<p>Electromagnetic compatibility (EMC) is a standard test to ensure that electromagnetic instruments used in scientific experiments, such as those on spacecraft, CubeSats, and sounding rockets, operate without causing interference. Depending on the sensitivity of electromagnetic sensors used for passive measurements of plasma waves, stricter EMC standards may be required, which could increase both development time and costs. The stricter EMC tolerance required for plasma wave measurement is not intended to ensure equipment functionality, but rather to improve the clean quality of the wave data. Currently, such tolerance is achieved through hardware-based solutions, such as additional shielding and filtering. However, if stricter EMC tolerance levels for electromagnetic radiation fields can be relaxed using a signal processing-based denoiser for plasma wave data, it can directly benefit the instrumental hardware by reducing the reliance on extensive hardware measures to satisfy EMC requirements. To address future EMC challenges from the perspective of signal processing, we applied an intelligent denoiser, incorporating waveform clustering and inpainting, to denoise impulse and frequency–sweeping interference signals from raw wave data collected by the SS-520-3 sounding rocket. This sounding rocket experiment was aimed at improving our understanding of wave–particle interactions in the polar cusp. One key feature of the waveform clustering method is that it is based on an unsupervised machine learning technique, which relies solely on objective signal features and does not require subjective training data. The resulting waveform, with unnecessary interference components suppressed, was successfully reconstructed with minimal phase degradation, achieving a correlation value of approximately 0.9 using test data. The denoising techniques are expected to be highly useful and contribute to the rapid development of future scientific experiments using sounding rockets and CubeSats.</p> Graphical Abstract <p></p>