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This study presents the development and application of a Kalman filter algorithm to enhance lightning return stroke waveforms by reducing noise. Utilizing an Auto-Regressive model of order 2 (AR2) to model the lightning signal, the proposed Kalman filter effectively mitigates the effects of noise. Empirical results demonstrate significant SNR improvements. The enhanced signal quality facilitates the detection of weak signals, leading to a notable increase in the number of detected lightning events. This improvement underscores the filter's practical utility in lightning localization systems. The study highlights the robustness and versatility of the Kalman filter, proposing a valuable framework for noise reduction and signal enhancement in lightning waveform analysis.

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Application of Kalman Filter in VLF/LF Lightning Location

  • 会议论文

  • 发表日期：

    2025年

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  • Lilang Xiao,
  • Yu Wang,
  • Hengxin He,
  • Zhong Fu,
  • Yang Cheng,
  • Chen Cheng

摘要

This study presents the development and application of a Kalman filter algorithm to enhance lightning return stroke waveforms by reducing noise. Utilizing an Auto-Regressive model of order 2 (AR2) to model the lightning signal, the proposed Kalman filter effectively mitigates the effects of noise. Empirical results demonstrate significant SNR improvements. The enhanced signal quality facilitates the detection of weak signals, leading to a notable increase in the number of detected lightning events. This improvement underscores the filter's practical utility in lightning localization systems. The study highlights the robustness and versatility of the Kalman filter, proposing a valuable framework for noise reduction and signal enhancement in lightning waveform analysis.