OTDR系统中基于改进FOX算法的小波去噪方法
Wavelet Denoising Method based on Improved FOX Algorithm in OTDR System
- 光通信研究 2026年第1期
- 作者机构：
1.武汉邮电科学研究院 研究生部，武汉 430074
2.烽火通信科技股份有限公司 网络产出线，武汉 430205
- 作者简介：
李希贤（2000-），男，湖北襄阳人。硕士，主要研究方向为光纤传感及其应用。
李昌元，高级工程师。E-mail：licy@fiberhome.com
- 基金信息：
DOI：10.13756/j.gtxyj.2026.240141
中图分类号： TN913.33收稿：2024-07-08，
修回：2024-08-05，
纸质出版：2026-02-10
- 稿件说明：
移动端阅览
李希贤，李昌元，王攀. OTDR系统中基于改进FOX算法的小波去噪方法[J]. 光通信研究，2026(1): 240141.
Li X X, Li C Y, Wang P. Wavelet Denoising Method based on Improved FOX Algorithm in OTDR System[J]. Study on Optical Communications, 2026(1): 240141.
李希贤，李昌元，王攀. OTDR系统中基于改进FOX算法的小波去噪方法[J]. 光通信研究，2026(1): 240141. DOI： 10.13756/j.gtxyj.2026.240141.
Li X X, Li C Y, Wang P. Wavelet Denoising Method based on Improved FOX Algorithm in OTDR System[J]. Study on Optical Communications, 2026(1): 240141. DOI： 10.13756/j.gtxyj.2026.240141.
摘要
目的
2
在传统光时域反射仪（OTDR）的研制中，信号去噪的处理依赖于累加平均的方法，虽有效但随着需求的提升对时间资源的消耗呈指数增长。针对新型OTDR设备对信号去噪效果和时间效率的更高要求，文章提出了一种基于改进红狐（FOX）优化算法的小波去噪方法。
方法
2
文章所提方法将改进的FOX优化算法与小波阈值去噪相结合，使小波阈值去噪能够摆脱大量参数带来的不确定性，面对不同特征的信号方便地进行参数设置进而实现部署。文章设计了改进的自适应FOX优化算法，在原算法的随机搜索阶段加入自适应因子，使算法在具备更优随机搜索能力的同时保证了收敛性能。改进算法经过测试函数集和显著性检验，验证了其相比于原算法具备更好的寻优性能。进一步地，将改进算法与小波阈值去噪结合的方法运用到OTDR仿真及实测信号实验中。
结果
2
仿真实验结果表明，相比于传统遗传算法和原始FOX算法，文章所提改进FOX算法在信噪比（SNR）和均方根误差（RMSE）两项评价指标上分别提升了16.10%、4.25%和降低了16.30%、4.97%，去噪后的信号相较于原始信号，SNR提高了3.187 dB，RMSE降低了0.272 6。实测数据实验中，去噪后的信号相较于原始信号，SNR提升了2.376 dB，未陷入2.368 dB的局部最优解，平均耗时19.56 s，结果均优于两种对比算法，并分别得到了参数设置方案。
结论
2
文章所提基于改进FOX优化算法的小波去噪方法，优化了参数设置过程，显著提升了去噪效果，为OTDR设备及其他应用场景的信号处理提供了一种新的解决方案。
Abstract
Objective
2
In traditional Optical Time Domain Reflectometer (OTDR)
the processing of signal denoising relies on the cumulative average method. The method is effective but the consumption of time resources increases exponentially with the increase of demand. Aiming at the higher requirement of signal denoising effect and time efficiency of new OTDR equipment
this paper proposes a wavelet denoising method based on improved FOX optimization algorithm.
Methods
2
The method proposed in this paper combines the improved FOX optimization algorithm with wavelet threshold denoising. Therefore
the wavelet threshold denoising can remove the uncertainty caused by a large number of parameters. Moreover
its parameters can be flexibly configured for signals with distinct characteristics
thereby facilitating practical deployment. In this paper
an improved adaptive FOX optimization algorithm is designed
and an adaptive factor is added to the random search stage of the original algorithm
resulting in better random search ability and ensuring convergence performance. The improved algorithm is tested by the test function set and the significance test
which verifies that the improved algorithm has better optimization performance than the original algorithm. Furthermore
the improved algorithm combined with wavelet threshold denoising method is applied to simulations and actual measurement experiments of OTDR signals.
Results
2
The simulation results show that compared with the traditional genetic algorithm and the original FOX algorithm
the proposed FOX algorithm increased the Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) by 16.10% and 4.25%
and decreased by 16.30% and 4.97%
respectively. Compared with the original signal
the SNR of the denoised signal has increased by 3.187 dB and the RMSE has decreased by 0.272 6. In the actual data experiment
compared with the original signal
the SNR improved by 2.376 dB and don't fall into the local optimal solution of 2.368 dB
and the average processing time is 19.56 s. The results are better than the two comparison algorithms
and the parameter setting schemes are obtained.
Conclusion
2
The wavelet denoising method based on the improved FOX optimization algorithm proposed in this paper optimizes the parameter setting process
significantly improves the denoising performance
and provides a new solution for the signal processing of OTDR equipment and other application scenarios.
关键词
Keywords
references
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