Online identification of satellite microvibration sources can provide a basis for online suppression of satellite microvibration, which has significant application value for improving the positioning accuracy and resolution of satellite. The complex time-varying environments in orbit, high real-time requirements, and numerous parameters to be estimated pose great challenges to the adaptability, efficiency, and accuracy of online source identification algorithms. Aiming at the challenges for online satellite source identification, a novel online sliding blind deconvolution (OSBD) algorithm is proposed. First, blind deconvolution is converted online to the instantaneous blind source separation (BSS) in the selected single frequency bin by sliding discrete Fourier transform (SDFT), which greatly reduces the computation and improves the efficiency of online separation. Second, my previous work, namely adaptive step-size equivariant adaptive separation via independence (EASI) algorithm based on nonlinear correlation (NC-EASI algorithm), is extended to the complex domain to achieve the online separation. Finally, the separated signals in time domain are obtained by performing the inverse SDFT. The effectiveness of the proposed algorithm under time-varying environment is validated by several numerical simulations and the excitation experiments of aluminum honeycomb panel cabin structure. Compared with the comparison algorithms, the separation accuracy of simulation and experiment in time-varying environments has been improved by an average of 4.295 and 6.64 dB, respectively, and the convergence time has been shortened by an average of 44.42% and 65.11%. The proposed algorithm can provide an effective means for the online source separation of complex mechanical systems.

中文翻译：

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时变环境下卫星微振动源分离的一种新型在线滑动盲解卷积算法

卫星微振动源在线识别可以为卫星微振动在线抑制提供依据，对于提高卫星定位精度和分辨率具有重要的应用价值。在轨时变环境复杂、实时性要求高、需要估计的参数众多，对在线源识别算法的适应性、效率和准确性提出了巨大的挑战。针对在线卫星源识别的挑战，提出一种新颖的在线滑动盲解卷积（OSBD）算法。首先，通过滑动离散傅里叶变换（SDFT）将盲解卷积在线转换为所选单频点内的瞬时盲源分离（BSS），大大减少了计算量，提高了在线分离的效率。其次，将我之前的工作，即基于非线性相关的自适应步长等变自适应独立独立分离（EASI）算法（NC-EASI算法）扩展到复杂领域，实现在线分离。最后，通过逆SDFT得到分离后的时域信号。通过多次数值模拟和铝蜂窝板座舱结构的激励实验验证了该算法在时变环境下的有效性。与对比算法相比，时变环境下仿真和实验的分离精度平均分别提高了4.295和6.64 dB，收敛时间平均缩短了44.42%和65.11%。该算法可为复杂机械系统的在线源分离提供有效手段。