frequency spectrum after the proposed method remain clearly discernible and consistent with the original signal (Fig. 4). The BER curve nearly overlaps with that of its after optimal processing (Fig. 5). Under $ {E}_{b}/{N}_{0}=10dB $, the constellation diagram is clearly restored, achieving an EVM of -11.52 dB (Fig. 6). Under coexisting jamming and multipath conditions, the proposed method can flexibly handle multipath. Compared to suppression strategies, multipath utilization improves both the output SINR and BER (Fig. 7a and 7b). Beam patterns reveal that a lower secondary lobe forms in the multipath direction after multipath utilization (Fig. 7c). Conclusions This paper proposes a spatial-domain anti-jamming framework for unmanned systems with lacking prior information. Only using the received mixed data, the proposed framework separates signals from different directions and identifies their types. Suppression or retention strategies are then applied based on these types. We finally achieve flexible handling of multipath signals while preserving the direct-path signal and suppressing jamming. Simulation results evaluated the performance of the proposed method in terms of output power and demodulation accuracy, demonstrating its ability to achieve effective jamming suppression and reliable information transmission even under limited prior knowledge regarding jamming, direct-path signal, and multipath characteristics. Future work will analyze the impact of array perturbations, more smart jamming, and coexisting communication modes on the proposal and extend it to increasingly complex unmanned system environments.
- Unmanned Systems /
- Spatial-Domain Anti-Jamming /
- Adaptive Beamforming /
- Spatial Smoothing

HTML全文

图 1 系统模型

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图 2 方法流程图

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图 3 两个干扰源时不同波束形成的干扰抑制能力对比。(a)10阵元条件下，固定JSR(5dB)，输出SINR随输入SNR变化。(b)固定SNR=0dB，输出SINR随JSR的变化。

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图 4 多个干扰源条件下误码率随$ {E}_{b}/{N}_{0} $变化趋势

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图 5 在干扰环境下$ {E}_{b}/{N}_{0}=10dB $时解调信号星座图的对比：(a)理论最优处理。(b)基于特征结构的方法。(c)所提方法。(d)无抗干扰处理的传统接收。

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图 6 干扰和多径条件下不同方法的性能。(a)不同方法输出SINR随SNR变化。(b)不同方法处理后QPSK误码率性能。(c)SNR=10dB条件下不同方法的波束图。(d)$ {E}_{b}/{N}_{0}=10dB $的星座图。

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图 7 不同模块的性能。(a)谱峰搜索中直射路径和多径信号对应谱峰的漏检率。(b)信号类型识别中干扰、直射和多径信号的误分类率。

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图 8 幅相误差条件下所提方法的性能

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表 1 仿真参数

类型	参数	值
阵列天线	阵型	ULA
	阵元数目	10
	阵元间隔	半波长
直射信号	调制方式	QPSK
	每符号采样点数	4
	符号速率	1000符号/秒
	入射角	4.3°
多径信号	幅度	$ U\left(0.5,1\right) $
	相位	$ U\left(0,2\pi \right) $
	时延	2符号
	入射角	–16.2°
干扰信号	类型	类噪声高斯干扰
干扰信号	入射角	–30.4°和40.2°

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