High-order cumulant-based weak signal detection method for Drones based on channel state variations.

Xiao Ninggui; Hu Yuqing; Zhang Hua; Xin Pujie; Pan Peng

doi:10.11959/j.issn.1000-0801.DXKX250751

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High-order cumulant-based weak signal detection method for Drones based on channel state variations.

更新时间：2026-05-07

- High-order cumulant-based weak signal detection method for Drones based on channel state variations.
- Telecommunications Science (2026)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.航天时代飞鸿技术有限公司，北京 102101
- 作者简介：
- 基金信息：
  
  The Project Supported by Zhejiang Provincial Natural Science Foundation of China(LQN26F030022)
- DOI：10.11959/j.issn.1000-0801.DXKX250751
  CLC： TP393
- Received：26 December 2025，
  
  Revised：2026-01-26，
  
  Accepted：09 April 2026，
- 稿件说明：
移动端阅览
Xiao Ninggui, Hu Yuqing, Zhang Hua, et al. High-order cumulant-based weak signal detection method for Drones based on channel state variations.[J/OL]. Telecommunications Science, 2026.
DOI：

Xiao Ninggui, Hu Yuqing, Zhang Hua, et al. High-order cumulant-based weak signal detection method for Drones based on channel state variations.[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX250751.

摘要

针对复杂城市环境中低空慢速小型无人机探测难题，本研究提出一种基于信道冲激响应高阶统计特征的无源探测方法。该方法通过构造接收信号的Hankel矩阵，基于奇异值分解，分析常态环境下以及无人机入侵下的接收信号分布规律，通过构造残差矩阵实现强静态背景与弱动态目标信号的分离。在此基础上引入高阶累积量计算，弱化噪声对信号的影响，通过方差量化无人机运动引起的统计非平稳性。仿真结果表明，在低速运动状态下，含无人机场景的高阶累积量方差相较于常态环境时，会产生显著的系统性抬升和波动。研究证实，融合奇异值分解与高阶累积量方差监测的联合检测框架，能够在低信噪比条件下有效提取无人机对信道的高阶统计扰动，为无人机的探测提供了新的技术途径。

Abstract

Aiming at the challenge of detecting low-altitude

slow-moving

and small unmanned aerial vehicles in complex urban environments

this study proposes a passive detection method based on higher-order statistical features of channel impulse responses. The method constructs a Hankel matrix from the received signals and utilizes singular value decomposition to analyze the distribution patterns of received signals under normal conditions versus those during UAV intrusion. By constructing a residual matrix

it achieves the separation of strong static background signals from weak dynamic target signals. On this basis

higher-order cumulant computation is introduced to mitigate the influence of noise on the signal

and the statistical non-stationarity induced by UAV motion is quantified through variance. Simulation results demonstrate that under uniform motion conditions

the higher-order cumulant variance in UAV-present scenarios exhibits a significant systematic elevation and fluctuation compared to that in normal environments. The study confirms that the joint detection framework integrating singular value decomposition and higher-order cumulant variance monitoring can effectively extract the higher-order statistical disturbances of the channel caused by UAVs under low signal-to-noise ratio conditions

providing a new technical pathway for UAV detection.

关键词

Keywords

references

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