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Virual Oral Question and Answer
IEEE/ICACT20250181 Question.12
Questioner: fbee5860@gmail.com    2025-02-19 오전 11:03:41 		IEEE/ICACT20250181 Answer.12
Answer by Auhor 3120220183@bit.edu.cn   2025-02-19 오전 11:03:41
The experimental part verifies the effectiveness of the algorithm in simulation and measured data, but does not mention the performance of different noise types (such as pulse noise, multipath interference). Is the improved method only applicable to Gaussian white noise scenarios? How robust is the algorithm in more complex real-world noise environments (such as urban multipath effects)? Current experiment scope: The experiments in this paper mainly focus on Gaussian white noise scenes, and verify the denoising performance of the algorithm under different signal-to-noise ratio (SNR). The results of simulation and measured data show that the improved method has remarkable denoising effect and localization accuracy improvement in Gaussian white noise environment. The applicability of complex noise environment: the paper does not explicitly test complex noise scenes such as pulse noise and multipath interference, but the wavelet threshold denoising method has strong universality and can theoretically be extended to other noise types. For complex noise such as multipath interference, it may be necessary to further adjust the wavelet basis function, decomposition layer number and threshold selection strategy to adapt to the non-stationary characteristics of the signal. Future work could include validating the robustness of the algorithm in more complex noise environments, such as multipath effects in urban environments or pulsed noise in industrial environments.

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