An improved SVMD-based denoising method for micro thrust measurement signals

Abstract Accurate thrust measurement is essential for spacecraft propulsion and space-based gravitational wave detection. However, thrust measurements are highly sensitive to environmental noise, making effective noise suppression critical for reliable signal acquisition. Thrust signals typically exhibit nonlinear abrupt transitions alongside smooth trends, posing challenges for traditional denoising methods, which often struggle to suppress noise while preserving key signal features. To address this issue, an improved successive variational mode decomposition (SVMD) algorithm optimized by the black-winged kite algorithm (BKA) is proposed. The BKA optimizes the penalty factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:math> based on a minimum envelope entropy fitness function. The enhanced SVMD then adaptively decomposes the signal, with the Hausdorff distance employed to distinguish between effective and noise modes, enabling accurate signal reconstruction. Experimental results on both simulated and actual thrust signals demonstrate that the proposed method significantly improves noise suppression compared to conventional approaches, while effectively preserving critical signal characteristics and enhancing data interpretation accuracy. These advantages make it a promising solution for thrust signal processing in high-precision measurement systems.