基于稀疏分解的轨道移频信号去噪算法研究

Denoising Algorithm for Track Circuit Frequency-shift Signal Based on Sparse Decomposition

随着我国铁路的高速发展,轨道移频信号的检测译码技术受到广泛关注;然而实际采集的轨道移频信号不可避免地会混入大量的背景噪声和干扰,因此译码前需要去噪以提高译码的准确性;提出一种基于稀疏分解的轨道移频信号去噪算法,利用移频信号特点构建过完备原子库,采用粗细二阶段匹配追踪算法实现移频信号的噪声去除;将文章算法应用到主流的ZPW-2000轨道移频信号中,结果表明,该算法具有比小波阈值、经验模式分解算法更好的去噪性能,能够有效地去除低信噪比移频信号的噪声,且去噪后译码信噪比可提高10 dB,另外,采用粗细二阶段原子搜索算法显著降低了匹配追踪的运算量,满足实时性要求。

With the advanced development of Chinese railway, track circuit frequency shift signal detection and decoding technologies have received great attention. However, the actual sampled track circuit frequency-shift signals are inevitably mixed with a great deal of background noise and interference, so it is essential to remove the noise before decoding to improve the accuracy of demodulation. For this reason, a novel denoising method for track circuit frequency-shift signal based on sparse decomposition is proposed. A redundancy dictionary is built up according to the feature of frequency shift signal. Based on the dictionary, noise reduction of frequency-shift signal is conducted via the two-phase matching pursuit algorithm. The experimental results on ZPW-2000 frequency shift signals show that this method can effectively remove noise from low SNR frequency-shift signal to improve the decoding SNR by 10dB, which is far better than the wavelet threshold denoising and the empirical mode decomposition denoising algorithms. Moreover, the two-phase searching technique significantly reduces the computation of matching pursuit, and satisfies the real-time requirement of railway applications.