基于多尺度线性注意力的BTM干扰抑制算法研究
BTM Interference Suppression Based on Multi-Scale Linear Attention
王栋 1吕旌阳1
作者信息
- 1. 北京邮电大学信息与通信工程学院，北京 100876
- 折叠
摘要
高速铁路BTM应答器报文信号易受牵引瞬态脉冲、钢轨辐射及带内谐波等非平稳混叠干扰的影响，进而导致报文丢失。针对现有方法对应答器报文信号特征利用不足的问题，本文提出一种多阶段混合下采样与多尺度线性注意力网络（MSHD-MLANet），其采用编码器-瓶颈层-解码器架构：编码器通过混合下采样压缩2FSK码元冗余并保留波形细节；瓶颈层采用并行多尺度空洞卷积与线性注意力，以线性复杂度提取长时序依赖；解码器结合亚像素卷积与跳跃连接实现高精度波形重构。实验结果表明，在本文实验条件下，该模型将强干扰环境下BTM报文信号的精确率由87.615%提升至99.717%，信噪比提高18.11dB；消融实验验证了各模块的有效性。对比实验进一步表明，该方法能够在兼顾计算量与推理时间的前提下，有效抑制BTM报文信号中的复杂混叠干扰。
Abstract
High-speed railway BTM balise message signals are susceptible to non-stationary aliasing interference, such as traction transient pulses, rail radiation, and in-band harmonics, which can lead to message loss. To address the insufficient utilization of balise message signal features in existing methods, this paper proposes a Multi-Stage Hybrid Downsampling and Multi-Scale Linear Attention Network (MSHD-MLANet), which adopts an encoder-bottleneck-decoder architecture. The encoder compresses 2FSK symbol redundancy through hybrid downsampling while preserving waveform details. The bottleneck layer employs parallel multi-scale dilated convolutions and linear attention to extract long-term temporal dependencies with linear complexity. The decoder combines sub-pixel convolution with skip connections to achieve high-precision waveform reconstruction. Experimental results show that, under the experimental conditions in this paper, the proposed model improves the precision of BTM message signals under strong interference from 87.615% to 99.717% and increases the signal-to-noise ratio by 18.11 dB. Ablation experiments verify the effectiveness of each module. Comparison experiments further demonstrate that the proposed method can effectively suppress complex aliasing interference in BTM message signals while balancing computational cost and inference time.关键词
信号与信息处理/应答器/深度学习/干扰抑制。Key words
Signal and Information Processing/Balise/Deep Learning/Interference Suppression引用本文复制引用
王栋,吕旌阳.基于多尺度线性注意力的BTM干扰抑制算法研究[EB/OL].(2026-11-13)[2026-11-14].http://www.paper.edu.cn/releasepaper/content/202611-9.学科分类
铁路运输工程
